Spectrometer device

Abstract

A spectrometer device (110) is disclosed. The spectrometer device (110) is configured to determine at least one item of spectral or spectroscopic information of at least one object (112). The spectrometer device (110) is configured to determine intensities of component wavelength signals of at least one light beam propagating from the object (112) to the spectrometer device (110). The spectrometer device (110) comprises at least one distance detector (134). The distance detector (134) is configured to determine at least one item of distance information about a distance between the at least one object (112) and the spectrometer device (110). The spectrometer device (110) comprises at least one pixelated imaging detector (121) configured to determine at least one image of the object (112). The spectrometer device (110) comprises at least one evaluation device (120) configured to determine at least one item of material information by evaluating the at least one image of the object (112) determined by the pixelated imaging detector (121). The evaluation device (120) is configured to perform at least one spectral analysis on the determined intensities of the component wavelength signals taking into account the determined distance information and the material information.

Description

Technical Field

[0001] The present invention relates to a spectrometer device, a method for determining at least one difference in at least one light characteristic of at least one light beam from at least one object, and various uses of the spectrometer device. Generally, such a device and method can be used in various applications, such as for investigative or monitoring purposes, in particular, infrared detection applications; spectroscopy applications; exhaust gas monitoring applications; combustion process monitoring applications; pollution monitoring applications; industrial process monitoring applications; chemical process monitoring applications; food processing process monitoring applications; water quality monitoring applications; air quality monitoring applications; quality control applications; motion control applications; exhaust gas control applications; gas sensing applications; gas analysis applications; chemical sensing applications; agricultural applications, such as for properties of soil, silage, fertilizer, crops or agricultural products; monitoring plant health; plastic identification and / or recycling applications, etc. However, other types of applications are also possible. Background Art

[0002] Various spectrometer devices and systems are known. Spectrometers typically emit light toward a sample or object and measure the reflected, transmitted, scattered, or received light. Spectral analysis is based on the difference between the emitted and received light. Spectrometers determine wavelength-dependent intensity differences before and after interaction with the sample or object. Spectrometers can further determine properties such as wavelength-dependent polarization differences.

[0003] To analyze differences in light properties before and after interaction with a sample or object, it is important to measure these light properties with minimal variation. Therefore, in known spectrometers, the interaction path with the sample or object in the spectrometer remains fixed and closed. However, there is a need for mobile spectrometers in which the interaction path can be varied, for example by moving the spectrometer.

[0004] Spectrometer devices can operate in either reflection or transmission mode, wherein in reflection mode, light is reflected from the sample, and in transmission mode, light is transmitted through the sample. Spectrometer devices operating in reflection mode may be advantageous in opaque or nearly opaque conditions. Furthermore, for transmission spectroscopy, the sample is often filled in a cuvette or vial. In reflection spectroscopy, it is possible to bring the spectrometer into close contact with the sample or even measure the sample from a distance. Therefore, reflection spectroscopy can be particularly convenient in mobile spectroscopy.

[0005] However, in reflectance spectroscopy, knowing the distance to the sample is crucial because the sample's light attenuation needs to be measured. In the case of translucent or partially opaque samples, this distance becomes unclear or difficult to measure. Furthermore, even when in direct contact with the sample, knowing the sample's translucency parameter is crucial to determine the attenuation according to the Beer-Lambert law.

[0006] In addition, spectroscopy is used in consumer applications that involve analyzing different samples for specific analytes. For example, a consumer application might include determining the lactose or fat content of a milk sample. Due to the variety of potential samples and analytes, these consumer applications are often time-consuming, difficult, and unreliable.

[0007] US 2008 / 191137 A1 describes a handheld enhanced photoemission spectroscopy ("EPS") detection system for identifying specific substances (e.g., controlled substances, illicit drugs and explosives, and other substances that would benefit from trace detection) and mixtures thereof in order to provide information to officials for identification purposes and to assist in determinations related to the legality, dangerousness and / or disposal decisions of such substances.

[0008] US 2009 / 219525 A1 describes a method comprising scanning a plurality of samples with a laser by moving the laser according to coordinates for movement of the laser, and measuring a distance of each of the plurality of samples; associating position information with each sample of the plurality of samples based on its distance from the laser and its coordinates for movement of the laser; recording Raman spectra for the plurality of samples; associating a Raman spectrum with each sample of the plurality of samples; and indicating the Raman spectrum and the position information for at least one sample.

[0009] Problems to be solved by the present invention

[0010] Therefore, the object of the present invention is to provide an apparatus and method that face the above technical challenges of known apparatuses and methods. In particular, the object of the present invention is to provide a movable spectroscopy apparatus and method for mobile spectroscopy of contactless spectroscopy that can reliably determine spectral information. Summary of the Invention

[0011] This problem is solved by the invention having the features of the independent patent claims. Advantageous developments of the invention which can be realized individually or in combination are presented in the dependent claims and / or in the following description and detailed examples.

[0012] As used hereinafter, the terms "having," "including," or "comprising," or any grammatical variants thereof, are used in a non-exclusive manner. Thus, these terms may refer to situations in which no features other than the features introduced by these terms are present in the entity described herein, or to situations in which one or more other features are present. As an example, the expressions "A has B," "A includes B," and "A contains B" may refer to situations in which no elements other than B are present in A (i.e., A consists solely and exclusively of B), or to situations in which one or more other elements (e.g., element C, elements C and D, or even other elements) are present in entity A in addition to B.

[0013] In addition, it should be noted that the terms "at least one", "one or more", or similar expressions indicating that a feature or element may be present once or more than once will generally be used only once when introducing the corresponding feature or element. Hereinafter, in most cases, when referring to the corresponding feature or element, the expression "at least one" or "one or more" will not be repeated, but the fact that the corresponding feature or element may be present once or more than once will be acknowledged.

[0014] Furthermore, as used hereinafter, the terms "preferably", "more preferably", "particularly", "more particularly", "specifically", "more specifically" or similar terms may be used in conjunction with optional features without limiting other possibilities. Therefore, the features introduced by these terms are optional features and are not intended to limit the scope of the claims in any way. As will be appreciated by those skilled in the art, the present invention may be implemented by using alternative features. Similarly, features introduced by "in an embodiment of the present invention" or similar expressions are intended to be optional features, without any limitation on alternative embodiments of the present invention, without any limitation on the scope of the present invention, and without any limitation on the possibility of combining features introduced in this manner with other optional or non-optional features of the present invention.

[0015] In a first aspect of the present invention, a spectrometer device is disclosed. The term "spectrometer device" refers to a device capable of recording signal intensities relative to corresponding wavelengths or portions thereof (such as wavelength intervals) of a spectrum, wherein the signal intensities can preferably be provided as electrical signals that can be used for further evaluation. The spectrometer device is configured to determine at least one spectral or spectroscopic information of at least one object. Specifically, the spectrometer device is configured to perform at least one spectral measurement, also denoted as spectral analysis. As commonly used, the term "spectrum" refers to an electromagnetic spectrum or a wavelength spectrum. Specifically, a spectrum can be a portion of the visible spectral range and / or the infrared (IR) spectral range, in particular the near-infrared (NIR) spectral range. Here, each portion of the spectrum consists of an optical signal, which is defined by a signal wavelength and a corresponding signal intensity. The spectrometer device can be configured to transmit and / or reflect a spectrum. For example, the spectrometer device can include at least one wavelength selective element, such as at least one linear variable filter element, at least one prism, at least one grating, etc., which is configured to separate incident light into a spectrum of component wavelength signals. The respective intensities of these wavelength signals can be determined using at least one pixelated optical detector and / or at least one grating and at least one single-pixel detector (also referred to as a single-pixel optical detector), as will be outlined in more detail below. When using at least one grating and at least one single-pixel detector, the position of the grating can be gradually varied so that only one wavelength or a narrowly distributed range of wavelengths is incident on the single-pixel detector. For example, the spectrometer device can be configured for absorption spectroscopy and can include, for example, at least one Fourier transform infrared spectroscopy (FTIR) spectrophotometer. In this embodiment, the spectrometer device can include at least one broadband light source. The FTIR spectrophotometer can include at least one interferometer, such as at least one Michelson interferometer. The FTIR spectrophotometer can be configured to illuminate an object with at least one light beam having a time-dependent spectrum. The FTIR spectrophotometer can include at least one moving mirror element, wherein the light beam generated by the broadband light source is alternately blocked and transmitted by the interferometer due to the movement of the mirror element. The spectrometer device can also include at least one microelectromechanical system (MEMS) configured to control the mirror element. The FTIR spectrophotometer can be configured to modulate the light beam depending on wavelength, such that different wavelengths are modulated at different rates. The FTIR spectrophotometer can include at least one fixed detector configured to detect an absorption spectrum of the light beam that has passed through the object. For example, the FTIR spectrophotometer can include at least one single-pixel optical detector.

[0016] As used herein, the term "light" generally refers to a division of electromagnetic radiation, which is generally referred to as "spectral range", and includes one or more of the visible spectral range, the ultraviolet spectral range and the infrared spectral range. Herein, the term "ultraviolet spectral range" generally refers to electromagnetic radiation with a wavelength of 1 nm to 380 nm, preferably 100 nm to 380 nm. Further, the term "visible spectral range" generally refers to a spectral range of 380 nm to 760 nm, partly according to the standard ISO-21348 valid at the date of publication of this document. The term "infrared spectral range" (IR) generally refers to electromagnetic radiation of 760 nm to 1000 pm, wherein the range of 760 nm to 1.5 pm is generally referred to as "near infrared spectral range" (NIR), while the range of 1.5 pm to 15 pm is referred to as "mid infrared spectral range" (MidIR) and the range of 15 pm to 1000 pm is referred to as "far infrared spectral range" (FIR). Preferably, light for typical purposes of the present application is light in the infrared (IR) spectral range, more preferably light in the near infrared (NIR) and mid infrared (MidIR) spectral range, especially light with a wavelength of 1 pm to 5 pm, preferably 1 pm to 3 pm.

[0017] The spectrometer device is configured to determine intensities of component wavelength signals of at least one light beam propagating from an object to the spectrometer device. The spectrometer device comprises at least one distance detector. The distance detector is configured to determine at least one item of distance information about a distance between the at least one object and the spectrometer device. The spectrometer device comprises at least one pixelated imaging detector configured to determine at least one image of the object. The spectrometer device comprises at least one evaluation device. The evaluation device is configured to determine at least one item of material information of the object by evaluating the at least one image of the object determined by the pixelated imaging detector. The evaluation device is configured to perform at least one spectral analysis on the determined intensities of the component wavelength signals taking into account the determined distance information and material information.

[0018] A spectrometer device is configured to determine the intensities of component wavelength signals of at least one light beam propagating from an object to the spectrometer device. For example, the spectrometer device may include at least one wavelength-selective element, such as at least one linear variable filter element, at least one prism, at least one grating, etc., configured to separate incident light into a spectrum of component wavelength signals. The respective intensities of these wavelength signals can be determined using at least one pixelated optical detector and / or at least one grating and at least one single-pixel detector. As used herein, the term "wavelength-selective element" refers to an optical element suitable for separating incident light into a spectrum of component wavelength signals. For example, the wavelength-selective element may be or may include at least one linear variable filter element, at least one prism, at least one grating, etc. For example, the wavelength-selective element may be and / or may include at least one optical filter, such as a variable-length filter, i.e., an optical filter comprising a plurality of filters (preferably a plurality of interference filters), which may be provided in particular as a continuous filter arrangement. Here, each filter may form a bandpass having a variable center wavelength (preferably continuously) for each spatial position on the filter along a single dimension (typically denoted by the term "length") on a receiving surface of the variable-length filter. In a preferred example, the variable center wavelength can be a linear function of the spatial position on the filter, in which case the variable length filter is often referred to as a "linear variable filter" or simply "LVF". However, other types of functions are also applicable to the relationship between the variable center wavelength and the spatial position on the filter. Here, the filter can be located on a transparent substrate, which can in particular contain at least one material that can exhibit a high optical transparency in the visible and / or infrared (IR) spectral range, in particular in the near infrared (NIR) spectral range, as described in more detail below, thereby achieving filter spectral characteristics that vary along the length of the filter, in particular continuously varying spectral characteristics. In particular, the filter element can be a wedge-shaped filter, which can be suitable for carrying at least one responsive coating on the transparent substrate, wherein the responsive coating can exhibit spatially variable characteristics, in particular spatially variable thickness. However, other types of variable length filters comprising other materials or exhibiting other spatially variable characteristics are also feasible. At the normal incidence angle of the incident light beam, the bandpass width of each filter included in the variable length filter can be equal to a fraction of the center wavelength of the specific filter, typically a few percent. For example, for a variable-length filter with a wavelength range of 1400 to 1700 nm and a 1% bandpass width, the bandpass width at normal incidence varies between 14 nm and 17 nm. However, other examples are possible. Due to this special configuration of the variable-length filter, only incident light with a wavelength within the tolerance dictated by the bandpass width and equal to the center wavelength assigned to a specific spatial location on the filter is able to propagate through the variable-length filter at that specific spatial location.Accordingly, a "transmission wavelength" can be defined for each spatial position on the length-variable filter, which can equal the center wavelength of the bandpass width ± 1 / 2. In other words, all light at the transmission wavelength that does not pass the length-variable filter can be absorbed or mostly reflected by the receiving surface of the length-variable filter. Accordingly, the length-variable filter has a varying transmittance, which enables the filter to separate the incident light into a spectrum.

[0019] The spectrometer device can comprise at least one spectral detector configured to determine intensities of component wavelength signals of the at least one light beam propagating from the object to the spectrometer device. The spectral detector can comprise at least one pixelated optical detector configured to determine intensities of the component wavelength signals. The spectral detector can comprise a matrix of optical sensors. The optical sensors of the spectral detector can each have a light-sensitive area. Each optical sensor of the spectral detector can be configured to generate at least one sensor signal in response to an illumination of the light-sensitive area by the at least one light beam propagating from the at least one object to the spectrometer device. Accordingly, the spectral detector can comprise a series of optical sensors, which can preferably be arranged in a one-dimensional matrix in a single row along the length of the length-variable filter, or in a two-dimensional matrix form in more than one row, in particular in two, three or four parallel rows, in particular in order to receive as much of the intensity of the incident light as possible. Accordingly, the number of pixels N in one direction can be higher than the number of pixels M in the other direction, such that a one-dimensional 1 x N matrix or a rectangular two-dimensional M x N matrix can be obtained, with M < 10 and N > 10, preferably N > 20, more preferably N > 50. Additionally, the matrix used herein can also be placed in a staggered arrangement. Here, in particular in order to facilitate the manufacturing of the series of optical sensors, each optical sensor of the spectral detector used herein can have the same or similar optical sensitivity within a tolerance level. However, other kinds of arrangements are feasible as well. For example, as mentioned above, the spectrometer device can be configured for absorption spectroscopy and can comprise, for example, at least one Fourier transform infrared spectroscopy (FTIR) spectrophotometer. In particular, the FTIR spectrophotometer can comprise at least one single-pixel optical detector.

[0020] Each of the optical sensors of the spectral detector can be adapted to receive at least a portion of one of the component wavelength signals. Each component wavelength signal is related to the intensity of each component wavelength. Light that can pass through a specific spatial position on the wavelength selection element at a wavelength selection element can subsequently be incident on the spectral detector. In other words, the spectral detector can preferably be placed in such a way that light can first be incident on the wavelength selection element and thereafter only the portion of light that can pass through a specific spatial position on the wavelength selection filter element can be incident on a corresponding spatial position on the pixelated optical detector of the spectral detector. As a result, the wavelength selection filter element can thus be used to separate the incident light into at least one corresponding spatial position by its associated wavelength(s), while a specific optical sensor comprised by the pixelated optical detector can thus be used to measure the intensity of the incident light that can pass through the wavelength selection filter element at the corresponding spatial position due to its specific wavelength and thus, be incident on the specific optical sensor provided for determining the intensity of the incident light at the specific wavelength. Thus, in a particularly preferred embodiment, the detector can comprise a sequence of optical sensors that can be positioned in the form of a series of optical sensors one after the other, wherein the sequence of optical sensors of the spectral detector can be placed along the length of the wavelength selection element in a parallel manner with respect to the consecutive arrangement of the interference filter.

[0021] In particular, in order to achieve a high resolution of the spectrometer device, each optical sensor of the spectral detector can thus be adapted to receive incident light only over a small spatial angle. This arrangement particularly reflects the setup of the wavelength selection element that is designed to produce the desired spectrum depending on the spatial position of the incident light along the length of the wavelength selection element. This special arrangement can be implemented by a spectral detector comprising a plurality of pixelated optical detectors, wherein each pixelated optical detector is adapted to receive at least a portion of one of the component wavelength signals provided by the length variable filter. As mentioned above, each component wavelength signal is thereby related to the intensity of each component wavelength. As generally used, the term “pixelated optical detector” or “pixelated optical sensor” refers to an optical detector comprising an array of individual pixel sensors, wherein each individual pixel sensor has at least a light sensitive area that produces an electrical signal depending on the intensity of the incident light, wherein the electrical signal can particularly be provided to an evaluation device for further evaluation. Here, the light sensitive area comprised by each individual optical sensor can in particular be a single uniform light sensitive area that is configured to receive incident light incident on the individual optical sensor. However, other arrangements of the optical sensors can also be conceivable.

[0022] The spectral detector can be designed to generate a signal, preferably an electronic signal, which is associated with the intensity of the incident light impinging on the individual optical sensor. The signal can be an analog signal and / or a digital signal. Accordingly, the electronic signals of adjacent optical sensors can be generated simultaneously or in a temporally consecutive manner. By way of example, during a line scan or a row scan, it is possible to generate a series of electronic signals which correspond to a series of individual pixels arranged in a row. In addition, the individual optical sensors can preferably be active pixel sensors which are adapted to amplify the electronic signals before they are provided to an external evaluation unit. To this end, the spectral detector can comprise one or more signal processing devices, such as one or more filters and / or analog-to-digital converters for processing and / or pre-processing the electronic signals.

[0023] The spectral detector can be selected from any known pixel sensor, in particular from a pixelated organic camera element, preferably a pixelated organic camera chip, or from a pixelated inorganic camera element, preferably a pixelated inorganic camera chip, more preferably from a CCD chip or a CMOS chip which is currently commonly used in various cameras. As an alternative, the spectral detector can be or comprise a photoconductor or a photodiode based on a material such as PbS, PbSe, Ge, InGaAs, extended InGaAs, InSb or HgCdTe. As a further alternative, it can comprise a thermoelectric, bolometer or thermopile detector element. Thus, here a camera chip with a matrix of 1 x N pixels or M x N pixels can be used, wherein M < 10 and N > 10, preferably N > 20, more preferably N > 50. Further, a monochromatic camera element, preferably a monochromatic camera chip, can be used, wherein the monochromatic camera element can be selected differently for each pixel sensor, in particular depending on the varying wavelength along the series of optical sensors.

[0024] The spectral detector can thus be adapted to provide a plurality of electrical signals which can be generated by the light-sensitive areas of the optical sensors comprised by the pixelated optical detector. The electrical signals provided by the pixelated optical detector of the spectrometer device can then be forwarded to an evaluation device. In this context, the term "evaluation device" refers to a device which is designated for determining information related to the spectrum of an object whose spectrum has been recorded, in particular using the spectrometer device described herein, wherein the information is obtained by evaluating the detector signals provided by the detector of the spectrometer device. The information can be provided, for example, in an electronic, visual, audible manner or any combination thereof. Furthermore, the information can be stored in a data storage device of the spectrometer device or in a separate storage device and / or can be provided via at least one interface, such as a wireless interface and / or a wired connection interface.

[0025] A spectrometer arrangement can include at least one concentrator device, also known as a light concentrator, for directing a light beam to a wavelength-selective element. As commonly used, the term "concentrator device" refers to a non-imaging optical element having an input and an output, the input also being referred to as an "entrance pupil" or "entrance aperture," and the output being located opposite the input, where the output can also be referred to by either the terms "exit pupil" or "exit aperture." The concentrator device further comprises an optical guiding structure located between the input and the output, wherein the concentrator device, in its normal operating orientation, is adapted to capture light at the input with a large angular spread, concentrate the captured light within the light guiding structure, and emit the concentrated light at the output. In a spectrometer arrangement, the concentrator device can be used in an inverse operating mode, wherein the entrance pupil facing the object can be the smaller aperture of the concentrator device to capture only light from the object, and the exit pupil facing the spectral detector can be the larger aperture of the concentrator device to distribute all collected light to a pixelated optical detector. Thus, for example, a light concentrator can be used in concentrating photovoltaics to allow for high solar concentration at the widest possible angle of incidence. For example, the concentrator device may be selected from: at least one of a tapered or conical light concentrator, a compound parabolic concentrator, a compound elliptical concentrator, and a compound hyperbolic concentrator.

[0026] In addition, the spectrometer device may further include at least one transfer device. The light beam emitted from the object may first pass through the transfer device until it subsequently passes through the wavelength selection element and finally impinges on the spectral detector. Therefore, as used herein, the term "transfer device" refers to an optical component that can be configured to transfer the light beam emitted from the reverse-operating condenser device to the spectral detector. Therefore, in a specific embodiment, the transfer device can be designed to shape the light beam before it is directed to the variable-length filter. In particular, the transfer device can be selected from an optical lens, a reflector, a grating, and a diffraction optical element. More particularly, the optical lens is particularly selected from a biconvex lens, a plano-convex lens, a biconcave lens, a plano-concave lens, an aspheric lens, a cylindrical lens, and a meniscus lens. Therefore, the transfer device can include a material that is at least partially transparent, preferably within the entire wavelength range of the above-mentioned wavelength selection element. For this purpose, the same or similar optically transparent materials mentioned in this regard can also be used. However, other optical elements are also feasible.

[0027] The light emitted from the object can originate from the object itself, but can also optionally have a different origin, from which it propagates to the object and then to the spectrometer device. This latter situation is particularly influenced by the at least one illumination source used. Thus, the light propagating from the object to the spectrometer device can be light that is reflected and / or scattered by the object. Alternatively or additionally, the light can be at least partially transmitted through the object.

[0028] The spectrometer device may include at least one radiation source. As used herein, the term "radiation source" refers to any device suitable for generating and / or providing at least one light beam for irradiating an object. The radiation source can be implemented in various ways. Thus, the radiation source can, for example, be part of the spectrometer device in the housing. However, alternatively or additionally, at least one radiation source can also be arranged outside the housing, for example as a separate light source. The radiation source can be arranged separately from the object and illuminate the object from a distance. As mentioned above, the radiation source can also, alternatively or additionally, be connected to the object or be part of the object, for example so that the electromagnetic radiation emitted from the object can also be generated directly by the radiation source. For example, at least one radiation source can be arranged on and / or in the object and directly generate electromagnetic radiation.

[0029] The illumination source may preferably comprise a luminous element, such as a thermal radiation source, which is known to provide sufficient emission in the visual spectral range and / or in the infrared (IR) spectral range, in particular in the near infrared (NIR) spectral range, in particular an incandescent lamp and / or a thermal infrared emitter, such as is available, for example, from Axetris AG, Schwarzenbergstrasse 10, CH-6056 A micromechanical thermal infrared emitter available under the trade name emirs50 in Switzerland, a thermal infrared emitter from LASER COMPONENTS GmbH, Werner-von-Siemens-Str. 15, 82140 Olching, Germany, or an infrared emitter from Hawkeye Technologies, 181 Research Drive #8, Milford CT06460, United States. Alternatively or additionally, the illumination source can be selected from at least one of the following illumination sources: a laser, in particular a laser diode, although other types of lasers can also be used; a light-emitting diode; an organic light source, in particular an organic light-emitting diode; a neon lamp; a structured light source; a flame source; or a heat source. Alternatively or additionally, other illumination sources can be used. It is particularly preferred that the light emitted by the object and / or the illumination source exhibits a spectral range that is closely related to the spectral sensitivity of the detector, in particular in such a way as to ensure that a detector that can be illuminated by the corresponding illumination source can provide a high-intensity detector signal, thereby enabling evaluation of the detector signal with a sufficient signal-to-noise ratio and simultaneously high resolution. The spectral detector can be sensitive within the emission range of the illumination source.

[0030] The spectrometer device comprises at least one distance detector. As used herein, the term "distance detector" may refer to any device configured for determining distance information about the distance between an object and the spectrometer device, in particular at least one longitudinal coordinate of the object. The distance detector may comprise at least one pixelated optical detector. The pixelated optical detector of the distance detector may be designed as a further pixelated optical detector and / or a spectral detector, as described in more detail above or below, which may be used as the pixelated optical detector of the distance detector. For example, the pixelated optical detectors of the spectral detector and the distance detector may be designed to be identical or different. The spectrometer device may be configured to determine the distance information and the information relating to the spectrum of the object simultaneously or sequentially.

[0031] The spectrometer device can constitute a coordinate system, wherein the longitudinal coordinate is a coordinate along the optical axis of the spectrometer device. The coordinate system can be a polar coordinate system, wherein the optical axis of the spectrometer device forms the z-axis and it can use the distance from the z-axis and the polar angle as additional coordinates. Directions parallel or antiparallel to the z-axis can be considered longitudinal directions and coordinates along the z-axis can be considered longitudinal coordinates or distances. Any direction perpendicular to the z-axis can be considered a transversal direction and the polar coordinate and / or the polar angle can be considered a transversal coordinate. As used herein, the term "distance information" refers to information about the longitudinal coordinate of the object and / or the distance value between the spectrometer device and the object. As used herein, the term "determining at least one distance information" refers to obtaining and / or measuring and / or deriving and / or estimating the distance information. The distance between the object and the spectrometer device can be obtained by using one or more of the following techniques: depth-from-photon-ratio, structured light, beam profile analysis, time-of-flight, shape-from-motion, depth-from-focus, triangulation, depth-from-defocus, stereo sensor. Furthermore, the distance information can be obtained using at least one FiP sensor as described in WO 2012 / 110924 A1 or WO 2014 / 097181 A1. Additionally or alternatively, the distance detector can be configured to determine the distance between the object and the spectrometer device based on a triangulation principle such as at least one triangulation proximity sensor. The distance detector can comprise at least one time-of-flight sensor. For example, at least one of the optical sensors can be adapted to generate at least one sensor signal depending on a time-of-flight (TOF) of an illumination beam propagating from an illumination source to the object and a reflected beam propagating from the object to the time-of-flight sensor. The time-of-flight sensor can be selected from the group consisting of: at least one pulsed time-of-flight detector; at least one phase-modulated time-of-flight detector; at least one direct time-of-flight detector; at least one indirect time-of-flight detector. For example, the pulsed time-of-flight detector can be at least one range-gated imager and / or at least one direct time-of-flight imager. For example, the phase-modulated time-of-flight detector can be at least one RF-modulated light source with at least one phase detector. The time-of-flight sensor can be adapted to determine a time delay between an emission of the illumination beam of the illumination source and a reception of the reflected beam.

[0032] The spectrometer device can be adapted to perform a determination of distance information such as a longitudinal coordinate before and / or during and / or after a spectral measurement.

[0033] Preferably, the spectrometer device, in particular the distance detector, can be adapted to determine position based on a photon ratio ranging technique. For details of methods and devices based on photon ratio ranging techniques, reference is made to International Patent Applications Nos. PCT / EP2017 / 079577, PCT / EP2017 / 079558, PCT / EP2017 / 079564, filed on November 17, 2017, and PCT / EP2018 / 056545, filed on March 15, 2018, the entire contents of which are incorporated herein by reference. Photon ratio ranging is a distance measurement technique that is very flexible in terms of detector technology and, therefore, also in terms of the wavelength of the light source used. Known mobile spectrometer technologies use silicon, InAs, InGaAs, or extended InGaAs detectors, where silicon has a very limited wavelength range, and InAs and InGaAs are both expensive. Lead salt detectors are particularly promising for mobile applications, as novel packaging techniques allow for compact sensor designs (see, for example, WO 2018 / 019921 A1). Using photon ratio ranging allows for reliable distance measurements and can be easily implemented in spectrometers with minimal additional effort.

[0034] The pixelated optical detector of the distance detector may include at least one sensor element comprising a matrix of optical sensors. For example, as described above or in more detail below, a spectral detector may be used as the pixelated optical detector of the distance detector. Additionally or alternatively, in addition to the spectral detector, the spectrometer device may also include a sensor element comprising a matrix of optical sensors. Each of the optical sensors of the distance detector may have a photosensitive region. Each optical sensor of the distance detector may be configured to generate at least one sensor signal, hereinafter referred to as a "distance sensor signal," in response to illumination of the photosensitive region by at least one light beam propagating from at least one object to the spectrometer device. At least one first optical sensor of the distance detector may be adapted to generate a first sensor signal in response to illumination by a first component wavelength, and at least one second optical sensor of the distance detector may be adapted to generate a second sensor signal in response to illumination by the first component wavelength. The evaluation device may be configured to determine at least one longitudinal coordinate z of the object by evaluating a combined signal Q of the first and second sensor signals. The evaluation device may be configured to evaluate the at least one sensor signal generated by the spectral detector by performing at least one spectral analysis taking into account the determined longitudinal coordinate z.

[0035] As used herein, an "optical sensor" generally refers to a photosensitive device for detecting a light beam, such as for detecting an illumination and / or a light spot generated by at least one light beam. As further used herein, a "photosensitive area" generally refers to an area of ​​an optical sensor that can be illuminated from the outside by at least one light beam, and in response to the illumination, at least one sensor signal is generated. The photosensitive area can specifically be located on the surface of the corresponding optical sensor. However, other embodiments are also feasible. The spectrometer device may include a plurality of optical sensors, each having a photosensitive area. As used herein, the term "each optical sensor having at least one photosensitive area" refers to a configuration having a plurality of individual optical sensors, each having one photosensitive area, as well as a configuration having a combined optical sensor having a plurality of photosensitive areas. Furthermore, the term "optical sensor" refers to a photosensitive device configured to generate an output signal.

[0036] Each optical sensor of the distance detector can be implemented so that there is exactly one photosensitive area in the corresponding optical sensor, for example by providing exactly one photosensitive area that can be illuminated, in response to which exactly one uniform sensor signal is generated for the entire optical sensor. Thus, each optical sensor of the distance detector can be a single-area optical sensor. However, the use of single-area optical sensors makes the detector configuration particularly simple and efficient. Thus, as an example, commercially available photosensors, such as commercially available silicon photodiodes, can be used in the device, each photosensor having exactly one photosensitive area. However, other embodiments are also possible. The optical sensor of the distance detector can be part of or constitute a pixelated optical device. For example, the optical sensor of the distance detector can be and / or include at least one CCD and / or CMOS device. As an example, the optical sensor of the distance detector can be part of or constitute at least one CCD and / or CMOS device having a pixel matrix, with each pixel forming a photosensitive area.

[0037] The optical sensor used to determine the distance information and the optical sensor used for spectroscopy can be identical. Specifically, the optical sensor used to determine the distance information can be used as the optical sensor used for spectroscopy, and vice versa. Thus, the optical sensor used to determine the distance information can correspond to or be designed as an optical sensor used for spectroscopy, and / or the matrix of optical sensors of the sensor elements can correspond to or be designed as a pixelated optical detector.

[0038] The optical sensor of the distance detector may specifically be or include at least one photodetector, preferably an inorganic photodetector, more preferably an inorganic semiconductor photodetector, and most preferably a silicon photodetector. Specifically, the optical sensor of the distance detector may be sensitive in the infrared spectral range. All pixels in the matrix or at least one group of optical sensors in the matrix may specifically be identical. Specifically, multiple groups of identical pixels in the matrix may be provided for different spectral ranges, or all pixels may have the same spectral sensitivity. In addition, the pixels may have the same size and / or electronic or optoelectronic properties. Specifically, the optical sensor may be or include at least one inorganic photodiode that is sensitive in the infrared spectral range, preferably in the range of 700 nm to 3.0 microns. Specifically, the optical sensor may be sensitive in a part of the near-infrared region, in which part, specifically in the range of 700 nm to 1100 nm, a silicon photodiode may be applied. Infrared optical sensors that can be used for the optical sensor are commercially available infrared optical sensors, for example those marketed under the trademark Hertzstueck by TrinamiX GmbH, D-67056 Ludwigshafen am Rhein, Germany. TM Commercially available infrared optical sensors. Thus, by way of example, the optical sensor may include at least one optical sensor of the intrinsic photovoltaic type, more preferably at least one semiconductor photodiode selected from the group consisting of: Ge photodiode, InGaAs photodiode, extended InGaAs photodiode, InAs photodiode, InSb photodiode, HgCdTe photodiode. Additionally or alternatively, the optical sensor may include at least one optical sensor of the extrinsic photovoltaic type, more preferably at least one semiconductor photodiode selected from the group consisting of: Ge:Au photodiode, Ge:Hg photodiode, Ge:Cu photodiode, Ge:Zn photodiode, Si:Ga photodiode, Si:As photodiode. Additionally or alternatively, the optical sensor may include at least one photoconductive sensor, such as a PbS or PbSe sensor, or a bolometer, preferably a bolometer selected from the group consisting of: VO bolometer and amorphous Si bolometer.

[0039] The matrix of the distance detector can be composed of individual pixels, such as individual optical sensors. Thus, a matrix of inorganic photodiodes can be formed. However, alternatively, a commercially available matrix can be used, such as one or more of a CCD detector (e.g., a CCD detector chip) and / or a CMOS detector (e.g., a CMOS detector chip). Therefore, generally speaking, the optical sensor of the distance detector can be and / or can include at least one CCD and / or CMOS device, and / or the optical sensor of the detector can form a sensor array (e.g., the matrix described above) or can be part of a sensor array. Thus, as an example, the optical sensor of the distance detector can include and / or form a pixel array, such as a rectangular array having m rows and n columns, where m and n are positive integers. For example, the sensor elements of the distance sensor can include line sensors, where n or m is equal to 1. For example, the sensor elements of the distance detector can include at least two optical sensors arranged in rows and / or columns, such as dual cells. For example, the sensor elements of the distance detector can be a quadrant diode system comprising a 2×2 optical sensor matrix. For example, given more than one column and more than one row, n>1 and m>1 are provided. Thus, as an example, n can be 2 to 16 or higher, and m can be 2 to 16 or higher. Preferably, the ratio of the number of rows to the number of columns is close to 1. As an example, n and m can be selected such that 0.3 ≤ m / n ≤ 3, such as by selecting m / n = 1:1, 4:3, 16:9, or similar values. For example, the array can be a square array with an equal number of rows and columns, such as by selecting m = 2, n = 2 or m = 3, n = 3, etc.

[0040] Specifically, the matrix of the distance detector can be a rectangular matrix with at least one row (preferably multiple rows) and multiple columns. As an example, the rows and columns can be oriented substantially vertically. As used herein, the term "substantially vertical" refers to a condition of vertical orientation with a tolerance of, for example, ±20° or less, preferably a tolerance of ±10° or less, and more preferably a tolerance of ±5° or less. Similarly, the term "substantially parallel" refers to a condition of parallel orientation with a tolerance of, for example, ±20° or less, preferably a tolerance of ±10° or less, and more preferably a tolerance of ±5° or less. Therefore, as an example, a tolerance of less than 20°, specifically less than 10°, or even less than 5° is acceptable. In order to provide a wide field of view, the matrix can specifically have at least 10 rows, preferably at least 500 rows, and more preferably at least 1000 rows. Similarly, the matrix can have at least 10 columns, preferably at least 500 columns, and more preferably at least 1000 columns. The matrix may include at least 50 optical sensors, preferably at least 100,000 optical sensors, and more preferably at least 5,000,000 optical sensors. The matrix may include a plurality of pixels in the range of several million pixels. However, other embodiments are also possible. Therefore, in an arrangement where axial rotational symmetry is desired, a circular or concentric arrangement of the optical sensors (also referred to as pixels) in the matrix is ​​preferably employed.

[0041] Preferably, the photosensitive area of ​​the optical sensor of the distance detector can be oriented substantially perpendicularly to the optical axis of the spectrometer arrangement. The optical axis can be a straight optical axis, or it can be bent or even split, for example by using one or more deflection elements and / or by using one or more beam splitters, wherein in the latter case, the substantially perpendicular orientation can indicate a local optical axis in the corresponding branch or beam path of the optical arrangement.

[0042] In order to determine the longitudinal coordinates of an object using the photon ratio ranging technique, at least two optical sensors of a distance detector may be employed. As described above, in order to obtain distance information, the spectrometer device includes at least one optical sensor and / or a plurality of optical sensors. Specifically, for spectroscopy, one optical sensor in combination with a prism or several optical sensors in combination with optical filters may be employed. For example, for spectroscopy, one of the optical sensors used to determine the longitudinal coordinates of an object using the photon ratio ranging technique may be employed. For example, a pixelated optical detector may be configured for spectroscopy and for determining the longitudinal coordinates of an object using the photon ratio ranging technique. Therefore, the use of photon ratio ranging may allow reliable distance measurement and may be easily implemented in a spectrometer with little additional effort.

[0043] As used herein, a "sensor signal" generally refers to a signal produced by an optical sensor and / or at least one pixel of an optical sensor in response to an illumination. In particular, a sensor signal can be or can comprise at least one electrical signal, e.g. at least one analog electrical signal and / or at least one digital electrical signal. More particularly, a sensor signal can be or can comprise at least one voltage signal and / or at least one current signal. More particularly, a sensor signal can comprise at least one photo current. Further, the raw sensor signal can be used or the pixelated optical detector of the distance detector, the optical sensor of the distance detector or any other element can be adapted to process or pre-process the sensor signal, thereby producing a secondary sensor signal, which can also be used as a sensor signal, e.g. by pre-processing such as filtering. The terms "first" and "second" sensor signal and component wavelength are used as names and do not indicate an order or whether other sensor signals and component wavelengths are used. Each optical sensor of the optical sensor matrix can be configured to produce at least one sensor signal in response to an illumination of the light sensitive area by at least one light beam (in particular having one component wavelength) propagating from at least one object to the spectrometer device. At least one first optical sensor of the optical sensor of the distance detector can be adapted to produce a first sensor signal in response to an illumination by a first component wavelength and at least one second optical sensor of the optical sensor of the distance detector can be adapted to produce a second sensor signal in response to an illumination by the first component wavelength.

[0044] As described above, the evaluation device can be adapted to evaluate the combined signal. Thereby, at least one longitudinal coordinate of the object is determined. As used herein, the term "longitudinal coordinate of the object" refers to a distance between the optical sensor matrix of the distance detector and the object. The evaluation can comprise evaluating a combined signal of the first sensor signal and the second sensor signal. As used herein, the term "combined signal" refers to a signal produced by combining at least two sensor signals, in particular by one or more of: dividing the sensor signals, dividing multiples of the sensor signals, or dividing linear combinations of the sensors. The evaluation device can be configured to derive the combined signal by one or more of: dividing the sensor signals, dividing multiples of the sensor signals, or dividing linear combinations of the sensors. The evaluation device can be configured to determine the longitudinal coordinate using at least one predetermined relationship between the combined signal and the longitudinal coordinate. The predetermined relationship can be one or more of an empirical relationship, a semi-empirical relationship and an analytically derived relationship. The evaluation device can comprise at least one data storage device for storing the predetermined relationship, e.g. a lookup list or a lookup table.

[0045] The combined signal can be determined using various means. For example, software means for deriving the quotient signal and / or hardware means for deriving the quotient signal can be used and implemented in the evaluation device. Thus, for example, the evaluation device can include at least one divider, wherein the divider is configured to derive the quotient signal. The divider can be embodied in whole or in part as either a software divider or a hardware divider, or both. The divider can be fully or partially integrated into the sensor element response, or can be embodied in whole or in part as a separate device from the sensor element.

[0046] For example, the combined signal Q is derived as follows:

[0047]

[0048] where x and y are transverse coordinates, A1 and A2 are different areas of at least one beam profile at the location of a sensor element of the distance detector, and E(x, y, z o ) indicates that at distance z o As used herein, the term "beam profile" refers to at least one intensity distribution of a light spot on an optical sensor as a function of pixels. The beam profile can be selected from a trapezoidal beam profile, a triangular beam profile, a conical beam profile, and a linear combination of Gaussian beam profiles.

[0049] Area A1 and area A2 may be different. In particular, A1 and A2 are not identical. Therefore, one or more of the shape or content of A1 and A2 may be different. Each sensor signal may include at least one item of information of at least one area of ​​the beam profile. In general, the beam profile depends on the brightness L(z o ) and beam shape S(x,y;z o ), E(x,y;z o ) = L·S. Thus, by deriving the combined signal, it is possible to determine the longitudinal coordinate independently of the brightness. In addition, using the combined signal allows the distance z to be determined independently of the object size. o The combined signal thus allows the distance z to be determined independently of the material properties and / or reflection properties and / or scattering properties of the object to be measured and independently of variations in the light source (e.g. due to variations in manufacturing accuracy, heat, water, dust, lens damage, etc.). o .

[0050] Each of the first and second sensor signals may include at least one item of information about at least one region of the beam profile. The photosensitive regions may be arranged such that one of the sensor signals includes information about the first region of the beam profile, and the other sensor signal includes information about the second region of the beam profile. The first region of the beam profile and the second region of the beam profile may be adjacent or overlapping regions, or both. The first and second regions may not be identical in area. The first region of the beam profile may include substantially edge information about the beam profile, and the second region of the beam profile may include substantially center information about the beam profile. The edge information may include information about the number of photons in the first region of the beam profile, and the center information may include information about the number of photons in the second region of the beam profile. The evaluation device may be configured to determine and / or select the first region of the beam profile and the second region of the beam profile. The beam profile may have a center, i.e., a maximum of the beam profile and / or a center point of the beam profile flat top and / or a geometric center of the light spot, and a descending edge extending from the center. The second region may include an inner region of the cross section, and the first region may include an outer region of the cross section. As used herein, the term "substantially center information" generally refers to a low proportion of edge information (i.e., the proportion of the intensity distribution corresponding to the edge) compared to the proportion of center information (i.e., the proportion of the intensity distribution corresponding to the center). Preferably, the proportion of edge information in the center information is less than 10%, more preferably less than 5%, and most preferably, the center information does not include any edge content. As used herein, the term "substantially edge information" generally refers to a low proportion of center information compared to the proportion of edge information. The edge information can include information from the entire beam profile, in particular, information from both the center region and the edge region. The proportion of center information in the edge information is less than 10%, preferably less than 5%, and more preferably, the edge information does not include any center information. If at least one region of the beam profile is near or around the center and includes substantially center information, then this at least one region can be determined and / or selected as the second region of the beam profile. If at least one region of the beam profile includes at least some portion of the descending edge of the cross-section, then this at least one region can be determined and / or selected as the first region of the beam profile. For example, the entire cross-section can be determined as the first region. The first region of the beam profile can be region A1, and the second region of the beam profile can be region A2.

[0051] Other options for the first area A1 and the second area A2 are also possible. For example, the first area can include a substantially outer area of ​​the beam profile, and the second area can include a substantially inner area of ​​the beam profile. For example, in the case of a two-dimensional beam profile, the beam profile can be divided into a left portion and a right portion, wherein the first area can include an area substantially of the left portion of the beam profile, and the second area can include an area substantially of the right portion of the beam profile.

[0052] The edge information may comprise information about the number of photons in a first region of the beam profile, whereas the center information may comprise information about the number of photons in a second region of the beam profile. The evaluation device may be adapted to determine an area integral of the beam profile. The evaluation device may be adapted to determine the edge information by integrating and / or summing over the first region. The evaluation device may be adapted to determine the center information by integrating and / or summing over the second region. For example, the beam profile may be a trapezoidal beam profile, and the evaluation device may be adapted to determine the integral of the trapezoid. Furthermore, when a trapezoidal beam profile is assumed, the determination of the edge signal and the center signal may be replaced by using an equivalent evaluation of the properties of the trapezoidal beam profile, e.g. determining the slope and position of the edges and the height of the center flat top and deriving the edge signal and the center signal by means of geometrical considerations.

[0053] Additionally or alternatively, the evaluation device can be adapted to determine one or both of center information and edge information from at least one slice or cut of the light spot. This can be achieved, for example, by replacing the area integral in the combined signal with a line integral along the slice or cut. To improve accuracy, several slices or cuts of the light spot can be used and averaged. In the case of an elliptical light spot profile, averaging several slices or cuts improves the distance information.

[0054] The evaluation device can be configured to derive the combined signal by one or more of: dividing the corresponding edge information and the corresponding center information, dividing the corresponding edge information and a multiple of the corresponding center information, and dividing a linear combination of the corresponding edge information and the corresponding center information. Thus, essentially, the photon ratio can be used as the physical basis of the photon ratio ranging technique.

[0055] As explained, for example, in WO 2012 / 110924 A1 or WO 2014 / 097181 A1, there is generally a predetermined or determinable relationship between the size of the light spot (e.g., its diameter, beam waist, or equivalent diameter) and the longitudinal coordinate of the object from which the light beam propagates toward the sensor element of the distance detector. Without wishing to be bound by this theory, the light spot can be characterized by two measurement variables: a measurement signal measured in a small measurement region at or near the center of the light spot (also called a center signal), and an integral signal or sum signal integrated over the light spot with or without the center signal. For a light beam with a certain total power that does not change when the beam is widened or focused, the sum signal should be independent of the spot size of the light spot and, therefore, at least when using a linear optical sensor within its corresponding measurement range, should be independent of the distance between the object and the sensor element of the distance detector. However, the center signal depends on the light spot size. Therefore, the center signal generally increases when the light beam is focused and decreases when the beam is defocused. Thus, by comparing the center signal with the sum signal, an item of information about the size of the light spot produced by the light beam and, thus, about the longitudinal coordinate of the reflection position can be generated. For example, the comparison of the center signal with the sum signal can be performed by forming a combined signal Q from the center signal and the sum signal and by using a predetermined or determinable relationship between the longitudinal coordinate and the combined signal for deriving the longitudinal coordinate.

[0056] The light beam incident on the optical sensors of the distance detector can completely illuminate the at least one optical sensor of the distance detector that generates the center signal, such that the at least one optical sensor that generates the center signal is completely located within the light beam, wherein the width of the light beam is greater than the photosensitive area of ​​the at least one optical sensor of the distance detector that generates the sensor signal. Conversely, preferably, the light beam can specifically generate a light spot that is smaller than the matrix over the entire matrix, such that the light spot is completely located within the matrix. A person skilled in the art of optics can easily adjust this by selecting one or more suitable lenses or elements that have a focusing or defocusing effect on the light beam, such as by using a suitable transfer device, as will be outlined in more detail below. As further used herein, a "light spot" generally refers to a visible or detectable circular or non-circular illumination.

[0057] The combined signal Q can be derived by one or more of: forming a quotient of the first signal and the second signal, or vice versa; forming a quotient of a multiple of the first signal and a multiple of the second signal, or vice versa; forming a quotient of a linear combination of the first signal and a linear combination of the second signal, or vice versa; forming a quotient of a first linear combination of the first signal and the second signal and a second linear combination of the first signal and the second signal. The evaluation device can be configured to determine the longitudinal coordinate z using at least one predetermined relationship between the combined signal Q and the longitudinal coordinate z of the object. The evaluation device can include at least one divider, wherein the divider is configured to derive the combined signal.

[0058] The evaluation device may be adapted to determine those optical sensors in the matrix of optical sensors of the distance detector that are illuminated by the first wavelength component. The evaluation device may be configured to determine at least one optical sensor of the distance detector that is illuminated by the first wavelength component and has the highest sensor signal and to form a first sensor signal. The first sensor signal may be at least one center signal. The evaluation device may be configured to evaluate the sensor signals of the optical sensors in the matrix that are illuminated by the first wavelength component and to form a second sensor signal. The second sensor signal may be at least one sum signal. The evaluation device may be configured to determine a combined signal Q by combining the center signal and the sum signal.

[0059] The term "center signal" generally refers to at least one sensor signal that includes information about the substantial center of a beam profile. As used herein, the term "highest sensor signal" refers to either a local maximum or a maximum within a region of interest, or both. For example, the center signal may be the signal of at least one optical sensor and / or pixel of a distance detector having the highest sensor signal among a plurality of sensor signals generated by the optical sensors and / or pixels of the entire matrix or pixels of a region of interest within the matrix, where the region of interest may be predetermined or determinable within an image generated by the optical sensors and / or pixels in the matrix of the distance detector. The center signal may be generated from a single optical sensor and / or pixel or from a group of optical sensors. In the latter case, the sensor signals of the group of optical sensors and / or pixels may be accumulated, integrated, or averaged to determine the center signal. The group of optical sensors and / or pixels generating the center signal may be a group of adjacent optical sensors and / or pixels, such as those located less than a predetermined distance from the actual optical sensor and / or pixel having the highest sensor signal, or may be a group of optical sensors and / or pixels generating sensor signals within a predetermined range from the highest sensor signal. The set of optical sensors and / or pixels generating the central signal may be selected to be as large as possible in order to allow for a maximum dynamic range. The evaluation device may be adapted to determine the central signal by integrating a plurality of sensor signals, for example a plurality of optical sensors and / or pixels located around the optical sensor and / or pixel having the highest sensor signal.

[0060] As described above, the center signal can generally be a single sensor signal, such as a sensor signal from an optical sensor and / or pixel of a distance detector at the center of the light spot, or can be a combination of multiple sensor signals, such as a combination of sensor signals from an optical sensor and / or pixel at the center of the light spot, or a secondary sensor signal derived by processing sensor signals derived according to one or more of the above possibilities. The determination of the center signal can be performed electronically, as the comparison of the sensor signals can be implemented relatively simply using conventional electronics, or can be performed entirely or partially by software. Specifically, the center signal can be selected from: the highest sensor signal; the average value of a group of sensor signals within a predetermined tolerance range relative to the highest sensor signal; the average value of sensor signals from a group of optical sensors and / or pixels including the optical sensor and / or pixel with the highest sensor signal and a predetermined group of adjacent optical sensors and / or pixels; the sum of sensor signals from a group of optical sensors and / or pixels including the optical sensor and / or pixel with the highest sensor signal and a predetermined group of adjacent optical sensors and / or pixels; the sum of a group of sensor signals within a predetermined tolerance range relative to the highest sensor signal; the average value of a group of sensor signals above a predetermined threshold; the sum of a group of sensor signals above a predetermined threshold; the integral of sensor signals from a group of optical sensors including the optical sensor with the highest sensor signal and a predetermined group of adjacent optical sensors; the integral of a group of sensor signals within a predetermined tolerance range relative to the highest sensor signal; the integral of a group of sensor signals above a predetermined threshold.

[0061] Similarly, the term "sum signal" generally refers to a signal that includes substantial edge information of the beam profile. For example, the sum signal can be derived by accumulating, integrating, or averaging the sensor signals of the entire matrix of distance detectors or a region of interest within the matrix, where the region of interest can be predetermined or determinable within an image generated by the optical sensors in the matrix. When accumulating, integrating, or averaging the sensor signals, the actual optical sensors generating the sensor signals can be omitted from the accumulation, integration, or averaging, or they can be included in the accumulation, integration, or averaging. The evaluation device can be adapted to determine the sum signal by integrating the signals of the entire matrix or the region of interest within the matrix. For example, the beam profile can be a trapezoidal beam profile, and the evaluation device can be adapted to determine the integral of the entire trapezoid. Furthermore, when assuming a trapezoidal beam profile, the determination of the edge and center signals can be replaced by using equivalent evaluations of the characteristics of the trapezoidal beam profile, such as determining the slope and position of the edges and the height of the central plateau, and deriving the edge and center signals through geometric considerations.

[0062] Similarly, the center signal and edge signal can also be determined by using segments of the beam profile, such as circular segments of the beam profile. For example, the beam profile can be divided into two segments along a secant or chord that does not pass through the center of the beam profile. Thus, one segment essentially contains edge information, while the other segment essentially contains center information. For example, to further reduce the amount of edge information in the center signal, the edge signal can be further subtracted from the center signal.

[0063] Additionally or alternatively, the evaluation device can be adapted to determine one or both of center information and edge information from at least one slice or cut of the light spot. This can be achieved, for example, by replacing the area integral in the quotient with a line integral along the slice or cut. To improve accuracy, several slices or cuts through the light spot can be used and averaged. In the case of an elliptical light spot profile, averaging several slices or cuts results in improved distance information.

[0064] The center signal is selected from: the highest sensor signal; the average value of a group of sensor signals within a predetermined tolerance range relative to the highest sensor signal; the average value of sensor signals from a group of optical sensors of the distance detector including the optical sensor with the highest sensor signal and a predetermined group of adjacent optical sensors; the sum of sensor signals from a group of optical sensors of the distance detector including the optical sensor with the highest sensor signal and a predetermined group of adjacent optical sensors; the sum of a group of sensor signals within a predetermined tolerance range relative to the highest sensor signal; the average value of a group of sensor signals above a predetermined threshold; the sum of a group of sensor signals above a predetermined threshold; the integral of sensor signals from a group of optical sensors including the optical sensor of the distance detector with the highest sensor signal and a predetermined group of adjacent optical sensors; the integral of a group of sensor signals within a predetermined tolerance range relative to the highest sensor signal; the integral of a group of sensor signals above a predetermined threshold. The sum signal is selected from: an average value of all sensor signals of the distance detector matrix; a sum of all sensor signals of the distance detector matrix; an integral of all sensor signals of the distance detector matrix; an average value of all sensor signals of the distance detector matrix excluding sensor signals from those optical sensors of the distance detector contributing to the center signal; a sum of all sensor signals of the distance detector matrix excluding sensor signals from those optical sensors of the distance detector contributing to the center signal; an integral of all sensor signals of the distance detector matrix excluding sensor signals from those optical sensors of the distance detector contributing to the center signal; a sum of sensor signals of optical sensors of the distance detector located within a predetermined range of the distance detector relative to the optical sensor with the highest sensor signal; an integral of sensor signals of optical sensors of the distance detector located within a predetermined range of the distance detector relative to the optical sensor with the highest sensor signal; a sum of sensor signals above a specific threshold value of optical sensors of the distance detector located within a predetermined range of the distance detector relative to the optical sensor with the highest sensor signal; an integral of sensor signals above a specific threshold value of optical sensors of the distance detector located within a predetermined range of the distance detector relative to the optical sensor with the highest sensor signal.

[0065] The combined signal Q may be a signal generated by combining the center signal and the sum signal. Specifically, the determination may include one or more of the following: forming a quotient of the center signal and the sum signal, or vice versa; forming a quotient of multiples of the center signal and multiples of the sum signal, or vice versa; forming a quotient of a linear combination of the center signal and a linear combination of the sum signal, or vice versa. Additionally or alternatively, the combined signal Q may include any signal or signal combination that contains at least one item of information regarding the comparison between the center signal and the sum signal.

[0066] The spectrometer device can be configured to determine at least one spectrum or spectroscopic information of at least one object. The spectrometer device includes at least one evaluation device configured to perform at least one spectral analysis while taking into account the determined distance information. The evaluation device can be configured to perform at least one spectral analysis while taking into account the determined longitudinal coordinate z. During the spectral analysis, at least one spectrum or spectroscopic information of the object can be determined. Specifically, the evaluation device can be configured to determine light attenuation due to the distance between the object and the spectrometer device. As used herein, the term "light attenuation" refers to the intensity loss caused by the path of a light beam (i.e., the distance from the object to the spectrometer device), the presence of the object, and / or environmental conditions. As used herein, the term "determining light attenuation" means approximating, measuring, and / or deriving light attenuation. The spectral analysis can include determining at least one difference in at least one light characteristic due to the presence of the object. The difference in the light characteristic can be selected from: at least one wavelength-dependent intensity difference; and at least one wavelength-dependent polarization difference. The evaluation device can be adapted to perform the spectral analysis while taking into account the light attenuation. The evaluation device can be adapted to correct the spectral intensity of the component wavelength signals determined by the optical detector. Specifically, the evaluation device can be adapted to correct the determined intensity values ​​for light attenuation, for example, by multiplying and / or dividing the determined intensity values ​​by at least one correction function. The correction function can be determined empirically, semi-empirically, and / or analytically. For example, the spectrometer device can be configured to determine light attenuation by measuring a background spectrum that depends on the optics, the light source, its properties, dust, and the like. The spectrometer device can be configured to derive a correction function, such as a background correction function, from this. However, the distance between the object and the spectrometer device can remain fixed during the background spectrum measurement. The spectrometer device can be a movable spectrometer device. Specifically, the distance between the object and the spectrometer device can be variable. The evaluation device can be configured to determine changes in the distance between the object and the spectrometer device. Therefore, the intensity values ​​and / or background spectrum must be further corrected for the effects caused by the distance between the object and the spectrometer device, as well as changes in the distance. The evaluation device can be adapted to correct the determined light attenuation for the effects caused by the distance between the object and the spectrometer device. To correct for light attenuation caused by distance, a further correction function, such as a polynomial correction function, for example a quadratic or higher-order polynomial, can be used. For example, the distance-dependent light attenuation can be corrected by a fraction of a polynomial in z, such as a polynomial in z of at most third degree divided by a polynomial in z of at most fifth degree, and the coefficients can be used to adjust the distance-dependent light attenuation function. For example, the correction function can be a rational polynomial function. For example, the polynomial A·1 / z can be used. 2, where A is a coefficient or constant and z is the longitudinal coordinate z. Further correction functions can be determined taking into account the luminous properties of the illumination source. Furthermore, further correction functions can be determined by taking into account predetermined reflective properties of the object (e.g., using a spot profile and / or assumed reflective properties of the object). Furthermore, the correction function can be a combined correction function that corrects for light attenuation caused by optics, ambient light, dust, and temperature, while also correcting for distance-dependent light attenuation. As an example, the combined correction function can be the product of a distance-independent correction function (such as a background correction function) and a distance-dependent correction function.

[0067] The spectrometer device may be adapted to determine at least one further longitudinal coordinate of the object by evaluating a combined signal Q of the first sensor signal and the second sensor signal generated in response to the second component wavelength. The evaluation device may be adapted to determine a combined longitudinal coordinate, such as an average value, from the longitudinal coordinate and the further longitudinal coordinate, and to perform the spectroscopic analysis taking into account the combined longitudinal coordinate.

[0068] As further used herein, the term "evaluation device" generally refers to any device adapted to perform a specified operation, preferably by using at least one data processing device, more preferably by using at least one processor and / or at least one application specific integrated circuit. Thus, as an example, at least one evaluation device may include at least one data processing device having software code comprising a plurality of computer commands stored thereon. The evaluation device may provide one or more hardware elements for performing one or more of the specified operations, and / or may provide one or more processors with software running thereon for performing one or more of the specified operations.

[0069] The aforementioned operations, including determining the longitudinal coordinates and performing the spectral analysis, are performed by at least one evaluation device. Thus, by way of example, one or more of the aforementioned relationships can be implemented in software and / or hardware, such as by implementing one or more lookup tables. Thus, by way of example, the evaluation device can include one or more programmable devices configured to perform the aforementioned evaluations, such as one or more computers, application-specific integrated circuits (ASICs), digital signal processors (DSPs), or field-programmable gate arrays (FPGAs). However, additionally or alternatively, the evaluation device can also be embodied in whole or in part by hardware.

[0070] The spectrometer device can be configured for contactless spectroscopy. Determining distance information and using it for correction of the spectral measurement allows for a variable distance between the object and the spectrometer device. Direct mechanical contact or the use of special sample holders can be avoided.

[0071] The evaluation device may further be configured to determine at least one lateral coordinate of the object by evaluating the lateral position of the at least one optical sensor of the distance detector having the highest sensor signal.

[0072] As described above, the spectrometer apparatus includes at least one wavelength-selective element configured to separate an incident light beam into spectra of component wavelength signals, the respective intensities of which are determined using a spectral detector. The wavelength-selective element is a linear variable filter. The spectrometer apparatus may include at least one conical concentrator. The spectrometer apparatus may also include at least two illumination sources, each configured to illuminate the object by generating and emitting at least one illumination beam. The illumination sources may be used simultaneously or alternately to illuminate the object. The illumination beam from one of the illumination sources may be directly reflected by the object and / or may be scattered from the object. Direct reflection alone cannot be directly distinguished from spectral information. Direct reflection may be distance-dependent and less wavelength-dependent than scattered light. The spectrometer apparatus may be adapted to separate the spectra of direct reflection and scattered light by recording at least two spectra at different object distances and / or by alternating between recording and comparing at least two spectra using two light sources. The spectrometer apparatus may be adapted to record spectra using both light sources simultaneously.

[0073] For example, the illumination sources can be used alternately. For example, initially, an illumination beam originating from, for example, a first illumination source may be directly reflected by and / or scattered from an object at a first distance, and the optical sensor of the spectral detector may be adapted to record at least one first spectrum. Subsequently, an illumination beam originating from a second illumination source may be directly reflected by and / or scattered from an object at the first distance, and the optical sensor of the spectral detector may be adapted to record at least one second spectrum. The order or sequence of using the illumination sources is described only as an example, and the illumination sources may also be used in other orders, such as first using the second illumination source and then the first illumination source, and / or repeatedly using one or both illumination sources. The spectrometer device, in particular the distance detector, may be configured to determine at least one item of distance information regarding the distance between at least one object and the spectrometer device. By comparing the first and second spectra, the evaluation device may be configured to determine at least two direct reflection peaks on the sensor element, specifically, a first direct reflection peak originating from direct reflection of the object illuminated by the first illumination source, and a second reflection peak originating from direct reflection of the object illuminated by the second illumination source. Specifically, the evaluation device can be configured to determine the difference in intensity distribution as a function of wavelength (particularly, peak) by comparing the first and second spectra. The positions of the peaks determined in the respective spectra can correspond to positions on the optical sensor matrix of the distance detector and / or spectral detector. The evaluation device can be adapted to determine the position at which directly reflected light impinges on the optical sensor matrix. For example, the evaluation device can be adapted to determine the position at which directly reflected light originating from an object illuminated by a first illumination source at a first distance impinges on the optical sensor matrix. The evaluation device can be adapted to determine the position at which directly reflected light originating from an object illuminated by a second illumination source at a first distance impinges on the optical sensor matrix. As described above, direct reflections can be distance-dependent and less wavelength-dependent than scattered light. Once the position at which the directly reflected light impinges on the optical sensor matrix is ​​known, distance information regarding the first distance between the spectrometer device and the object can be determined using a triangulation algorithm. The evaluation device can be adapted to determine the first distance to the object by using at least one triangulation algorithm.

[0074] For example, the illumination sources may be used simultaneously. For example, an illumination beam originating from a first illumination source may be directly reflected by an object at a first distance and / or may be scattered from the object. A second illumination source may be used to illuminate the object. An illumination beam originating from the second illumination source may be directly reflected by an object at the first distance and / or may be scattered from the object. The optical sensor of the spectral detector may be adapted to record at least one first spectrum of the received light for the object at the first distance. The recording of the spectrum is repeated for the second distance from the object. For example, an illumination beam originating from the first illumination source may be directly reflected by an object at a second distance and / or may be scattered from the object. An illumination beam originating from the second illumination source may be directly reflected by an object at the second distance and / or may be scattered from the object. The optical sensor of the spectral detector may be adapted to record at least one first spectrum of the received light for the object at the second distance. By comparing the first and second spectra, the evaluation device may be configured to determine at least four direct reflection peaks on the sensor element. Specifically, the evaluation device may be adapted to determine, in the first spectrum, a first direct reflection peak originating from direct reflection of the object illuminated by the first illumination source and a second reflection peak originating from direct reflection of the object illuminated by the second illumination source. Specifically, the evaluation device can be adapted to determine, in the second spectrum, a third direct reflection peak originating from direct reflection from an object illuminated by the first illumination source and a fourth direct reflection peak originating from direct reflection from an object illuminated by the second illumination source. The evaluation device can be configured to determine a difference in the intensity distribution as a function of wavelength (particularly, peaks) by comparing the first spectrum and the second spectrum. The positions of the peaks determined in the respective spectra can correspond to positions on an optical sensor matrix of the distance detector and / or the spectrum detector. Specifically, the evaluation device can be adapted to determine the position at which the directly reflected light impinges on the optical sensor matrix. For example, the light is reflected from an object at a first distance and a second distance, and the evaluation device can be adapted to determine the position at which the directly reflected light impinges on the optical sensor matrix from the object at the first distance illuminated by the first illumination source. The evaluation device can be adapted to determine the position at which the directly reflected light impinges on the optical sensor matrix from the object at the first distance illuminated by the second illumination source. The evaluation device can be adapted to determine the position at which the directly reflected light impinges on the optical sensor matrix from the object at the second distance illuminated by the first illumination source. The evaluation device can be adapted to determine the position at which the directly reflected light impinges on the optical sensor matrix from the object at the first distance illuminated by the second illumination source. Once at least two positions corresponding to the same object distance of the position where the directly reflected light is incident on the matrix are known, distance information about the distance between the spectrometer device and the object can be determined using a triangulation algorithm. The evaluation device can be adapted to determine the distance to the object by using at least one triangulation algorithm.

[0075] The evaluation device is configured to determine material information of the object by evaluating at least one image of the object determined by at least one pixelated imaging detector, wherein the at least one pixelated imaging detector is configured to determine the at least one image of the object. As used herein, the term "material information" refers to any information about the material of the object that is configured to characterize and / or identify and / or classify the material. For example, the material information may be at least one property selected from the group consisting of: scattering coefficient, translucency, transparency, deviation of reflection from a Lambertian surface, speckle, material and / or material class; object type and / or object class; and the like. The material information may include information regarding material properties. As used herein, the term "material property" refers to at least one property of a material that is configured to characterize and / or identify and / or classify the material. For example, the material property may be a property selected from the group consisting of: roughness, light penetration depth into the material, a property characterizing the material as biological or non-biological, reflectivity, specular reflectivity, diffuse reflectivity, surface properties, translucency, scattering, particularly backscattering behavior, and the like. The at least one material property may be a property selected from the group consisting of: scattering coefficient, translucency, transparency, deviation of reflection from a Lambertian surface, speckle, and the like.

[0076] As used herein, the term "determining material information" refers to at least one of: determining material properties and assigning material properties to an object. The evaluation device may include at least one database comprising a list and / or table of predefined and / or predetermined material information, such as a lookup list or table. The list and / or table of material information may be determined and / or generated by performing at least one test measurement using a spectrometer according to the present invention, for example, by performing a material test using a sample with known material properties. The list and / or table of material information may be determined and / or generated at the manufacturer's site and / or by a user of the spectrometer device. Material information may also be assigned to a material classifier, such as one or more of the following: a material name; a material group, such as biological or non-biological material, translucent or non-translucent material, metal or non-metal, skin or non-skin, fur or non-fur, carpet or non-carpet, reflective or non-reflective, specular or non-specular, foam or non-foam, hair or non-hair; a roughness group, etc. The evaluation device may comprise at least one database comprising lists and / or tables comprising material information and associated material names and / or material groups.

[0077] In general, an "object" can be any sample of interest. A sample can be a liquid sample or a solid sample. An object can include one or more articles and / or one or more parts of an article, wherein at least one article or at least one part thereof can include at least one component that can provide a spectrum suitable for investigation. Additionally or alternatively, an object can be or can include one or more living beings and / or one or more parts thereof, such as one or more body parts or body fluids of a human (e.g., a user) or an animal. For example, an object can be at least one object selected from the group consisting of: a scene, a human (such as a person), wood, a carpet, foam, an animal (such as a cow), a plant, a piece of tissue, metal, a toy, a metal object, a beverage, a food (such as fruit, meat, fish), a dish, cosmetics, applied cosmetics, cloth, fur, hair, a skincare product, a cream, oil, powder, carpet, juice, a suspension, paint, a plant, a body, a body part, an organic material, an inorganic material, a reflective material, a screen, a display, a wall, a piece of paper, such as a photograph. The object can include at least one surface onto which the radiation is projected. The surface can be adapted to at least partially reflect the radiation toward the spectrometer device. For example, without wishing to be bound by theory, human skin may have a reflectance profile, also denoted as a backscattering profile, which includes a portion resulting from back reflection from the surface, denoted as surface reflection, and a portion resulting from diffuse reflection of light that penetrates the skin, denoted as the diffuse portion of back reflection. For information on the reflectance profile of human skin, see "Lasertechnik in der Medizin: Grundlagen, Systeme, Anwendungen," "Wirkung von Laserstrahlung auf Gewebe," 1991, pp. 171-266, Jürgen Eichler, Theo Seiler, Springer-Verlag, ISBN 0939-0979. The surface reflectance of skin may increase with increasing wavelength toward the near infrared. Furthermore, the penetration depth may increase with increasing wavelength from the visible to the near infrared. The diffuse portion of back reflection may also increase with the penetration depth of light. These material properties can be used to distinguish skin from other materials, specifically by analyzing the backscattering profile.

[0078] Specifically, the spectrometer device can be configured to detect biological tissue, particularly human skin. As used herein, the term "biological tissue" generally refers to biological material comprising living cells. The spectrometer device can be a device for detecting (particularly optically detecting) biological tissue, particularly human skin. The term "detection of biological tissue" refers to determining and / or verifying whether a surface to be inspected or tested is or comprises biological tissue, particularly human skin, and / or distinguishing biological tissue (particularly human skin) from other tissues (particularly other surfaces), and / or distinguishing different types of biological tissue, such as different types of human tissue, such as muscle, fat, organs, etc. For example, biological tissue can be or include human tissue or a portion thereof, such as skin, hair, muscle, fat, organs, etc. For example, biological tissue can be or include animal tissue or a portion thereof, such as skin, fur, muscle, fat, organs, etc. For example, biological tissue can be or include plant tissue or a portion thereof. The spectrometer device can be adapted to distinguish animal tissue or a portion thereof from one or more of inorganic tissues, metal surfaces, and plastic surfaces (e.g., agricultural machinery or milking machines). The spectrometer device may be suitable for distinguishing plant tissue or parts thereof from one or more of inorganic tissue, metal surfaces, plastic surfaces, for example, of agricultural machinery. The spectrometer device may be suitable for distinguishing food and / or beverages from plates and / or glasses. The spectrometer device may be suitable for distinguishing different types of food, such as fruit, meat, and fish. The spectrometer device may be suitable for distinguishing cosmetics and / or applied cosmetics from human skin. The spectrometer device may be suitable for distinguishing human skin from foam, paper, wood, displays, screens. The spectrometer device may be suitable for distinguishing human skin from clothing. The spectrometer device may be suitable for distinguishing materials of maintenance products and machine parts, such as metal parts, etc. The spectrometer device may be suitable for distinguishing organic materials from inorganic materials. The spectrometer device may be suitable for distinguishing human biological tissue from the surfaces of artificial or inanimate objects. The spectrometer device may be particularly useful for non-therapeutic and non-diagnostic applications.

[0079] The evaluation device may be configured to determine the material information by applying at least one material-dependent image filter φ to the image of the object determined by the pixelated imaging detector. In particular, the evaluation device may be configured to determine at least one material characteristic by applying the material-dependent image filter φ to the image of the object determined by the imaging detector.

[0080] The material-dependent image filter may be at least one filter selected from the following: a brightness filter; a speckle shape filter; a square norm gradient; a standard deviation; a smoothness filter, such as a Gaussian filter or a median filter; a contrast filter based on grayscale appearance; an energy filter based on grayscale appearance; a homogeneity filter based on grayscale appearance; a dissimilarity filter based on grayscale appearance; a law energy filter; a threshold area filter; or a linear combination thereof, or by |ρ φother，φm |≥0.40 Another material-related image filter φ associated with one or more of a brightness filter, a speckle shape filter, a squared norm gradient, a standard deviation, a smoothness filter, an energy filter based on grayscale appearance, a homogeneity filter based on grayscale appearance, a dissimilarity filter based on grayscale appearance, an energy filter of a law, or a threshold area filter, or a linear combination thereof other , where φ m It is one of the brightness filter, speckle shape filter, square norm gradient, standard deviation, smoothness filter, energy filter based on grayscale appearance, homogeneity filter based on grayscale appearance, dissimilarity filter based on grayscale appearance, law energy filter, or threshold area filter, or a linear combination thereof.

[0081] As used herein, the term "image of an object" refers to an image determined by a pixelated imaging detector that includes at least one object feature (such as a reflective feature). As further used herein, but without limitation, the term "image" may specifically refer to data recorded using an imaging detector, such as a plurality of electronic readouts from an imaging device, such as pixels of a pixelated optical detector. Thus, the image itself may include pixels, the pixels of the image being associated with the pixels of the pixelated optical detector. Thus, when referring to a "pixel", either reference is made to a unit of image information produced by a single pixel of the pixelated optical detector, or directly to a single pixel of the pixelated optical detector. As used herein, the term "reflective feature" refers to a feature within an image plane generated by the object in response to illumination, for example, using at least one illumination feature. In particular, the imaging detector may be configured to determine and / or image and / or record an image of an object.

[0082] At least one light beam can propagate from the object toward the spectrometer device. The light beam can originate from the object or from an illumination source, such as an illumination source that directly or indirectly illuminates the object, wherein the light beam is reflected or scattered by the object and thereby directed at least partially toward the detector. The spectrometer device can be used in active and / or passive illumination scenarios. For example, the at least one illumination source can be adapted to illuminate the object, for example, by directing the light beam toward an object that reflects the light beam. In addition to or as an alternative to the at least one illumination source, the spectrometer device can also utilize radiation already present in the scene, such as radiation from at least one ambient light source.

[0083] The imaging detector may be configured to record a beam profile of at least one reflection feature of an image of the object. The evaluation device may be configured to identify and / or select at least one reflection feature, in particular at least one light spot, in the image provided by the imaging detector. The evaluation device may be configured to perform at least one image analysis and / or image processing in order to identify the reflection feature. The image analysis and / or image processing may use at least one feature detection algorithm. The image analysis and / or image processing may include one or more of the following: filtering; selection of at least one region of interest; forming a difference image between an image created by the sensor signal and at least one offset; inverting the sensor signal by inverting the image created by the sensor signal; forming a difference image between images created by the sensor signal at different times; background correction; decomposition into color channels; decomposition into hue, saturation and brightness channels; frequency decomposition; singular value decomposition; applying a blob detector; applying a corner detector; applying a Hessian determinant filter; applying a region detector based on the curvature principle; applying a maximum stable extremum region detector; applying a generalized Hough transform; applying a ridge detector; applying an affine invariant feature detector; applying an affine adaptive interest point operator; applying a Harris affine region detector; applying a Hessian affine The image is evaluated by applying a region detector, applying a scale-invariant feature transform, applying a scale-space extrema detector, applying a local feature detector, applying an accelerated robust feature algorithm, applying a gradient localization and orientation histogram algorithm, applying a histogram of orientation gradient descriptors, applying a Deriche edge detector, applying a differential edge detector, applying a spatiotemporal interest point detector, applying a Moravec corner detector, applying a Canny edge detector, applying a Laplacian-Gaussian filter, applying a difference-of-Gaussian filter, applying a Sobel operator, applying a Laplacian operator, applying a Scharr operator, applying a Prewitt operator, applying a Roberts operator, applying a Kirsch operator, applying a high-pass filter, applying a low-pass filter, applying a Fourier transform, applying a Radon transform, applying a Hough transform, applying a wavelet transform, thresholding, and creating a binary image. Specifically, the image is evaluated by selecting a region of interest in the image. The region of interest can be manually determined by a user or automatically, such as by identifying an object within the image generated by a sensor element. For example, in the case of a light spot-like reflection feature, the region of interest can be selected as the area surrounding the light spot outline.

[0084] For example, the illumination source can be adapted to generate and / or project a point cloud such that a plurality of illumination regions are generated on a matrix of optical sensors (e.g., CMOS detectors). Furthermore, interference may be present on the optical sensor matrix of the imaging detector, such as interference caused by speckle and / or external light and / or multiple reflections. The evaluation device can be adapted to determine at least one region of interest, e.g., one or more pixels illuminated by the light beam for determining the longitudinal coordinate of the object. For example, the evaluation device can be adapted to perform filtering methods, e.g., speckle analysis and / or edge filtering, and / or object recognition methods.

[0085] The evaluation device may be configured to perform at least one image correction. The image correction may include at least one background subtraction. The evaluation device may be adapted to remove contributions from background light from the reflected beam profile, for example by imaging without further illumination.

[0086] The evaluation device can be configured to determine material information by evaluating a beam profile of an image of the object. As used herein, the term "beam profile of an image" refers to at least one intensity distribution of at least one reflective feature of the image (such as a light spot on a sensor element) as a function of pixels. The beam profile of the image (also denoted as reflected beam profile) can be selected from a trapezoidal beam profile, a triangular beam profile, a conical beam profile, and a linear combination of Gaussian beam profiles. As used herein, the term "evaluating the beam profile" refers to applying at least one material-dependent image filter to the beam profile and / or at least one specific region of the beam profile.

[0087] As used herein, an "image" refers to a two-dimensional function f(x,y) where a brightness and / or color value is given for any x,y position in the image. The positions may be discretized corresponding to the recorded pixels. The brightness and / or color may be discretized corresponding to the bit depth of the optical sensor. As used herein, the term "image filter" refers to at least one mathematical operation applied to a beam profile and / or at least one specific region of a beam profile. Specifically, an image filter φ maps an image f or a region of interest in the image onto real numbers, in, Represents features, particularly material features in the case of material-dependent image filters. The image may be subject to noise and the same applies to the features. Therefore, the features can be random variables. The features can be normally distributed. If the features are not normally distributed, they can be transformed to be normally distributed, such as by a Box-Cox transformation.

[0088] The evaluation device can be configured to determine at least one material characteristic by applying at least one material-dependent image filter Φ to the image As used herein, the term "material-related" image filter refers to an image filter having a material-related output. The output of a material-related image filter is denoted herein as "material feature " or "material-related feature ". The material feature can be or can comprise material information.

[0089] The material-related image filter can be at least one filter selected from a luminance filter; a blob shape filter; a squared norm gradient; a standard deviation; a smoothness filter such as a Gaussian filter or a median filter; a contrast filter based on gray level occurrence; an energy filter based on gray level occurrence; a homogeneity filter based on gray level occurrence; a dissimilarity filter based on gray level occurrence; a law's energy filter; a threshold region filter; or a linear combination thereof; or another material-related image filter φother，φm |≥ 0.40, with one or more of a luminance filter, a blob shape filter, a squared norm gradient, a standard deviation, a smoothness filter, an energy filter based on gray level occurrence, a homogeneity filter based on gray level occurrence, a dissimilarity filter based on gray level occurrence, a law's energy filter, or a threshold region filter, or a linear combination thereof, or other wherein φ m is one of a luminance filter, a blob shape filter, a squared norm gradient, a standard deviation, a smoothness filter, an energy filter based on gray level occurrence, a homogeneity filter based on gray level occurrence, a dissimilarity filter based on gray level occurrence, a law's energy filter, or a threshold region filter, or a linear combination thereof. The other material-related image filter φ other may be related by |p φother,φm |≥ 0.60, preferably by |p φother,φm |≥ 0.80, with one or more of the material-related image filters φ m .

[0090] The similarity of two image filters φ i and φ j may be assessed by the correlation of their features, in particular by computing the Pearson correlation coefficient,

[0091]

[0092] wherein μ and σ are the mean and standard deviation of the obtained features.

[0093] The material-dependent image filter may be at least one arbitrary filter φ that passes hypothesis testing. As used herein, the term "passes hypothesis testing" refers to the fact that the null hypothesis H0 is rejected and the alternative hypothesis H1 is accepted. Hypothesis testing may include testing the material dependence of an image filter by applying the image filter to a predefined data set. The data set may include multiple beam profile images. As used herein, the term "beam profile image" refers to N B The sum of Gaussian radial basis functions,

[0094]

[0095]

[0096] Among them, N B Each of the Gaussian radial basis functions is centered (x lk ,y lk ), pre-factor a lk and the exponential factor α = 1 / ∈ is defined. The exponential factor is the same for all Gaussian functions in all images. The center position x lk ,y lk For all images f k : Each beam profile image in the dataset may correspond to a material classifier and a distance. The material classifier may be a label such as “Material A”, “Material B”, etc. k The (x,y) formula is combined with the following parameter table to generate the beam profile image:

[0097]

[0098] The x,y values ​​are integers corresponding to pixels, where The image can have a pixel size of 32x32. The dataset of beam profile images can be obtained by using the above-mentioned k The formula is combined with the parameter set to obtain f k The value of each pixel in the 32x32 image can be obtained by k (x, y) is obtained by inserting integer values ​​from 0, ..., 31 for x, y. For example, for pixel (6, 9), the value f can be calculated k (6, 9).

[0099] Then, for each image f k , the eigenvalue corresponding to the filter Φ can be calculated Among them, z k is an image f corresponding to a predefined dataset k This produces the corresponding eigenvalues The hypothesis test can use the null hypothesis that the filter does not distinguish between material classifiers. The null hypothesis can be represented by H0: μ1 = μ2 = ... = μ J Given, where μ m is the eigenvalue corresponding to The expected value of each material group. The index m represents the material group. The hypothesis test can be used as an alternative hypothesis that the filter distinguishes between at least two material classifiers. The alternative hypothesis can be represented by H1: m,m′:μ m ≠μ m′ As used herein, the term "does not distinguish between material classifiers" means that the expected values ​​of the material classifiers are the same. As used herein, the term "distinguish between material classifiers" means that at least two expected values ​​of the material classifiers are different. As used herein, "distinguish between at least two material classifiers" is used synonymously with "suitable material classifiers". The hypothesis test may include at least one analysis of variance (ANOVA) on the generated feature values. In particular, the hypothesis test may include determining the mean of the feature values ​​for each J material, i.e., the overall J mean, For m∈[0,1,…,J-1], where N m Gives the number of eigenvalues ​​for each J material in the predefined data set. Hypothesis testing can include determining the average of all N eigenvalues Hypothesis testing may include determining the mean sum of squares within the following:

[0100]

[0101] Hypothesis testing could include the mean sum of squares between,

[0102]

[0103] Hypothesis testing may include performing an F test:

[0104] o Where, d1=NJ, d2=J-1,

[0105] οF(x)=1–CDF(x)

[0106] o p = F (mssb / mssw)

[0107] In this article, I x is the regularized incomplete beta function, The Euler Beta function as well as is an incomplete beta function. If the p-value p is less than or equal to the predefined significance level, the image filter may pass the hypothesis test. If p≤0.075, preferably p≤0.05, more preferably p≤0.025, and most preferably p≤0.01, the filter may pass the hypothesis test. For example, in the case where the predefined significance level is α=0.075, if the p-value is less than α=0.075, the image filter may pass the hypothesis test. In this case, the null hypothesis H0 can be rejected and the alternative hypothesis H1 can be accepted. Therefore, the image filter distinguishes at least two material classifiers. Therefore, the image filter passes the hypothesis test.

[0108] In the following, the image filter is described assuming that the reflectance image comprises at least one reflectance feature, in particular a speckle image. The speckle image f can be represented by the function f:R 2 →R ≥0 Given, where the background of image f may have been subtracted. However, other reflectance features may be possible.

[0109] For example, a material-dependent image filter can be a brightness filter. The brightness filter can return a brightness measure of the spot as a material feature. The material feature can be determined by the following formula:

[0110]

[0111] where f is the spot image. The distance of the spot is represented by z, where z can be obtained, for example, by using defocus ranging or photon ratio ranging techniques and / or by using triangulation techniques. The surface normal of the material is represented by n∈R 3 The vector d is given and can be obtained as the normal to the surface spanned by at least three measurement points. ray ∈R 3 is the direction vector of the light source. Since the position of the spot is known by using defocus ranging or photon ratio ranging techniques and / or by using triangulation techniques, where the position of the light source is known as a parameter of the detector system, d ray is the difference vector between the spot and the light source position.

[0112] For example, a material-dependent image filter may be a filter with an output that depends on the shape of the spots. The material-dependent image filter may return a value related to the translucency of the material as a material characteristic. The translucency of the material affects the shape of the spots. The material characteristic may be given by:

[0113]

[0114] Where 0<α, β<1 is the weight of the spot height h, and H represents the weight side function, that is, H(x)=1:x≥0, H(x)=0:x<0. The spot height h can be determined by the following formula:

[0115]

[0116] Among them, B r is the inner circle of the spot with radius r.

[0117] For example, the material-dependent image filter may be a squared norm gradient. The material-dependent image filter may return a value related to a measure of soft and hard transitions and / or roughness of the speckle as a material feature. The material feature may be defined by the following formula:

[0118]

[0119] For example, a material-dependent image filter can be the standard deviation. The standard deviation of speckle can be determined by:

[0120]

[0121] where μ is the mean value given by μ = ∫(f(x))dx.

[0122] For example, the material-dependent image filter can be a smoothness filter, such as a Gaussian filter or a median filter. In one embodiment of a smoothness filter, the image filter can be based on the observation that bulk scattering exhibits less speckle contrast than diffuse scattering materials. The image filter can quantify the smoothness of the speckles, corresponding to the speckle contrast, as a material characteristic. The material characteristic can be determined by the following equation:

[0123]

[0124] Where F is a smoothness function, such as a median filter or a Gaussian filter. The image filter may include a division by the distance z, as described in the above formula. The distance z may be determined, for example, using defocus ranging or photon ratio ranging techniques and / or by using triangulation techniques. This may allow the filter to be sensitive to distance. In one embodiment of a smoothness filter, the smoothness filter may be based on the standard deviation of the extracted speckle noise pattern. The speckle noise pattern N may be described empirically by the following formula:

[0125] f(x)=f0(x)·(N(X)+1),

[0126] Where f0 is the despeckled image. N(X) is a noise term that models the speckle pattern. The computation of the despeckled image can be difficult. Therefore, the despeckled image can be approximated using a smoothed version of f, i.e., f0 ≈ F(f), where F is a smoothness operator like a Gaussian filter or a median filter. Therefore, an approximation of the speckle pattern can be given by:

[0127]

[0128] The material characteristics of the filter can be determined by the following formula:

[0129]

[0130] Where Var represents the variance function.

[0131] For example, the image filter can be a contrast filter based on grayscale appearance. The material filter can be based on the grayscale appearance matrix M f,ρ (g1g2)=[p g1,g2 ], and p g1,g2 is the occurrence rate of the grayscale combination (g1,g2)=[f(x1,y1),f(x2,y2)], and the relationship ρ defines the distance between (x1,y1) and (x2,y2), which is ρ(x,y)=(x+a,y+b), where a and b are selected from 0,1.

[0132] The material characteristics of the contrast filter based on grayscale appearance can be given by the following formula:

[0133]

[0134] For example, the image filter may be an energy filter based on grayscale occurrences. The material filter is based on the grayscale occurrence matrix defined above.

[0135] The material characteristics of the energy filter based on the grayscale appearance can be given by the following formula:

[0136]

[0137] For example, the image filter may be a homogeneity filter based on grayscale occurrence. The material filter is based on the grayscale occurrence matrix defined above.

[0138] The material characteristics of the homogeneity filter based on grayscale appearance can be given by the following formula:

[0139]

[0140] For example, the image filter may be a dissimilarity filter based on grayscale occurrence. The material filter is based on the grayscale occurrence matrix defined above.

[0141] The material characteristics of the grayscale-based dissimilarity filter can be given by the following formula:

[0142]

[0143] For example, the image filter may be an energy filter of the law. The material filter may be based on the laws vector L5 = [1, 4, 6, 4, 1] and E5 = [-1, -2, 0, -2, -1] and the matrix L5 (E5) T and E5(L5) T .

[0144] Image f k Convolve with these matrices:

[0145]

[0146] as well as

[0147]

[0148]

[0149]

[0150] The material characteristics of the energy filter of the law can be determined by the following formula:

[0151]

[0152] For example, the material-dependent image filter may be a threshold area filter. The material feature may be associated with two regions in the image plane. The first region Ω1 may be a region where the function f is greater than α times the maximum value of f. The second region Ω2 may be a region where the function f is less than α times the maximum value of f but greater than a threshold ε times the maximum value of f. Preferably, α may be 0.5 and ε may be 0.05. Due to speckle or noise, the region may not only correspond to the inner and outer circles around the center of the spot. As an example, Ω1 may include speckle or unconnected areas in the outer circle. The material feature may be determined by the following formula:

[0153]

[0154] Where Ω1 = {x|f(x) > α·max(f(x))} and Ω2 = {x|ε·max(f(x)) <f(x)<α·max(f(x))}。

[0155] Material information m can be obtained by using The evaluation device may be configured to use the material characteristic The material information of the object is determined based on at least one predetermined relationship between the material information of the object and the material information of the object. The predetermined relationship can be one or more of an empirical relationship, a semi-empirical relationship, and an analytically derived relationship. The evaluation device can include at least one data storage device for storing the predetermined relationship, such as a lookup list or table.

[0156] Ideally, the image filter would produce features that depend only on material properties. However, the image filters used in beam profile analysis may produce features that depend on both distance and material properties, such as translucency. At least one of the material-dependent image filters may be a function of distance. The evaluation device may be configured to determine whether the material-dependent image filter used is a function of distance. Specifically, the evaluation device may be configured to determine a correlation coefficient between the material-dependent image filter and the method for determining distance information. In the case where the correlation coefficient between the material-dependent image filter and the method for determining distance information is close to 1 or -1, the distance can be projected by projecting the material features onto the principal axis with minimum variance. As an example, the material features may be projected onto an axis that is orthogonal to the relevant principal component. In other words, the material features may be projected onto a second principal component. This can be done using principal component analysis known to those skilled in the art.

[0157] After determining the longitudinal coordinate z, the To determine the material information, so that the information about the longitudinal coordinate z can be taken into account for the evaluation Specifically, the material information m can be represented by the function The function may be predefined and / or predetermined. For example, the function may be a linear function.

[0158] Additionally or alternatively, the evaluation device can be configured to determine material information by one or more of: comparative image analysis, such as based on comparing the object image with an object library; material property analysis, such as by comparing parameters determined from the image of the object 112 with a database of stored parameters (such as color, translucency, material state, etc.). The evaluation device can include at least one database including the object library and / or stored parameters, such as a list and / or table of possible objects and possible parameters, such as a lookup list or lookup table. The object library can include images of different objects, with which the determined image of the object can be compared. The evaluation device can be configured to determine at least one parameter of the object via image analysis, such as reflectivity, color, translucency, state (such as liquid or solid), roughness, etc.

[0159] The optical sensor used to determine distance information and / or the optical sensor used for spectroscopy and / or the optical sensor used to image an object can be the same. Specifically, the optical sensor used to determine distance information can be used to image an object and / or for spectroscopy, or vice versa. Thus, the optical sensor used to determine distance information and / or for spectroscopy can correspond to or be designed as an optical sensor used to image an object.

[0160] The evaluation device can be configured to perform at least one spectroscopic analysis on the determined intensities, taking into account the determined distance information and material information. The material information can be used to pre-classify the object, specifically before performing the spectroscopic analysis, in particular before performing spectroscopic measurements and / or evaluating the determined spectrum. The spectrometer device can be configured to select at least one analyte of interest based on the material information and to perform spectroscopic measurements on the selected analyte of interest. Additionally or alternatively, the material information can be used as an input parameter for evaluating the determined spectrum, which can accelerate the evaluation.

[0161] The spectrometer device may include at least one display device configured to display material information. Displaying the material information may include any form of presentation, such as graphically displaying the material information. For example, the display device may be configured to display suggestions for materials or products that the object may be. For example, the material information may be "white liquid" or "white translucent liquid," and the display device may display a list of suggestions, such as paint, milk, cream, yogurt, dough, starch, etc.

[0162] The spectrometer device may be configured to select at least one analyte of interest depending on the material information. For example, the evaluation device may include a database storing material information and associated analytes of interest. The display device may provide a list of potential analytes of interest. The spectrometer device may include at least one human-machine interface configured to allow a user to select at least one analyte from the list. The spectrometer device may be configured to perform at least one spectroscopic analysis on the selected analyte of interest. Thus, it may be possible to allow material information of the sample to be provided, in particular before the spectral information is determined, to facilitate the user's application. As an example, the spectrometer device may allow the detection of whether the sample is milk in order to display the fat or lactose content.

[0163] In another aspect, the present invention discloses a method for determining at least one difference in at least one light characteristic of at least one light beam originating from at least one object. In this method, at least one spectrometer device according to the present invention is used, for example, according to one or more embodiments of the spectrometer device disclosed above or disclosed in more detail below. However, other types of spectrometer devices may also be used. The method comprises the following method steps, which may be performed in a given order or in a different order. Furthermore, one or more additional method steps not listed may be present. Furthermore, one, more than one, or even all method steps may be performed repeatedly.

[0164] The method steps are as follows:

[0165] - determining the intensity of a component wavelength signal of at least one light beam propagating from the object to the spectrometer device;

[0166] - determining at least one item of distance information between at least one object and the spectrometer device by using at least one distance detector;

[0167] - determining at least one item of material information of the object by evaluating at least one image of the object determined by at least one pixelated imaging detector using at least one evaluation device;

[0168] - performing at least one spectral analysis on the determined intensities of the component wavelength signals taking into account the determined distance information and material information.

[0169] For details, options and definitions, reference may be made to the spectrometer device described above. Thus, in particular, as described above, the method may comprise using a spectrometer device according to the invention (eg according to one or more embodiments given above or in more detail below).

[0170] In another aspect of the invention, the use of a spectrometer device according to the invention (for example according to one or more embodiments given above or in more detail below) is proposed for a purpose of use selected from the following items: spectroscopy applications; exhaust gas monitoring applications; combustion process monitoring applications; pollution monitoring applications; industrial process monitoring applications; chemical process monitoring applications; food processing process monitoring applications; water quality monitoring applications; air quality monitoring applications; quality control applications; temperature control applications; motion control applications; exhaust gas control applications; gas sensing applications; gas analysis applications; motion sensing applications; chemical sensing applications; mobile applications; medical applications; mobile spectroscopy applications; food analysis applications; agricultural applications, such as properties of soil, silage, feed, crops or agricultural products, monitoring plant health; plastic identification and / or recycling applications.

[0171] In general, in the context of the present invention, the following embodiments are considered to be preferred:

[0172] Embodiment 1: A spectrometer device configured to determine at least one spectral or spectroscopic information of at least one object, wherein the spectrometer device is configured to determine the intensity of component wavelength signals of at least one light beam propagating from the object to the spectrometer device, wherein the spectrometer device includes at least one distance detector, wherein the distance detector is configured to determine at least one distance information about the distance between the at least one object and the spectrometer device, wherein the spectrometer device includes at least one pixelated imaging detector, wherein the at least one pixelated imaging detector is configured to determine at least one image of the object, wherein the spectrometer device includes at least one evaluation device, wherein the evaluation device is configured to determine at least one material information of the object by evaluating at least one image of the object determined by the pixelated imaging detector, wherein the evaluation device is configured to perform at least one spectral analysis on the determined intensity of the component wavelength signals taking into account the determined distance information and the material information.

[0173] Embodiment 2: The spectrometer device according to the preceding embodiment, wherein the spectrometer device is a movable spectrometer device.

[0174] Embodiment 3: The spectrometer device according to any one of the preceding embodiments, wherein the spectrometer device comprises at least one wavelength selection element configured to separate incident light into a spectrum of component wavelength signals, wherein the respective intensities of the component wavelength signals are determined by employing at least one pixelated optical detector comprising a plurality of pixels and / or at least one single-pixel optical detector.

[0175] Embodiment 4: The spectrometer device according to any one of the preceding embodiments, wherein the spectrometer device is configured for contactless spectroscopy.

[0176] Embodiment 5: The spectrometer device according to any one of the preceding embodiments, wherein the spectrometer device is configured such that a distance between the object and the spectrometer device is variable.

[0177] Embodiment 6: The spectrometer device according to the preceding embodiment, wherein the evaluation device is configured to determine a change in the distance between the object and the spectrometer device.

[0178] Embodiment 7: The spectrometer device according to any of the two preceding embodiments, wherein the evaluation device is adapted to determine the light attenuation caused by the distance between the object and the spectrometer device based on the determined distance information.

[0179] Embodiment 8: A spectrometer device according to any of the preceding embodiments, wherein the spectral analysis comprises determining at least one difference in at least one light characteristic caused by the presence of the object, wherein the difference in the light characteristic is selected from: at least one wavelength-dependent intensity difference; at least one wavelength-dependent polarization difference.

[0180] Embodiment 9: The spectrometer device according to the previous embodiment, wherein the distance information is obtained by using one or more of the following techniques: photon ratio ranging, structured light, beam profile analysis, time of flight, shape from motion recovery, focus ranging, triangulation, defocus ranging, stereo sensor.

[0181] Embodiment 10: A spectrometer device according to any one of the preceding embodiments, wherein the distance detector comprises at least one sensor element having a matrix of optical sensors, each of the optical sensors having a photosensitive area, wherein each optical sensor can be configured to generate at least one sensor signal in response to illumination of the photosensitive area by at least one light beam propagating from the at least one object to the spectrometer device, wherein at least one first optical sensor among the optical sensors is adapted to generate a first sensor signal in response to illumination by a first component wavelength, and wherein at least one second optical sensor among the optical sensors is adapted to generate a second sensor signal in response to illumination by the first component wavelength, and wherein the evaluation device is configured to determine at least one longitudinal coordinate z of the object by evaluating a combined signal Q of the first sensor signal and the second sensor signal.

[0182] Embodiment 11: A spectrometer device according to the previous embodiment, wherein the combined signal Q is derived by one or more of the following: forming a quotient of the first signal and the second signal, or vice versa; forming a quotient of multiples of the first signal and multiples of the second signal, or vice versa; forming a quotient of a linear combination of the first signal and a linear combination of the second signal, or vice versa; forming a quotient of a first linear combination of the first signal and the second signal and a second linear combination of the first signal and the second signal.

[0183] Embodiment 12: The spectrometer device according to any of the two preceding embodiments, wherein the evaluation device is configured to determine the longitudinal coordinate z using at least one predetermined relationship between the combined signal Q and the longitudinal coordinate z of the object.

[0184] Embodiment 13: A spectrometer device according to any one of the three preceding embodiments, wherein the evaluation device is configured to determine the at least one optical sensor that is irradiated by the first component wavelength and has the highest sensor signal and to form the first sensor signal, wherein the first sensor signal is at least one center signal, wherein the evaluation device is configured to evaluate the sensor signals of the optical sensors in the matrix that are irradiated by the first wavelength component and to form the second sensor signal, wherein the second sensor signal is at least one sum signal, wherein the evaluation device is configured to determine the combined signal Q by combining the center signal and the sum signal.

[0185] The spectral instrument device according to any one of the preceding five embodiments, wherein the center signal is selected from: the highest sensor signal; an average of a set of sensor signals within a predetermined tolerance range relative to the highest sensor signal; an average of sensor signals from a set of optical sensors comprising the optical sensor with the highest sensor signal and a predetermined set of neighboring optical sensors; a sum of sensor signals from a set of optical sensors comprising the optical sensor with the highest sensor signal and a predetermined set of neighboring optical sensors; a sum of a set of sensor signals within a predetermined tolerance range relative to the highest sensor signal; an average of a set of sensor signals above a predetermined threshold; a sum of a set of sensor signals above a predetermined threshold; an integral of sensor signals from a set of optical sensors comprising the optical sensor with the highest sensor signal and a predetermined set of neighboring optical sensors; an integral of a set of sensor signals within a predetermined tolerance range relative to the highest sensor signal; an integral of a set of sensor signals above a predetermined threshold, wherein the sum signal is selected from: an average of all sensor signals of the matrix; a sum of all sensor signals of the matrix; an integral of all sensor signals of the matrix; an average of all sensor signals of the matrix except for the sensor signals from those optical sensors contributing to the center signal; a sum of all sensor signals of the matrix except for the sensor signals from those optical sensors contributing to the center signal; an integral of all sensor signals of the matrix except for the sensor signals from those optical sensors contributing to the center signal; a sum of sensor signals of optical sensors within a predetermined range relative to the optical sensor with the highest sensor signal; an integral of sensor signals of optical sensors within a predetermined range relative to the optical sensor with the highest sensor signal; a sum of sensor signals of optical sensors above a certain threshold within a predetermined range relative to the optical sensor with the highest sensor signal; an integral of sensor signals of optical sensors above a certain threshold within a predetermined range relative to the optical sensor with the highest sensor signal.

[0186] The spectral instrument device according to any one of the preceding five embodiments, wherein the spectral instrument device is adapted to determine at least one further longitudinal coordinate of the object by evaluating a combined signal Q of first sensor signals and second sensor signals generated in response to a second set of wavelengths, wherein the evaluation device is adapted to determine a combined longitudinal coordinate from the longitudinal coordinate and the further longitudinal coordinate and to perform the spectral analysis taking into account the combined longitudinal coordinate.

[0187] Embodiment 16: A spectrometer device according to any one of the preceding embodiments, wherein the at least one item of material information is at least one characteristic selected from the following: scattering coefficient, translucency, transparency, deviation from Lambertian surface reflection, speckle, material and / or material class; object type and / or object class, etc.

[0188] Embodiment 17: The spectrometer device according to any one of the preceding embodiments, wherein the evaluation device is configured to determine the material information by applying at least one material-related image filter Φ to the image of the object determined by the pixelated imaging detector, wherein the material-related image filter is at least one filter selected from the following: a brightness filter; a speckle shape filter; a square norm gradient; a standard deviation; a smoothness filter, such as a Gaussian filter or a median filter; a contrast filter based on grayscale appearance; an energy filter based on grayscale appearance; a homogeneity filter based on grayscale appearance; a dissimilarity filter based on grayscale appearance; an energy filter based on a law; a threshold area filter; or a linear combination thereof; or by |ρ φother，φm |≥0.40 Another material-related image filter φ associated with one or more of a brightness filter, a speckle shape filter, a squared norm gradient, a standard deviation, a smoothness filter, an energy filter based on grayscale appearance, a homogeneity filter based on grayscale appearance, a dissimilarity filter based on grayscale appearance, an energy filter based on a law, or a threshold area filter, or a linear combination thereof other , where φ m It is one of the brightness filter, speckle shape filter, square norm gradient, standard deviation, smoothness filter, energy filter based on grayscale appearance, homogeneity filter based on grayscale appearance, dissimilarity filter based on grayscale appearance, law energy filter, or threshold area filter, or a linear combination thereof.

[0189] Embodiment 18: A spectrometer device according to the preceding embodiments, wherein the material-dependent image filter is at least one filter that passes a hypothesis test, wherein the hypothesis test uses a null hypothesis that the filter does not distinguish between material classifiers and an alternative hypothesis that the filter distinguishes between at least two material classifiers, wherein the filter passes the hypothesis test if the p-value p is less than or equal to a predefined significance level, wherein p≤0.075, preferably p≤0.05, more preferably p≤0.025, and most preferably p≤0.01.

[0190] Embodiment 19: The spectrometer device according to any one of the preceding embodiments, wherein the evaluation device is configured to determine material information by one or more of the following: comparative image analysis, such as based on a comparison of the image of the object with an object library; material property analysis, such as by comparing parameters determined from the image of the object with a database having stored parameters such as color, translucency, material state, etc.

[0191] Embodiment 20: The spectrometer device according to any one of the preceding embodiments, wherein the spectrometer device comprises at least one display device configured to display material information.

[0192] Embodiment 21: The spectrometer device according to any one of the preceding embodiments, wherein the spectrometer device is configured to select at least one analyte of interest depending on the material information, wherein the spectrometer device is configured to perform at least one spectroscopic analysis on the selected analyte of interest.

[0193] Embodiment 22: The spectrometer device according to any of the preceding embodiments, wherein the spectrometer device comprises at least one illumination source, wherein the illumination source is adapted to illuminate the object with at least one illumination beam.

[0194] Embodiment 23: The spectrometer device according to any one of the preceding embodiments, wherein the illumination source comprises at least one laser source.

[0195] Embodiment 24: The spectrometer device according to any one of the preceding embodiments, wherein the pixelated imaging detector is at least one detector selected from the group consisting of: at least one CCD detector; at least one CMOS detector; at least one InGaAs detector.

[0196] Embodiment 25: A method for determining at least one difference in at least one light characteristic of at least one light beam originating from at least one object, wherein in the method a spectrometer device according to any of the preceding embodiments relating to a spectrometer device is used, the method comprising the following steps:

[0197] - determining the intensity of a component wavelength signal of at least one light beam propagating from the object to the spectrometer device;

[0198] - determining at least one item of distance information between at least one object and the spectrometer device by using at least one distance detector;

[0199] - determining at least one item of material information of the object by evaluating at least one image of the object determined by at least one pixelated imaging detector using at least one evaluation device;

[0200] - performing at least one spectral analysis of the determined intensities of the component wavelength signals taking into account the determined distance information and the material information.

[0201] Example 26: The use of the spectrometer device according to any one of the preceding embodiments involving spectrometer devices, for a purpose selected from the following: infrared detection applications; spectroscopy applications; exhaust monitoring applications; combustion process monitoring applications; pollution monitoring applications; industrial process monitoring applications; chemical process monitoring applications; food processing process monitoring applications; water quality monitoring applications; air quality monitoring applications; quality control applications; temperature control applications; motion control applications; exhaust control applications; gas sensing applications; gas analysis applications; motion sensing applications; chemical sensing applications; mobile applications; medical applications; mobile spectroscopy applications; food analysis applications; agricultural applications, such as characteristics of soil, silage, feed, crops or agricultural products, monitoring plant health; plastic identification and / or recycling applications. BRIEF DESCRIPTION OF THE DRAWINGS

[0202] Further optional details and features of the present invention will become apparent from the following description of preferred exemplary embodiments in conjunction with the dependent claims. In this case, specific features may be implemented separately or in combination with other features. The present invention is not limited to the exemplary embodiments. Exemplary embodiments are schematically illustrated in the accompanying drawings. Identical reference numerals in the various figures designate identical elements or elements having the same function, or elements corresponding to each other in terms of their function.

[0203] Specifically, in the figure:

[0204] Figure 1 An exemplary embodiment of a spectrometer arrangement according to the invention is shown. DETAILED DESCRIPTION

[0205] exist Figure 1 , a schematic diagram of an embodiment of a spectrometer device 110 according to the present invention is shown. Spectrometer device 110 may be adapted to perform at least one spectroscopic analysis comprising determining at least one difference in at least one light characteristic due to the presence of object 112. The difference in the light characteristic may be selected from: at least one wavelength-dependent intensity difference; at least one wavelength-dependent polarization difference.

[0206] The spectrometer device 110 may include a wavelength selection element 114 configured to separate incident light into a spectrum of component wavelength signals, the respective intensities of which are determined using at least one spectral detector including at least one pixelated optical detector 116. For example, the wavelength selection element 114 may be or may include at least one prism. For example, the wavelength selection element 114 may be and / or may include at least one optical filter, such as a variable length filter.

[0207] The pixelated optical detector 116 can include at least one sensor element having a matrix of optical sensors 118. Each optical sensor 118 can have a photosensitive region. Each optical sensor 118 can be configured to generate at least one sensor signal in response to illumination of the photosensitive region by at least one light beam propagating from at least one object 112 to the spectrometer device 110. The detector 116 can include a series of optical sensors 118, which can be arranged in a one-dimensional matrix, preferably in a single row along the length of the variable-length filter, or in a two-dimensional matrix, preferably in more than one row, particularly in two, three, or four parallel rows, in particular to receive as much of the intensity of the incident light as possible. Thus, the number of pixels N in one direction can be higher than the number of pixels M in the other direction, thereby obtaining a one-dimensional 1×N matrix or a rectangular two-dimensional M×N matrix, where M<10 and N≥10, preferably N≥20, and more preferably N≥50. Furthermore, the matrices used herein can also be arranged in a staggered arrangement. Here, each optical sensor 118 used herein may have the same or similar optical sensitivity within a tolerance level, in particular for ease of manufacturing the series of optical sensors 118. However, other kinds of arrangements are also feasible.

[0208] Each of the optical sensors 118 of the pixelated optical detector 116 can be adapted to receive at least a portion of one of the component wavelength signals. Each component wavelength signal is associated with the intensity of each component wavelength. Light that may have passed through the wavelength selective element 114 at a specific spatial location on the wavelength selective element 114 can subsequently be incident on the pixelated optical detector 116. In other words, the pixelated optical detector 118 can preferably be positioned in such a manner that light may first be incident on the wavelength selective element 114, and thereafter only the portion of the light that may have passed through the specific spatial location on the wavelength selective element 114 can be incident on the corresponding spatial location on the pixelated optical detector 116. As a result, the wavelength selective element 114 can be used to separate the incident light into at least one corresponding spatial location by its associated wavelength(s), and a specific optical sensor 118 included in the pixelated optical detector 116 can be used to measure the intensity of the incident light that, due to its specific wavelength, may have been able to pass through the wavelength selective element 114 at the corresponding spatial location and, therefore, be incident on the specific optical sensor 118 provided for determining the intensity of the incident light at the specific wavelength. Therefore, in a particularly preferred embodiment, the detector 116 may include a sequence of optical sensors 118, which may be positioned in the form of a series of optical sensors 118 one after another, wherein the sequence of optical sensors 118 may be placed along the length of the wavelength selective element 114 in a parallel manner relative to the continuous arrangement of the interference filter.

[0209] In particular, to achieve high resolution for the spectrometer arrangement, each optical sensor 118 can therefore be adapted to receive incident light only over a small spatial angle. This arrangement particularly reflects the configuration of the wavelength-selective element 114, which is designed to generate a desired spectrum depending on the spatial position at which the incident light strikes along the length of the wavelength-selective element 114. This particular arrangement can be implemented by a pixelated optical detector 116 comprising a plurality of optical sensors 118, in particular a plurality of pixelated optical sensors 118, wherein each optical sensor 118 is adapted to receive at least a portion of one of the component wavelength signals provided by the variable-length filter. As described above, each component wavelength signal is thus related to the intensity of each component wavelength.

[0210] The pixelated optical sensor 116 can be designed to generate a signal, preferably an electronic signal, which is associated with the intensity of the incident light incident on the individual optical sensor. The signal can be an analog signal and / or a digital signal. Accordingly, the electronic signals of adjacent optical sensors can be generated simultaneously or in a temporally continuous manner. For example, during a line scan or row scan, it is possible to generate a sequence of electronic signals, which correspond to a series of individual pixels arranged in a row. In addition, the individual optical sensors 118 can preferably be active pixel sensors, which can be suitable for amplifying the electronic signal before providing it to an external evaluation unit. To this end, the pixelated optical detector 116 can include one or more signal processing devices, such as one or more filters and / or analog-to-digital converters for processing and / or preprocessing the electronic signal.

[0211] The pixelated optical detector 116 can be selected from any known pixelated sensor, in particular from a pixelated organic camera element, preferably a pixelated organic camera chip, or from a pixelated inorganic camera element, preferably a pixelated inorganic camera chip, more preferably from a CCD chip or CMOS chip commonly used in various cameras today. Alternatively, the pixelated optical detector can be or include a photoconductor, in particular an inorganic photoconductor, especially PbS, PbSe, Ge, InGaAs, extended InGaAs, InSb, or HgCdTe. As a further alternative, it can include a pyroelectric, bolometer, or thermopile detector element. Thus, a camera chip with a matrix of 1×N pixels or M×N pixels, where M<10 and N≥10, preferably N≥20, and more preferably N≥50, can be used. Furthermore, a monochrome camera element, preferably a monochrome camera chip, can be used, where the monochrome camera element can be selected differently for each pixel sensor, in particular depending on the wavelength along the array of optical sensors.

[0212] As another alternative, the pixelated optical detector 116 may be based on a FiP sensor, which is disclosed in, among other documents, WO 2012 / 110924 A1, WO 2014 / 097181 A1 or WO 2016 / 120392 A1. Alternatively, other kinds of pixelated optical detectors are also feasible.

[0213] Thus, the pixelated optical detector 116 can be adapted to provide a plurality of electrical signals that can be generated by the photosensitive areas of the optical sensors included in the pixelated optical detector. The electrical signals provided by the pixelated optical detector 116 of the spectrometer device 110 can then be forwarded to an evaluation device 120. The evaluation device 120 can be configured to determine information related to the spectrum of the object 112 (whose spectrum has been recorded, in particular by using the spectrometer device 110 described herein), wherein the information can be obtained by evaluating the detector signals provided by the pixelated optical detector 116. The information can be provided, for example, electronically, visually, audibly, or in any combination thereof. Furthermore, the information can be stored in a data storage device of the spectrometer device 110 or in a separate storage device and / or can be provided to at least one external device (such as a display device) via at least one interface 122 (such as a wireless interface and / or a wired connection interface).

[0214] The spectrometer device 110 may include at least one condenser device (not shown here) for directing the light beam to the wavelength selective element 114. In addition, the spectrometer device 110 may also include at least one transfer device 124. The light beam 126 emitted from the object 112 may first pass through the transfer device 124 until it subsequently passes through the wavelength selective element 114 and is finally incident on the pixelated optical detector 116. The transfer device 124 may be selected from an optical lens, a curved mirror, a grating, and a diffractive optical element. More particularly, the optical lens may be selected from a biconvex lens, a plano-convex lens, a biconcave lens, a plano-concave lens, an aspheric lens, a cylindrical lens, and a meniscus lens. Therefore, the transfer device 124 may include a material that is at least partially transparent, preferably a material that is at least partially transparent over the entire wavelength range of the wavelength selective element 114 mentioned above. For this purpose, the same or similar optically transparent materials mentioned in this regard may also be used. However, other optical elements are also feasible.

[0215] Light beam 126 emerging from object 112 can originate from the object itself, but can also optionally have a different origin, from which it propagates to object 112 and then to spectrometer device 110. The latter situation is particularly influenced by the at least one illumination source 128 used. Thus, light beam 126 propagating from object 112 to spectrometer device 110 can be light that can be reflected by object 112 and / or a reflective device connected to object 112. Alternatively or additionally, the light can be at least partially transmitted through object 112.

[0216] Spectrometer device 110 may include an illumination source 128. Illumination source 128 may be implemented in various ways. Thus, illumination source 128 may, for example, be part of spectrometer device 110 within housing 130. Alternatively or additionally, however, illumination source 128 may also be located outside housing 130, for example as a separate light source. Illumination source 128 may be located separately from object 112 and illuminate object 112 from a distance. Illumination source 128 may preferably include an illumination source known to provide sufficient emission in the visible spectrum and / or in the infrared (IR) spectrum, particularly in the near-infrared (NIR) spectrum, particularly an incandescent lamp. Alternatively or additionally, illumination source 128 may be selected from at least one of the following illumination sources: a laser, particularly a laser diode, although other types of lasers may also be used; a light-emitting diode; an organic light source, particularly an organic light-emitting diode; a neon lamp; a structured light source; a flame source; or a heat source. Alternatively or additionally, other illumination sources may be used.

[0217] The spectrometer device 110 is configured to determine at least one item of distance information regarding the distance between at least one object 112 and the spectrometer device 110. The spectrometer device 110 may be adapted to determine the distance information and information related to the spectrum of the object 112 simultaneously or subsequently. The spectrometer device 110 may be adapted to determine the distance information (such as a longitudinal coordinate) before, during, and / or after the spectral measurement. The spectrometer device 110 may constitute a coordinate system in which the longitudinal coordinate is a coordinate along the optical axis 132 of the spectrometer device 110. The coordinate system may be a polar coordinate system in which the optical axis 132 of the spectrometer device 110 forms the z-axis, and distances and polar angles from the z-axis may be used as additional coordinates. Directions parallel to or antiparallel to the z-axis may be considered longitudinal directions, and coordinates along the z-axis may be considered longitudinal coordinates or distances. Any direction perpendicular to the z-axis may be considered a transverse direction, and the polar coordinates and / or polar angles may be considered transverse coordinates. The distance between the object 112 and the spectrometer device 110 can be obtained using one or more of the following: photon ratio ranging, structured light, beam profiling, time of flight, shape from motion, focus ranging, triangulation, defocus ranging, stereo sensors. In addition, at least one FiP sensor can be used to obtain distance information, as described in WO 2012 / 110924 A1 or WO 2014 / 097181 A1.

[0218] Figure 1An embodiment is shown in which the spectrometer device 110 includes at least one distance detector 134 configured to determine the distance between the object 112 and the spectrometer device 110. The distance between the object 112 and the spectrometer device 110 can be obtained using one or more of the following techniques: photon ratio ranging, structured light, beam profiling, time of flight, shape from motion, in-focus ranging, triangulation, out-of-focus ranging, and stereo sensors. For example, the distance detector 134 can be configured to determine the distance based on triangulation principles (such as at least one triangulation proximity sensor) and / or based on the time-of-flight (TOF) principle. The spectrometer device 110 can include at least one TOF sensor. The TOF sensor can be adapted to generate at least one sensor signal based on the flight time taken for an illumination beam 136, for example, from the illumination source 128 to propagate to the object 112 and for the beam 126 to propagate from the object 112 to the TOF sensor. The TOF sensor can be selected from: at least one pulsed TOF detector; at least one phase-modulated TOF detector; at least one direct TOF detector; and at least one indirect TOF detector. For example, the pulsed time-of-flight detector can be at least one range-gated imager and / or at least one direct time-of-flight imager. For example, the phase-modulated time-of-flight detector can be at least one RF modulated light source having at least one phase detector. The time-of-flight sensor can be adapted to determine the time delay between the emission of an illumination beam from the illumination source and the receipt of a reflected beam.

[0219] Specifically, Figure 1 An embodiment is shown in which the spectrometer arrangement 110, and in particular the distance detector 134, can be adapted to determine position based on a photon ratio ranging technique. For details of methods and apparatus based on photon ratio ranging techniques, reference is made to international patent applications PCT / EP2017 / 079577, PCT / EP2017 / 079558, PCT / EP2017 / 079564, filed on November 17, 2017, and PCT / EP2018 / 056545, filed on March 15, 2018, the entire contents of which are incorporated herein by reference. Photon ratio ranging is a distance measurement technique that is very flexible in terms of detector technology and, therefore, also in terms of the wavelength of the light source used. Known mobile spectrometer technologies use silicon, InAs, InGaAs, or extended InGaAs detectors, where silicon has a very limited wavelength range, and InAs and InGaAs are both expensive. Lead salt detectors are particularly promising for mobile applications, as novel packaging techniques enable compact sensor designs (see, for example, WO 2018 / 019921 A1). Using photon ratio ranging allows for reliable distance measurements and can be easily implemented in spectrometers with minimal additional effort.

[0220] Distance detector 134 may include at least one sensor element comprising a matrix of optical sensors. Additionally or alternatively, pixelated optical detector 116 may serve as the sensor element. In each case, the optical sensors may each have a photosensitive region. Each optical sensor may be configured to generate at least one sensor signal in response to illumination of the photosensitive region by at least one light beam propagating from at least one object to the spectrometer device. At least one first optical sensor among the optical sensors may be adapted to generate a first sensor signal in response to illumination by a first component wavelength, and at least one second optical sensor among the optical sensors may be adapted to generate a second sensor signal in response to illumination by the first component wavelength. Evaluation device 120 may be configured to determine at least one longitudinal coordinate z of object 112 by evaluating a combined signal Q of the first and second sensor signals. Evaluation device 120 is configured to evaluate the at least one sensor signal generated by the optical sensors of the matrix of optical sensors by performing at least one spectroscopic analysis taking into account the determined longitudinal coordinate z.

[0221] The optical sensor used to determine distance information and the optical sensor used for spectroscopy can be identical and can be implemented as optical sensor 118. Specifically, the optical sensor used to determine distance information can be used as the optical sensor used for spectroscopy, and vice versa. Thus, the optical sensor used to determine distance information can correspond to or be designed as the optical sensor used for spectroscopy, and / or the optical sensor matrix of sensor elements can correspond to or be designed as pixelated optical sensor 116.

[0222] The optical sensors 118 and / or the optical sensors of the distance detector 134 specifically can be or can comprise at least one photodetector, preferably an inorganic photodetector, more preferably an inorganic semiconductor photodetector, most preferably a silicon photodetector. Specifically, the optical sensors can be sensitive in the infrared spectral range. All pixels of the matrix or at least one group of optical sensors of the matrix specifically can be identical. Specifically, a plurality of groups of identical pixels in the matrix can be provided for different spectral ranges, or all pixels have the same spectral sensitivity. Furthermore, the pixels can have the same size and / or electronic or optoelectronic properties. Specifically, the optical sensors 118 can be or can comprise at least one inorganic photodiode which is sensitive in the infrared spectral range, preferably in the range of 700 nm to 3.0 micrometer. Specifically, the optical sensors 118 can be sensitive in a part of the near infrared region, in which part, specifically in the range of 700 nm to 1100 nm, silicon photodiodes can be applied. The infrared optical sensors which can be used for the optical sensors can be commercially available infrared optical sensors, for example the commercially available infrared optical sensors under the trademark name Hertzstueck™ by the company TrinamiX GmbH, D-67056 Ludwigshafen am Rhein, Germany. Thus, as an example, the optical sensors 118 can comprise at least one optical sensor of the intrinsic photovoltaic type, more preferably at least one semiconductor photodiode selected from the group consisting of Ge photodiodes, InGaAs photodiodes, extended InGaAs photodiodes, InAs photodiodes, InSb photodiodes, HgCdTe photodiodes. Additionally or alternatively, the optical sensors can comprise at least one optical sensor of the extrinsic photovoltaic type, more preferably at least one semiconductor photodiode selected from the group consisting of Ge:Au photodiodes, Ge:Hg photodiodes, Ge:Cu photodiodes, Ge:Zn photodiodes, Si:Ga photodiodes, Si:As photodiodes. Additionally or alternatively, the optical sensors can comprise at least one photoconductive sensor, for example a PbS or PbSe sensor, a bolometer, preferably a bolometer selected from the group consisting of VO bolometers and amorphous Si bolometers.

[0223] To determine the longitudinal coordinates of object 112 using photon ratio ranging technology, at least two optical sensors may be employed. To obtain distance information, distance detector 134 may include at least one optical sensor and / or multiple optical sensors. Specifically, for spectroscopy, one optical sensor 118 in combination with a prism or several optical sensors in combination with optical filters may be employed. For example, one of optical sensors 118 used to determine the longitudinal coordinates of an object using photon ratio ranging technology may be used for spectroscopy. For example, pixelated optical detector 116 may be configured for spectroscopy and for determining the longitudinal coordinates of object 112 using photon ratio ranging technology. Therefore, using photon ratio ranging allows for reliable distance measurement and can be easily implemented in a spectrometer with minimal additional effort.

[0224] Each optical sensor of the distance detector 134 and / or each optical sensor 118 of the matrix of optical sensors 118 may be configured to generate at least one sensor signal in response to illumination of a photosensitive area by at least one light beam (in particular, having one component wavelength) propagating from the at least one object 112 to the spectrometer device 110. The distance detector 134 and / or at least one first optical sensor of the optical sensors 118 may be adapted to generate a first sensor signal in response to illumination by the first component wavelength, and the distance detector 134 and / or at least one second optical sensor of the optical sensors 118 may be adapted to generate a second sensor signal in response to illumination by the first component wavelength.

[0225] The evaluation device 120 can be adapted to evaluate the combined signal. At least one longitudinal coordinate of the object 112 is thereby determined. The evaluation can include evaluating a combined signal of the first sensor signal and the second sensor signal. The evaluation device 120 can be configured to derive the combined signal by one or more of: dividing the sensor signals, dividing multiples of the sensor signals, or dividing a linear combination of the sensors. The evaluation device 120 can be configured to determine the longitudinal coordinate using at least one predetermined relationship between the combined signal and the longitudinal coordinate. The predetermined relationship can be one or more of an empirical relationship, a semi-empirical relationship, and an analytically derived relationship. The evaluation device 120 can include at least one data storage device for storing the predetermined relationship, such as a lookup list or a lookup table.

[0226] The combined signal can be determined using various means. For example, software means for deriving the quotient signal and / or hardware means for deriving the quotient signal can be used and implemented in evaluation device 120. Thus, for example, evaluation device 120 can include at least one divider 138, wherein divider 138 is configured to derive the quotient signal. Divider 138 can be embodied in whole or in part as a software divider and / or a hardware divider. Divider 138 can be fully or partially integrated into the sensor element response, or can be embodied in whole or in part as a separate device from the sensor element.

[0227] For example, the combined signal Q is derived as follows:

[0228]

[0229] where x and y are transverse coordinates, A1 and A2 are different regions of at least one beam profile at the location of a sensor element of the distance detector 134, and E(x, y, z o ) indicates that at distance z o The beam profile may be selected from a trapezoidal beam profile, a triangular beam profile, a conical beam profile, and a linear combination of Gaussian beam profiles. Area A1 and area A2 may be different. In particular, A1 and A2 are not identical. Thus, one or more of the shape or content of A1 and A2 may be different. Each sensor signal may include at least one item of information about at least one area of ​​the beam profile. Typically, the beam profile depends on the brightness L(z o ) and beam shape S(x,y;z o ), E(x,y;z o )=L·S. Thus, by deriving the combined signal, the longitudinal coordinate can be determined independently of the brightness. In addition, using the combined signal allows the distance z to be determined independently of the object size. o The combined signal thus allows the distance z to be determined independently of the material properties and / or reflection properties and / or scattering properties of the object 112 to be measured and independently of variations in the light source (such as due to variations in manufacturing accuracy, heat, water, dust, lens damage, etc.). o .

[0230] Each of the first and second sensor signals may include at least one item of information about at least one region of the beam profile. The photosensitive regions may be arranged such that one of the sensor signals includes information about the first region of the beam profile, and the other sensor signal includes information about the second region of the beam profile. The first and second regions of the beam profile may be adjacent or overlapping regions, or both. The first and second regions may not be identical in area. The first region of the beam profile may include substantially edge information about the beam profile, and the second region of the beam profile may include substantially center information about the beam profile. Edge information may include information related to the number of photons in the first region of the beam profile, and center information may include information related to the number of photons in the second region of the beam profile. Evaluation device 120 may be configured to determine and / or select the first and second regions of the beam profile. The beam profile may have a center, i.e., a maximum of the beam profile and / or a center point of the beam profile flat top and / or a geometric center of the light spot, and a descending edge extending from the center. The second region may include an inner region of the cross section, and the first region may include an outer region of the cross section. Preferably, the proportion of edge information contained in the center information is less than 10%, more preferably less than 5%, and most preferably, the center information does not include any edge content. The edge information may include information about the entire beam profile, in particular, information from the center region and the edge region. The proportion of center information in the edge information is less than 10%, preferably less than 5%, and more preferably, the edge information does not include any center information. If at least one region of the beam profile is close to or around the center and includes substantially center information, then the at least one region may be determined and / or selected as the second region of the beam profile. If at least one region of the beam profile includes at least some portion of the descending edge of the cross section, then the at least one region may be determined and / or selected as the first region of the beam profile. For example, the entire region of the cross section may be determined as the first region. The first region of the beam profile may be region A1, and the second region of the beam profile may be region A2.

[0231] Other options for the first area A1 and the second area A2 are also possible. For example, the first area can include a substantially outer area of ​​the beam profile, and the second area can include a substantially inner area of ​​the beam profile. For example, in the case of a two-dimensional beam profile, the beam profile can be divided into a left portion and a right portion, wherein the first area can include an area substantially of the left portion of the beam profile, and the second area can include an area substantially of the right portion of the beam profile.

[0232] The edge information may include information about the number of photons in a first region of the beam profile, and the center information may include information about the number of photons in a second region of the beam profile. The evaluation device 120 may be adapted to determine an area integral of the beam profile. The evaluation device 120 may be adapted to determine the edge information by integrating and / or summing over the first region. The evaluation device may be adapted to determine the center information by integrating and / or summing over the second region. For example, the beam profile may be a trapezoidal beam profile, and the evaluation device may be adapted to determine the integral of the trapezoid. Furthermore, when assuming a trapezoidal beam profile, the determination of the edge and center signals may be replaced by using equivalent evaluations of the characteristics of the trapezoidal beam profile (e.g., determining the slope and position of the edge and the height of the central plateau and deriving the edge and center signals through geometric considerations). Additionally or alternatively, the evaluation device 120 may be adapted to determine one or both of the center and edge information from at least one slice or cut of the light spot. For example, this may be achieved by replacing the area integral in the combined signal with a line integral along the slice or cut. To improve accuracy, several slices or cuts of the light spot may be used and averaged. In case of an elliptical spot profile, averaging several slices or cuts improves the distance information.

[0233] As explained, for example, in WO 2012 / 110924 A1 or WO 2014 / 097181 A1, there is generally a predetermined or determinable relationship between the size of the light spot (e.g., its diameter, beam waist, or equivalent diameter) and the longitudinal coordinate of the object from which the light beam propagates toward the sensor element. Without wishing to be bound by this theory, the light spot can be characterized by two measurement variables: a measurement signal measured in a small measurement region at or near the center of the light spot (also referred to as a center signal), and an integrated signal or sum signal integrated over the light spot with or without the center signal. For a light beam with a certain total power that does not change when the beam is widened or focused, the sum signal should be independent of the spot size of the light spot and, therefore, at least when using a linear optical sensor within its corresponding measurement range, should be independent of the distance between the object 112 and the spectrometer device 110. However, the center signal depends on the light spot size. Therefore, the center signal generally increases when the light beam is focused and decreases when the beam is defocused. Thus, by comparing the center signal with the sum signal, an item of information about the size of the light spot produced by the light beam and, thus, about the longitudinal coordinate of the reflection position can be generated. For example, the comparison of the center signal with the sum signal can be performed by forming a combined signal Q from the center signal and the sum signal and by using a predetermined or determinable relationship between the longitudinal coordinate and the combined signal for deriving the longitudinal coordinate.

[0234] Evaluation device 120 may be adapted to determine which optical sensors of distance detector 134 and / or optical sensor 118 are illuminated by the first wavelength component. Evaluation device 120 may be configured to determine at least one optical sensor 118 illuminated by the first wavelength component and having the highest sensor signal and to form a first sensor signal. The first sensor signal may be at least one center signal. Evaluation device 120 may be configured to evaluate the sensor signals of the optical sensors of distance detector 134 and / or optical sensor 118 illuminated by the first wavelength component and to form a second sensor signal. The second sensor signal may be at least one sum signal. Evaluation device 120 may be configured to determine a combined signal Q by combining the center signal and the sum signal.

[0235] For example, the center signal may be the signal of the pixel having the highest sensor signal among a plurality of sensor signals generated by the optical sensors of the distance detector 134 and / or the optical sensor 118 and / or the pixels of the entire matrix or the pixels of a region of interest within the matrix, wherein the region of interest may be predetermined or determinable within the image generated by the optical sensors 118 and / or the pixels of the matrix. The center signal may be generated from a single optical sensor and / or pixel of the distance detector 134 and / or the optical sensor 118 or from a group of optical sensors of the distance detector 134 and / or the optical sensor 118, wherein in the latter case, by way of example, the sensor signals of the group of optical sensors and / or pixels may be accumulated, integrated, or averaged to determine the center signal. The set of optical sensors and / or pixels of the distance detector 134 and / or optical sensor 118 that generates the center signal can be a set of adjacent optical sensors and / or pixels, such as optical sensors and / or pixels that are less than a predetermined distance from the actual optical sensor and / or pixel with the highest sensor signal, or can be a set of optical sensors and / or pixels that generate sensor signals within a predetermined range from the highest sensor signal. The set of optical sensors and / or pixels of the distance detector 134 and / or optical sensor 118 that generates the center signal can be selected to be as large as possible to allow for a maximum dynamic range. The evaluation device 120 can be adapted to determine the center signal by integrating a plurality of sensor signals (e.g., a plurality of optical sensors and / or pixels located around the optical sensor 118 and / or pixel with the highest sensor signal).

[0236] As described above, the center signal can generally be a single sensor signal, such as a sensor signal from an optical sensor and / or pixel at the center of the light spot, or can be a combination of multiple sensor signals, such as a combination of sensor signals from optical sensors and / or pixels at the center of the light spot, or a secondary sensor signal derived by processing sensor signals derived according to one or more of the above possibilities. Determination of the center signal can be performed electronically, as the comparison of sensor signals can be relatively simple to implement using conventional electronics, or can be performed entirely or partially by software. Specifically, the center signal can be selected from: the highest sensor signal; the average value of a group of sensor signals within a predetermined tolerance range relative to the highest sensor signal; the average value of sensor signals from a group of optical sensors and / or pixels including the optical sensor and / or pixel with the highest sensor signal and a predetermined group of adjacent optical sensors and / or pixels; the sum of sensor signals from a group of optical sensors and / or pixels including the optical sensor and / or pixel with the highest sensor signal and a predetermined group of adjacent optical sensors and / or pixels; the sum of a group of sensor signals within a predetermined tolerance range relative to the highest sensor signal; the average value of a group of sensor signals above a predetermined threshold; the sum of a group of sensor signals above a predetermined threshold; the integral of sensor signals from a group of optical sensors including the optical sensor with the highest sensor signal and a predetermined group of adjacent optical sensors; the integral of a group of sensor signals within a predetermined tolerance range relative to the highest sensor signal; the integral of a group of sensor signals above a predetermined threshold.

[0237] For example, the sum signal can be derived by accumulating, integrating, or averaging the sensor signals for the entire matrix or a region of interest within the matrix, where the region of interest can be predetermined or determinable within the image generated by the distance detector 134 and / or the optical sensors of the optical sensors 118 in the matrix. When accumulating, integrating, or averaging the sensor signals, the actual optical sensors generating the sensor signals can be omitted from the accumulation, integration, or averaging, or they can be included in the accumulation, integration, or averaging. The evaluation device 120 can be adapted to determine the sum signal by integrating the signals for the entire matrix or a region of interest within the matrix. For example, the beam profile can be a trapezoidal beam profile, and the evaluation device can be adapted to determine the integral of the entire trapezoid. Furthermore, when assuming a trapezoidal beam profile, the determination of the edge and center signals can be replaced by using equivalent evaluations of the characteristics of the trapezoidal beam profile, such as determining the slope and position of the edges and the height of the central plateau, and deriving the edge and center signals through geometric considerations.

[0238] Similarly, the center signal and edge signal can also be determined by using segments of the beam profile, such as circular segments of the beam profile. For example, the beam profile can be divided into two segments via a secant or chord that does not pass through the center of the beam profile. Thus, one segment contains substantially edge information, while the other segment contains substantially center information. For example, to further reduce the amount of edge information in the center signal, the edge signal can be further subtracted from the center signal.

[0239] The combined signal Q may be a signal generated by combining the center signal and the sum signal. Specifically, the determination may include one or more of the following: forming a quotient of the center signal and the sum signal, or vice versa; forming a quotient of multiples of the center signal and multiples of the sum signal, or vice versa; forming a quotient of a linear combination of the center signal and a linear combination of the sum signal, or vice versa. Additionally or alternatively, the combined signal Q may include any signal or signal combination that contains at least one item of information regarding the comparison between the center signal and the sum signal.

[0240] The spectrometer device 110 can be configured to determine at least one spectrum or spectroscopic information of at least one object 112. The spectrometer device 110 includes at least one evaluation device 120 configured to perform at least one spectral analysis while taking into account the determined distance information. The evaluation device 120 can be configured to perform at least one spectral analysis while taking into account the determined longitudinal coordinate z. During the spectral analysis, the at least one spectrum or spectroscopic information of the object can be determined. Specifically, the evaluation device 120 can be configured to determine light attenuation caused by the distance between the object and the spectrometer device 110. The spectral analysis can include determining at least one difference in at least one light characteristic caused by the presence of the object 112. The difference in the light characteristic can be selected from: at least one wavelength-dependent intensity difference; and at least one wavelength-dependent polarization difference. The evaluation device 120 can be adapted to perform the spectral analysis while taking into account light attenuation. The evaluation device 120 can be adapted to correct the spectral intensity of the component wavelength signals determined by the optical detector. Specifically, the evaluation device 120 can be adapted to correct the determined intensity values ​​for light attenuation, for example, by multiplying and / or dividing the determined intensity values ​​by at least one correction function. The correction function can be determined empirically, semi-empirically, and / or analytically. For example, the spectrometer device can be configured to determine light attenuation by measuring a background spectrum that depends on the optics, the light source, light source properties, dust, and the like. The spectrometer device 110 can be configured to derive a correction function, such as a background correction function, from this. However, the distance between the object and the spectrometer can remain fixed during the background spectrum measurement. The spectrometer device can be a movable spectrometer device. Specifically, the distance between the object 112 and the spectrometer device 110 can be variable. The evaluation device 120 can be configured to determine changes in the distance between the object 112 and the spectrometer device 110. Therefore, the intensity values ​​and / or background spectrum must be further corrected for effects caused by the distance between the object and the spectrometer device, as well as changes in the distance. The evaluation device 120 can be adapted to correct the determined light attenuation for effects caused by the distance between the object and the spectrometer. To correct for light attenuation caused by distance, a further correction function, such as a polynomial correction function, for example a quadratic or higher-order polynomial, can be used. For example, the distance-dependent light attenuation can be corrected by a fraction of a polynomial in z, such as a polynomial in z of at most third degree divided by a polynomial in z of at most fifth degree, and the coefficients can be used to adjust the distance-dependent light attenuation function. For example, the correction function can be a rational polynomial function. For example, the polynomial A·1 / z can be used. 2, where A is a coefficient or constant and z is the longitudinal coordinate z. Further correction functions can be determined taking into account the luminous properties of the illumination source. Furthermore, further correction functions can be determined by taking into account predetermined reflective properties of the object (e.g., using a spot profile and / or assumed reflective properties of the object). Furthermore, the correction function can be a combined correction function that corrects for light attenuation caused by optics, ambient light, dust, and temperature, while also correcting for distance-dependent light attenuation. As an example, the combined correction function can be the product of a distance-independent correction function (such as a background correction function) and a distance-dependent correction function.

[0241] The spectrometer device 110 can be adapted to determine at least one further longitudinal coordinate of the object by evaluating a combined signal Q of the first sensor signal and the second sensor signal generated in response to the second component wavelength. The evaluation device 120 can be adapted to determine a combined longitudinal coordinate, such as an average value, from the longitudinal coordinate and the further longitudinal coordinate and to perform a spectral analysis taking into account the combined longitudinal coordinate.

[0242] The spectrometer device 110 can be configured for contactless spectroscopy. Determining distance information and using it for correction of spectral measurements allows for a variable distance between the object and the spectrometer device. Direct mechanical contact or the use of special sample holders can be avoided.

[0243] The evaluation device 120 is configured to determine material information of the object 112 by evaluating at least one image of the object 112 determined by at least one imaging detector 121, wherein Figure 1 In the embodiment of the present invention, one or both of the pixelated optical detector 116 and the distance detector 134 can be used as the imaging detector 121. For example, the material information can be at least one property selected from the group consisting of: scattering coefficient, translucency, transparency, deviation of reflection from a Lambertian surface, speckle, material and / or material class; object type and / or object class, etc. The material information can include information about material properties. For example, the material property can be a property selected from the group consisting of: roughness, light penetration depth into the material, properties characterizing the material as a biological or non-biological material, reflectivity, specular reflectivity, diffuse reflectivity, surface properties, amount of translucency, scattering, particularly backscattering behavior, etc. The at least one material property can be a property selected from the group consisting of: scattering coefficient, translucency, transparency, deviation of reflection from a Lambertian surface, speckle, etc.

[0244] The evaluation device 120 may include at least one database 140 including lists and / or tables of predefined and / or predetermined material information, such as a lookup list or table. The list and / or table of material information may be determined and / or generated by performing at least one test measurement using a spectrometer according to the present invention, for example, by performing a material test using a sample with known material properties. The list and / or table of material information may be determined and / or generated at the manufacturer's site and / or by a user of the spectrometer device. Material information may also be assigned to a material classifier, such as one or more of the following: material name; material group, such as biological or non-biological material, translucent or non-translucent material, metal or non-metal, skin or non-skin, fur or non-fur, carpet or non-carpet, reflective or non-reflective, specular or non-specular, foam or non-foam, hair or non-hair; roughness group, etc. The evaluation device 120 may include at least one database 142 including lists and / or tables including material information and associated material names and / or material groups.

[0245] Object 112 may include one or more articles and / or one or more parts of articles, wherein at least one article or at least one part thereof may include at least one component that can provide a spectrum suitable for investigation. Additionally or alternatively, object 112 may be or include one or more living beings and / or one or more parts thereof, such as one or more body parts or body fluids of a human (e.g., a user) or an animal. For example, object 112 may be at least one object selected from the group consisting of: a scene, a human (e.g., a person), wood, carpet, foam, an animal (e.g., a cow), a plant, a piece of tissue, metal, a toy, a metal object, a beverage, a food (e.g., fruit, meat, fish), a dish, cosmetics, applied cosmetics, cloth, fur, hair, a skincare product, a cream, oil, powder, carpet, juice, a suspension, paint, a plant, a body, a body part, an organic material, an inorganic material, a reflective material, a screen, a display, a wall, a sheet of paper, such as a photograph. Object 112 may include at least one surface onto which illumination is projected. The surface may be adapted to at least partially reflect the illumination toward the spectrometer device. For example, without wishing to be bound by theory, human skin may have a reflectance profile, also denoted as a backscattering profile, which includes a portion resulting from back reflection from the surface, denoted as surface reflection, and a portion resulting from diffuse reflection of light that penetrates the skin, denoted as the diffuse portion of back reflection. For information on the reflectance profile of human skin, see "Lasertechnik in der Medizin: Grundlagen, Systeme, Anwendungen," "Wirkung von Laserstrahlung auf Gewebe," 1991, pp. 171-266, Jürgen Eichler, Theo Seiler, Springer-Verlag, ISBN 0939-0979. The surface reflectance of skin may increase with increasing wavelength toward the near infrared. Furthermore, the penetration depth may increase with increasing wavelength from the visible to the near infrared. The diffuse portion of back reflection may increase with the penetration depth of light. These material properties can be used to distinguish skin from other materials, specifically by analyzing the backscattering profile.

[0246] Specifically, the spectrometer device 110 can be configured to detect biological tissue, in particular human skin. The spectrometer device 110 can be a device for detecting (in particular, optically detecting) biological tissue, in particular human skin. Detecting biological tissue can include determining and / or verifying whether a surface to be inspected or tested is or includes biological tissue, in particular human skin, and / or distinguishing biological tissue, in particular human skin, from other tissue (in particular, other surfaces), and / or distinguishing different types of biological tissue, such as different types of human tissue, such as muscle, fat, organs, etc. For example, the biological tissue can be or include human tissue or a portion thereof, such as skin, hair, muscle, fat, organs, etc. For example, the biological tissue can be or include animal tissue or a portion thereof, such as skin, fur, muscle, fat, organs, etc. For example, the biological tissue can be or include plant tissue or a portion thereof. The spectrometer device 110 can be adapted to distinguish animal tissue or a portion thereof from one or more of inorganic tissue, metal surfaces, and plastic surfaces (e.g., agricultural machinery or milking machines). The spectrometer device 110 may be adapted to distinguish plant tissue or parts thereof from one or more of inorganic tissue, metal surfaces, and plastic surfaces, such as those of agricultural machinery. The spectrometer device 110 may be adapted to distinguish food and / or beverages from plates and / or glasses. The spectrometer device 110 may be adapted to distinguish different types of food, such as fruit, meat, and fish. The spectrometer device 110 may be adapted to distinguish cosmetics and / or applied cosmetics from human skin. The spectrometer device 110 may be adapted to distinguish human skin from foam, paper, wood, displays, and screens. The spectrometer device 110 may be adapted to distinguish human skin from clothing. The spectrometer device 110 may be adapted to distinguish maintenance products from machine component materials, such as metal components, etc. The spectrometer device 110 may be adapted to distinguish organic materials from inorganic materials. The spectrometer device 110 may be adapted to distinguish human biological tissue from the surfaces of artificial or inanimate objects. The spectrometer device 110 may be particularly useful for non-therapeutic and non-diagnostic applications.

[0247] The evaluation device 120 may be configured to determine material information by applying at least one material-dependent image filter φ to the image of the object 112 determined by the imaging detector 121. Specifically, the evaluation device may be configured to determine at least one material characteristic φ by applying the material-dependent image filter φ to the image of the object 112 determined by the pixelated optical detector 116 and / or the distance detector 134. m .

[0248] The material dependent image filter can be at least one filter selected from a luminance filter; a blob shape filter; a squared norm gradient; a standard deviation; a smoothness filter such as a Gaussian filter or a median filter; a contrast filter based on gray level occurrence; an energy filter based on gray level occurrence; a homogeneity filter based on gray level occurrence; a dissimilarity filter based on gray level occurrence; a law's energy filter; a threshold region filter; or a linear combination thereof, or another material dependent image filter φ φother，φm |≥0.40 with one or more of a luminance filter, a blob shape filter, a squared norm gradient, a standard deviation, a smoothness filter, an energy filter based on gray level occurrence, a homogeneity filter based on gray level occurrence, a dissimilarity filter based on gray level occurrence, a law's energy filter, or a threshold region filter, or a linear combination thereof. other where φ m is one of a luminance filter, a blob shape filter, a squared norm gradient, a standard deviation, a smoothness filter, an energy filter based on gray level occurrence, a homogeneity filter based on gray level occurrence, a dissimilarity filter based on gray level occurrence, a law's energy filter, or a threshold region filter, or a linear combination thereof.

[0249] The pixelated optical detector 116 can be configured to record a beam profile of at least one reflective feature of an image of the object 112. The evaluation device 120 can be configured to identify and / or select at least one reflective feature, specifically at least one light spot, in the image provided by the pixelated optical detector 116. The evaluation device can be configured to perform at least one image analysis and / or image processing to identify the reflective feature. The image analysis and / or image processing can use at least one feature detection algorithm. Image analysis and / or image processing may include one or more of the following: filtering; selection of at least one region of interest; forming a difference image between an image created by the sensor signal and at least one offset; inverting the sensor signal by inverting the image created by the sensor signal; forming a difference image between images created by the sensor signal at different times; background correction; decomposition into color channels; decomposition into hue, saturation and brightness channels; frequency decomposition; singular value decomposition; applying a blob detector; applying a corner detector; applying a Hessian determinant filter; applying a region detector based on the curvature principle; applying a maximally stable extreme region detector; applying a generalized Hough transform; applying a ridge detector; applying an affine invariant feature detector; applying an affine adaptive interest point operator; applying a Harris affine region ... finite bounding box; applying a Hessian bounding box; applying a finite bounding box; applying a finite bounding box; applying a finite bounding box; applying a finite bounding box; applying a finite bounding box; applying a finite bounding box; applying a finite bounding box; applying a finite bounding box; applying a finite bounding box; applying a finite bounding box The image is evaluated by applying a region detector, applying a scale-invariant feature transform, applying a scale-space extrema detector, applying a local feature detector, applying an accelerated robust feature algorithm, applying a gradient localization and orientation histogram algorithm, applying a histogram of orientation gradient descriptors, applying a Deriche edge detector, applying a differential edge detector, applying a spatiotemporal interest point detector, applying a Moravec corner detector, applying a Canny edge detector, applying a Laplacian-Gaussian filter, applying a difference-of-Gaussian filter, applying a Sobel operator, applying a Laplacian operator, applying a Scharr operator, applying a Prewitt operator, applying a Roberts operator, applying a Kirsch operator, applying a high-pass filter, applying a low-pass filter, applying a Fourier transform, applying a Radon transform, applying a Hough transform, applying a wavelet transform, thresholding, and creating a binary image. Specifically, the image is evaluated by selecting a region of interest in the image. The region of interest can be manually determined by a user or automatically, such as by identifying an object within the image generated by a sensor element. For example, in the case of a light spot-like reflection feature, the region of interest can be selected as the area surrounding the light spot outline.

[0250] For example, the illumination source 128 can be adapted to generate and / or project a point cloud such that a plurality of illumination regions are generated on the matrix of the distance detector 134 and / or the optical sensor 118 (e.g., a CMOS detector). Furthermore, interference may be present on the distance detector 134 and / or the matrix of the optical sensor, such as interference caused by speckle and / or external light and / or multiple reflections. The evaluation device 120 can be adapted to determine at least one region of interest, e.g., one or more pixels illuminated by the light beam for determining the longitudinal coordinate of the object. For example, the evaluation device 120 can be adapted to perform filtering methods, e.g., speckle analysis and / or edge filtering, and / or object recognition methods.

[0251] The evaluation device 120 may be configured to perform at least one image correction. The image correction may include at least one background subtraction. The evaluation device may be adapted to remove the influence of background light from the reflected beam profile, for example by imaging without further illumination.

[0252] The evaluation device 112 can be configured to determine material information by evaluating a beam profile of an image of the object. The beam profile of the image (also denoted as a reflected beam profile) can be selected from a trapezoidal beam profile, a triangular beam profile, a conical beam profile, and a linear combination of Gaussian beam profiles.

[0253] The image may be a two-dimensional function f(x,y), where a brightness and / or color value is given for any x,y position in the image. The positions may be discretized corresponding to the recorded pixels. The brightness and / or color may be discretized corresponding to the bit depth of the optical sensor. An image filter may be at least one mathematical operation applied to the beam profile and / or at least one specific region of the beam profile. Specifically, the image filter φ maps the image f or a region of interest in the image onto real numbers, in, Represents features, particularly material features in the case of material-dependent image filters. The image may be subject to noise and the same applies to the features. Therefore, the features can be random variables. The features can be normally distributed. If the features are not normally distributed, they can be transformed to be normally distributed, such as by a Box-Cox transformation.

[0254] The evaluation device may be configured to determine at least one material characteristic by applying at least one material-dependent image filter φ to the image The material-dependent image filter may be at least one filter selected from the following: a brightness filter; a speckle shape filter; a square norm gradient; a standard deviation; a smoothness filter, such as a Gaussian filter or a median filter; a contrast filter based on grayscale appearance; an energy filter based on grayscale appearance; a homogeneity filter based on grayscale appearance; a dissimilarity filter based on grayscale appearance; an energy filter based on a law; a threshold area filter; or a linear combination thereof; or by |ρ φother，φm |≥0.40 Another material-related image filter φ associated with one or more of a brightness filter, a speckle shape filter, a squared norm gradient, a standard deviation, a smoothness filter, an energy filter based on grayscale appearance, a homogeneity filter based on grayscale appearance, a dissimilarity filter based on grayscale appearance, an energy filter of a law, or a threshold area filter, or a linear combination thereof other , where φ m is one of a brightness filter, a speckle shape filter, a square norm gradient, a standard deviation, a smoothness filter, an energy filter based on grayscale appearance, a homogeneity filter based on grayscale appearance, a dissimilarity filter based on grayscale appearance, an energy filter based on a law, or a threshold area filter, or a linear combination thereof. Another material-related image filter φ other You can use |ρ φother,φm |≥0.60, preferably by |ρ φother,φm |≥0.80 Material-related image filter φ m One or more of the are related.

[0255] The material-dependent image filter may be at least one arbitrary filter φ that passes hypothesis testing. As used herein, the term "passes hypothesis testing" refers to the fact that the null hypothesis H0 is rejected and the alternative hypothesis H1 is accepted. Hypothesis testing may include testing the material dependence of an image filter by applying the image filter to a predefined data set. The data set may include multiple beam profile images. As used herein, the term "beam profile image" refers to N B The sum of Gaussian radial basis functions,

[0256]

[0257]

[0258] Among them, N B Each of the Gaussian radial basis functions is centered (x lk ,y lk ), pre-factor a lk and the exponential factor α = 1 / ∈ is defined. The exponential factor is the same for all Gaussian functions in all images. The center position x lk ,y lk For all images fk : Each beam profile image in the dataset may correspond to a material classifier and a distance. The material classifier may be a label such as “Material A”, “Material B”, etc. k The (x,y) formula is combined with the following parameter table to generate the beam profile image:

[0259]

[0260] The x,y values ​​are integers corresponding to pixels, where The image can have a pixel size of 32x32. The dataset of beam profile images can be obtained by using the above-mentioned k The formula is combined with the parameter set to obtain f k Continuous description. For each pixel in the 32x32 image, the value can be obtained by k (x,y) is obtained by interpolating integer values ​​from 0,...,31 for x,y. For example, for pixel (6,9), the value f can be calculated k (6,9).

[0261] Then, for each image f k , the eigenvalue corresponding to the filter Φ can be calculated Among them, z k is an image f corresponding to a predefined dataset k This produces the corresponding eigenvalues The hypothesis test can use the null hypothesis that the filter does not distinguish between material classifiers. The null hypothesis can be represented by H0: μ1 = μ2 = ... = μ J Given, where μ m is the eigenvalue corresponding to The expected value of each material group is m. The index m represents the material group. The hypothesis test can be used as an alternative hypothesis that the filter distinguishes between at least two material classifiers. The alternative hypothesis can be represented by H1: m,m′:μ m ≠μ m′ As used herein, the term "does not distinguish between material classifiers" means that the expected values ​​of the material classifiers are the same. As used herein, the term "distinguish between material classifiers" means that at least two expected values ​​of the material classifiers are different. As used herein, "distinguish between at least two material classifiers" is used synonymously with "suitable material classifiers". The hypothesis test may include at least one analysis of variance (ANOVA) on the generated feature values. In particular, the hypothesis test may include determining the mean of the feature values ​​for each J material, i.e., the overall J mean, For m∈[0,1,…,J-1], where N m Gives the number of eigenvalues ​​for each J material in the predefined data set. Hypothesis testing can include determining the average of all N eigenvalues Hypothesis testing may include determining the mean sum of squares within the following:

[0262]

[0263] Hypothesis testing could include the mean sum of squares between,

[0264]

[0265] Hypothesis testing may include performing an F test:

[0266] o Where, d1=NJ, d2=J-1,

[0267] οF(x)=1–CDF(x)

[0268] o p = F (mssb / mssw)

[0269] In this article, I x is the regularized incomplete beta function, The Euler Beta function as well as is an incomplete beta function. If the p-value p is less than or equal to the predefined significance level, the image filter may pass the hypothesis test. If p≤0.075, preferably p≤0.05, more preferably p≤0.025, and most preferably p≤0.01, the filter may pass the hypothesis test. For example, in the case where the predefined significance level is α=0.075, if the p-value is less than α=0.075, the image filter may pass the hypothesis test. In this case, the null hypothesis H0 can be rejected and the alternative hypothesis H1 can be accepted. Therefore, the image filter distinguishes at least two material classifiers. Therefore, the image filter passes the hypothesis test.

[0270] In the following, the image filter is described assuming that the reflectance image comprises at least one reflectance feature, in particular a speckle image. The speckle image f can be represented by the function f:R 2 →R ≥0 Given, where the background of image f may have been subtracted. However, other reflectance features may be possible.

[0271] For example, a material-dependent image filter can be a brightness filter. The brightness filter can return a brightness measure of the spot as a material feature. The material feature can be determined by the following formula:

[0272]

[0273] where f is the spot image. The distance of the spot is represented by z, where z can be obtained, for example, by using defocus ranging or photon ratio ranging techniques and / or by using triangulation techniques. The surface normal of the material is represented by n∈R 3 The vector d is given and can be obtained as the normal to the surface spanned by at least three measurement points. ray ∈R 3 is the direction vector of the light source. Since the position of the spot is known by using defocus ranging or photon ratio ranging techniques and / or by using triangulation techniques, where the position of the light source is known as a parameter of the detector system, d ray is the difference vector between the spot and the light source position.

[0274] For example, a material-dependent image filter may be a filter with an output that depends on the shape of the spots. The material-dependent image filter may return a value related to the translucency of the material as a material characteristic. The translucency of the material affects the shape of the spots. The material characteristic may be given by:

[0275]

[0276] Where 0<α, β<1 is the weight of the spot height h, and H represents the weight function, that is, H(x)=1:x≥0, H(x)=0:x<0. The spot height h can be determined by the following formula:

[0277]

[0278] Among them, B r is the inner circle of the spot with radius r.

[0279] For example, the material-dependent image filter may be a squared norm gradient. The material-dependent image filter may return a value related to a measure of soft and hard transitions and / or roughness of the speckle as a material feature. The material feature may be defined by the following formula:

[0280]

[0281] For example, a material-dependent image filter can be the standard deviation. The standard deviation of speckle can be determined by:

[0282]

[0283] where μ is the mean value given by μ = ∫(f(x))dx.

[0284] For example, the material-dependent image filter can be a smoothness filter, such as a Gaussian filter or a median filter. In one embodiment of a smoothness filter, the image filter can be based on the observation that bulk scattering exhibits less speckle contrast than diffuse scattering materials. The image filter can quantify the smoothness of the speckles, corresponding to the speckle contrast, as a material characteristic. The material characteristic can be determined by the following equation:

[0285]

[0286] Where F is a smoothness function, such as a median filter or a Gaussian filter. The image filter may include a division by the distance z, as described in the above formula. The distance z may be determined, for example, using defocus ranging or photon ratio ranging techniques and / or by using triangulation techniques. This may allow the filter to be sensitive to distance. In one embodiment of a smoothness filter, the smoothness filter may be based on the standard deviation of the extracted speckle noise pattern. The speckle noise pattern N may be described empirically by the following formula:

[0287] f(x)=f0(x)·(N(X)+1),

[0288] Where f0 is the despeckled image. N(X) is a noise term that models the speckle pattern. The computation of the despeckled image can be difficult. Therefore, the despeckled image can be approximated using a smoothed version of f, i.e., f0 ≈ F(f), where F is a smoothness operator like a Gaussian filter or a median filter. Therefore, an approximation of the speckle pattern can be given by:

[0289]

[0290] The material characteristics of the filter can be determined by the following formula:

[0291]

[0292] Where Var represents the variance function.

[0293] For example, the image filter can be a contrast filter based on grayscale appearance. The material filter can be based on the grayscale appearance matrix M f,ρ (g1g2)=[p g1,g2 ], and p g1,g2 is the occurrence rate of the grayscale combination (g1,g2)=[f(x1,y1),f(x2,y2)], and the relationship ρ defines the distance between (x1,y1) and (x2,y2), which is ρ(x,y)=(x+a,y+b), where a and b are selected from 0,1.

[0294] The material characteristics of the contrast filter based on grayscale appearance can be given by the following formula:

[0295]

[0296] For example, the image filter may be an energy filter based on grayscale occurrences. The material filter is based on the grayscale occurrence matrix defined above.

[0297] The material characteristics of the energy filter based on the grayscale appearance can be given by the following formula:

[0298]

[0299] For example, the image filter may be a homogeneity filter based on grayscale occurrence.The material filter is based on the grayscale occurrence matrix defined above.

[0300] The material characteristics of the homogeneity filter based on grayscale appearance can be given by the following formula:

[0301]

[0302] For example, the image filter may be a dissimilarity filter based on grayscale occurrence. The material filter is based on the grayscale occurrence matrix defined above.

[0303] The material characteristics of the dissimilarity filter based on grayscale appearance can be given by the following formula:

[0304]

[0305] For example, the image filter can be an energy filter of the law. The material filter can be based on the law vectors L5 = [1, 4, 6, 4, 1] and E5 = [-1, -2, 0, -2, -1] and the matrix L5 (E5) T and E5(L5) T .

[0306] Image f k Convolve with these matrices:

[0307]

[0308] as well as

[0309]

[0310]

[0311]

[0312] The material characteristics of the energy filter of the law can be determined by the following formula:

[0313]

[0314] For example, the material-dependent image filter may be a threshold area filter. The material feature may be associated with two regions in the image plane. The first region Ω1 may be a region where the function f is greater than α times the maximum value of f. The second region Ω2 may be a region where the function f is less than α times the maximum value of f but greater than a threshold ε times the maximum value of f. Preferably, α may be 0.5 and ε may be 0.05. Due to speckle or noise, the region may not only correspond to the inner and outer circles around the center of the spot. As an example, Ω1 may include speckle or unconnected areas in the outer circle. The material feature may be determined by the following formula:

[0315]

[0316] Where Ω1 = {x|f(x) > α·max(f(x))} and Ω2 = {x|ε·max(f(x)) <f(x)<α·max(f(x))}。

[0317] Material information m can be obtained by using The evaluation device may be configured to use the material characteristic The material information of the object is determined based on at least one predetermined relationship between the material information of the object and the material information of the object. The predetermined relationship can be one or more of an empirical relationship, a semi-empirical relationship, and an analytically derived relationship. The evaluation device can include at least one data storage device for storing the predetermined relationship, such as a lookup list or table.

[0318] Ideally, the image filter would produce features that depend only on material properties. However, the image filters used in beam profile analysis may produce features that depend on both distance and material properties, such as translucency. At least one of the material-dependent image filters may be a function of distance. The evaluation device may be configured to determine whether the material-dependent image filter used is a function of distance. Specifically, the evaluation device may be configured to determine a correlation coefficient between the material-dependent image filter and the method for determining distance information. In the case where the correlation coefficient between the material-dependent image filter and the method for determining distance information is close to 1 or -1, the distance can be projected by projecting the material features onto the principal axis with minimum variance. As an example, the material features may be projected onto an axis that is orthogonal to the relevant principal component. In other words, the material features may be projected onto a second principal component. This can be done using principal component analysis known to those skilled in the art.

[0319] After determining the longitudinal coordinate z, the To determine the material information, so that the information about the longitudinal coordinate z can be taken into account for the evaluation Specifically, the material information m can be represented by the function is determined. The function can be predefined and / or predetermined. For example, the function can be a linear function.

[0320] Subsequently, for each image f k , the eigenvalue of the filter is calculated, where z k is the distance value corresponding to the image f k from the predefined data set. This results in a data set with corresponding generated eigenvalues . The hypothesis test can use a null hypothesis that the filter does not distinguish between the material classifiers. The null hypothesis can be given by H0: μ1= μ2=... = μ J , where μ m is the expected value for each material group corresponding to the eigenvalue . The index m denotes the material group. The hypothesis test can be used as an alternative hypothesis that the filter distinguishes between at least two material classifiers. The alternative hypothesis can be given by H1: m,m': μ m ≠ μ m′ . As used herein, the term "does not distinguish between the material classifiers" means that the expected values of the material classifiers are the same. As used herein, the term "distinguishes between the material classifiers" means that at least two of the expected values of the material classifiers are different. As used herein, "distinguishes between at least two material classifiers" is used synonymously with "suitable material classifier." The hypothesis test can include at least one analysis of variance (ANOVA) on the generated eigenvalues. In particular, the hypothesis test can include determining the mean of the eigenvalues for each of the J materials, i.e., the total J mean, for m e [0, 1,..., J - 1], where N m gives the number of eigenvalues for each of the J materials in the predefined data set. The hypothesis test can include determining the mean of all N eigenvalues The hypothesis test can include determining the mean square sum within

[0321]

[0322] The hypothesis test can include the mean square sum between

[0323]

[0324] The hypothesis test can include performing an F-test:

[0325] o where d1 = N - J, d2 = J - 1,

[0326] o F(x) = 1 - CDF(x)

[0327] o p = F(mssb / mssw)

[0328] In this context, I x is the regularized incomplete beta function, where the Euler beta function and is the incomplete beta function. The image filter can pass the hypothesis test if the p-value p is less than or equal to a predefined significance level. The filter can pass the hypothesis test if p < 0.075, preferably p < 0.05, more preferably p < 0.025, and most preferably p < 0.01. For example, in case the predefined significance level is a = 0.075, the image filter can pass the hypothesis test if the p-value is less than a = 0.075. In this case, the null hypothesis H0 can be rejected and the alternative hypothesis H1 can be accepted. Thus, the image filter distinguishes between at least two material classifiers. Thus, the image filter passes the hypothesis test.

[0329] In the following, the image filter is described under the assumption that the reflection image comprises at least one reflection feature, in particular a speckle image. The speckle image f can be given by the function f: R 2 → R ≥0 wherein a background of the image f can have been subtracted. However, other reflection features can be possible.

[0330] For example, the material-related image filter can be a brightness filter. The brightness filter can return a brightness measure of the speckle as the material feature. The material feature can be determined by

[0331]

[0332] wherein f is the speckle image. The distance of the speckle is denoted by z, wherein z can be obtained, for example, by using a range from defocus or a range from photon ratio technique and / or by using a triangulation technique. The surface normal of the material is given by n e R 3 and can be obtained as the normal of the surface spanned by at least three measurement points. The vector d ray e R 3 is the direction vector of the light source. Since the position of the speckle is known by using a range from defocus or a range from photon ratio technique and / or by using a triangulation technique, wherein the position of the light source is known as a parameter of the detector system, d ray is the difference vector between the speckle and the light source position.

[0333] For example, the material-related image filter can be a filter with an output depending on the shape of the speckle. This material-related image filter can return a value related to the translucency of the material as the material feature. The translucency of the material influences the shape of the speckle. The material feature can be given by

[0334]

[0335] Where 0<α, β<1 is the weight of the spot height h, and H represents the weight function, that is, H(x)=1:x≥0, H(x)=0:x<0. The spot height h can be determined by the following formula:

[0336]

[0337] Among them, B r is the inner circle of the spot with radius r.

[0338] For example, the material-dependent image filter may be a squared norm gradient. The material-dependent image filter may return a value related to a measure of soft and hard transitions and / or roughness of the speckle as a material feature. The material feature may be defined by the following formula:

[0339]

[0340] For example, a material-dependent image filter can be the standard deviation. The standard deviation of speckle can be determined by:

[0341]

[0342] where μ is the mean value given by μ = ∫(f(x))dx.

[0343] For example, the material-dependent image filter can be a smoothness filter, such as a Gaussian filter or a median filter. In one embodiment of a smoothness filter, the image filter can be based on the observation that bulk scattering exhibits less speckle contrast than diffuse scattering materials. The image filter can quantify the smoothness of the speckles, corresponding to the speckle contrast, as a material characteristic. The material characteristic can be determined by the following equation:

[0344]

[0345] Where F is a smoothness function, such as a median filter or a Gaussian filter. The image filter may include a division by the distance z, as described in the above formula. The distance z may be determined, for example, using defocus ranging or photon ratio ranging techniques and / or by using triangulation techniques. This may allow the filter to be sensitive to distance. In one embodiment of a smoothness filter, the smoothness filter may be based on the standard deviation of the extracted speckle noise pattern. The speckle noise pattern N may be described empirically by the following formula:

[0346] f(x)=f0(x)·(N(X)+1),

[0347] where f0is the decorrelated speckle image. N(X) is a noise term modeling the speckle pattern. The computation of the decorrelated speckle image can be difficult. Therefore, the decorrelated speckle image can be approximated with a smoothed version of f, i.e., f0~ F(f), where F is a smoothing operator like a Gaussian filter or a median filter. Thus, the approximation of the speckle pattern can be given by:

[0348]

[0349] The material characteristic of this filter can be determined by:

[0350]

[0351] where Var denotes the variance function.

[0352] For example, the image filter can be a contrast filter based on the gray level occurrence. The material filter can be based on the gray level occurrence matrix M f,ρ (g1g2) = [p g1,g2 ], while p g1,g2 is the occurrence rate of the gray level combination (g1, g2) = [f(x1, y1), f(x2, y2)], and the relation p defines the distance between (x1, y1) and (x2, y2), which is p(x, y) = (x + a, y + b) with a and b chosen from 0, 1.

[0353] The material characteristic of the contrast filter based on the gray level occurrence can be given by:

[0354]

[0355] For example, the image filter can be an energy filter based on the gray level occurrence. The material filter is based on the gray level occurrence matrix defined above.

[0356] The material characteristic of the energy filter based on the gray level occurrence can be given by:

[0357]

[0358] For example, the image filter can be a homogeneity filter based on the gray level occurrence. The material filter is based on the gray level occurrence matrix defined above.

[0359] The material characteristic of the homogeneity filter based on the gray level occurrence can be given by:

[0360]

[0361] For example, the image filter can be a dissimilarity filter based on the gray level occurrence. The material filter is based on the gray level occurrence matrix defined above.

[0362] The material characteristics of the dissimilarity filter based on grayscale appearance can be given by the following formula:

[0363]

[0364] For example, the image filter can be an energy filter of the law. The material filter can be based on the law vectors L5 = [1, 4, 6, 4, 1] and E5 = [-1, -2, 0, -2, -1] and the matrix L5 (E5) T and E5(L5) T .

[0365] Image f k Convolve with these matrices:

[0366]

[0367] as well as

[0368]

[0369]

[0370]

[0371] The material characteristics of the energy filter of the law can be determined by the following formula:

[0372]

[0373] For example, the material-dependent image filter may be a threshold area filter. The material feature may be associated with two regions in the image plane. The first region Ω1 may be a region where the function f is greater than α times the maximum value of f. The second region Ω2 may be a region where the function f is less than α times the maximum value of f but greater than a threshold ε times the maximum value of f. Preferably, α may be 0.5 and ε may be 0.05. Due to speckle or noise, the region may not only correspond to the inner and outer circles around the center of the spot. As an example, Ω1 may include speckle or unconnected areas in the outer circle. The material feature may be determined by the following formula:

[0374]

[0375] Where Ω1 = {x|f(x) > α·max(f(x))} and Ω2 = {x|ε·max(f(x)) <f(x)<α·max(f(x))}。

[0376] Material information m can be obtained by using The evaluation device 120 may be configured to use the material characteristics The material information of the object is determined in dependence of at least one predetermined relationship to the material information of the object. The predetermined relationship can be one or more of an empirical relationship, a semi-empirical relationship and an analytically derived relationship. The evaluation device 120 can comprise at least one data storage device for storing the predetermined relationship, e.g. a look-up list or a look-up table.

[0377] In an ideal case, the image filter would yield a feature that depends only on the material property. However, the image filter used in the beam profile analysis can yield a feature that depends on the distance and the material property, such as the translucency. At least one of the material dependent image filters can be a function of the distance. The evaluation device 120 can be configured to determine whether the material dependent image filter used is a function of the distance. In particular, the evaluation device 120 can be configured to determine the correlation coefficient of the material dependent image filter and the method used for determining the distance information. In case the correlation coefficient of the material dependent image filter and the method used for determining the distance information is close to 1 or -1, the distance can be projected by projecting the material feature onto the principal axis with the least variance. As an example, the material feature can be projected onto an axis that is orthogonal to the relevant principal component. In other words, the material feature can be projected onto the second principal component. This can be done using a principal component analysis known to the person skilled in the art.

[0378] After determining the longitudinal coordinate z, the material information can be determined by subsequently evaluating such that information about the longitudinal coordinate z can be considered for evaluating In particular, the material information m can be determined by a function The function can be predefined and / or predetermined. For example, the function can be a linear function.

[0379] Additionally or alternatively, the evaluation device 120 can be configured to determine the material information by one or more of: a comparison image analysis, such as a comparison of the image of the object 112 with a library of objects; a material property analysis, such as by comparing parameters determined from the image of the object 112 with a database of stored parameters, such as color, translucency, state of matter, etc. The evaluation device 120 can comprise at least one database, such as the database 140, comprising a library of objects and / or stored parameters, such as a list and / or table of possible objects and possible parameters, such as a look-up list or a look-up table. The library of objects can comprise images of different objects, the determined image of the object can be compared with these images of different objects. The evaluation device 120 can be configured to determine at least one parameter of the object, such as reflectivity, color, translucency, state, such as liquid or solid, roughness, etc., via image analysis.

[0380] The evaluation device 120 can be configured to perform at least one spectral analysis on the determined intensities taking into account the determined distance information and material information. The material information can be used for pre-classifying the object, in particular before performing the spectral analysis, in particular before performing the spectral measurement and / or evaluating the determined spectrum. The spectrometer device 110 can be configured to select at least one analyte of interest depending on the material information and can perform a spectral measurement on the selected analyte of interest. Additionally or alternatively, the material information can be used as an input parameter for evaluating the determined spectrum, which can allow for speeding up the evaluation.

[0381] The spectrometer device 110 can comprise at least one display device 142 configured to display the material information. The display of the material information can comprise any form of presentation, such as displaying the material information in a graphical manner.

[0382] For example, further, the display device 142 can be configured to display a suggestion for what kind of material or product the object 112 can be. As an example, the material information can be "white liquid" or "white translucent liquid" and the display device can display a list of suggestions, such as paint, milk, cream, yogurt, dough, starch, etc.

[0383] The spectrometer device can be configured to select at least one analyte of interest depending on the material information. For example, the evaluation device 120 can comprise a database storing material information and associated analytes of interest. The display device 142 can provide a list of potential analytes of interest. The spectrometer device 110 can comprise at least one human-machine interface configured to allow a user to select at least one analyte of the list. The spectrometer device can be configured to perform at least one spectral analysis on the selected analyte of interest. Thus, it can be possible to allow providing material information of the sample, in particular before determining the spectral information, to facilitate the application for the user. As an example, the spectrometer device 110 can allow detecting whether the sample is milk to display the fat or lactose content.

[0384] Reference numeral list

[0385] 110 spectrometer device

[0386] 112 object

[0387] 114 wavelength selection element

[0388] 116 pixelated optical detector

[0389] 118 optical sensor

[0390] 120 evaluation device

[0391] 121 imaging detector

[0392] 122 interfaces

[0393] 124 Transfer Device

[0394] 126 Beam

[0395] 128 Irradiation Source

[0396] 130 housing

[0397] 132 optical axis

[0398] 134 Distance Detector

[0399] 136 irradiation beam

[0400] 138 Divider

[0401] 140 Database

[0402] 142 Display device

Claims

1. A spectrometer device (110) configured to determine at least one spectrum or spectroscopic information of at least one object (112), wherein: The spectrometer device (110) is configured to determine the intensity of component wavelength signals of at least one light beam propagating from the object (112) to the spectrometer device (110), wherein the spectrometer device (110) comprises at least one distance detector (134), wherein the distance detector (134) is configured to determine at least one item of distance information about the distance between the at least one object (112) and the spectrometer device (110), wherein the spectrometer device (110) comprises at least one pixelated imaging detector (121), wherein the at least one pixelated imaging detector (121) is configured to determine at least one image of the object (112), wherein the spectrometer device (110) comprises at least one An evaluation device (120), wherein the evaluation device (120) is configured to determine at least one material information of the object (112) by evaluating at least one image of the object (112) determined by the pixelated imaging detector (121), wherein the evaluation device (120) is configured to determine the material information by applying at least one material-dependent image filter φ to the image of the object (112) determined by the pixelated imaging detector (121), wherein the material-dependent image filter is an image filter having a material-dependent output, wherein the material-dependent image filter comprises a brightness filter, wherein the brightness filter returns a brightness measure of a spot as a material characteristic, wherein the material characteristic φm is determined by the following formula: Where f is the speckle image, x represents the pixel of the pixelated imaging detector, n is the surface normal of the material, z is the distance of the spot, the position of the light source is a parameter of the spectrometer device, and d ray is the difference vector between the spot and the light source position, and where the spot image f is given by the function f:R 2 →R ≥0 Given, The evaluation device (120) is configured to perform at least one spectral analysis on the determined intensities of the component wavelength signals taking into account the determined distance information and the material information, and the evaluation device (120) is further configured to use the material information to pre-classify the object before performing spectral measurement and / or evaluating the determined spectrum.

2. The spectrometer device (110) according to the preceding claim, wherein The spectrometer device (110) is a movable spectrometer device.

3. The spectrometer device (110) according to any one of the preceding claims, wherein The spectrometer device (110) comprises at least one wavelength selective element (114) configured to separate incident light into a spectrum of component wavelength signals, wherein respective intensities of the component wavelength signals are determined by employing at least one pixelated optical detector (116) comprising a plurality of pixels and / or at least one single pixel optical detector.

4. The spectrometer device (110) according to any one of the preceding claims, wherein The spectral analysis includes determining at least one difference in at least one light characteristic due to the presence of the object (112), wherein the difference in the light characteristic is selected from: at least one wavelength-dependent intensity difference; at least one wavelength-dependent polarization difference.

5. The spectrometer device (110) according to the preceding claim, wherein The distance information is obtained by using one or more of the following techniques: photon ratio ranging, structured light, beam profiling, time of flight, shape from motion, on-focus ranging, triangulation, through-focus ranging, stereo sensors.

6. The spectrometer device (110) according to any one of the preceding claims, wherein The distance detector (134) comprises at least one sensor element of a matrix of optical sensors, each of which has a photosensitive area, wherein each optical sensor is configured to generate at least one sensor signal in response to illumination of the photosensitive area by at least one light beam propagating from the object (112) to the spectrometer device (110), wherein at least one first optical sensor of the optical sensors is adapted to generate a first sensor signal in response to illumination by a first component wavelength, and wherein at least one second optical sensor of the optical sensors is adapted to generate a second sensor signal in response to illumination by the first component wavelength, wherein the evaluation device (120) is configured to determine at least one longitudinal coordinate z of the object (112) by evaluating a combined signal Q of the first sensor signal and the second sensor signal.

7. The spectrometer device (110) according to the preceding claim, wherein The combined signal Q is derived by one or more of: forming a quotient of the first signal and the second signal, or vice versa; forming a quotient of a multiple of the first signal and a multiple of the second signal, or vice versa; forming a quotient of a linear combination of the first signal and a linear combination of the second signal, or vice versa; forming a quotient of a first linear combination of the first signal and the second signal and a second linear combination of the first signal and the second signal, wherein the evaluation device (120) is configured to determine the longitudinal coordinate z using at least one predetermined relationship between the combined signal Q and the longitudinal coordinate z of the object (112).

8. The spectrometer device (110) according to any one of the preceding claims, wherein The at least one item of material information is at least one property selected from the group consisting of: scattering coefficient, translucency, transparency, deviation of reflection from Lambertian surfaces, speckle, material and / or material class; object type and / or object class, and the like.

9. The spectrometer device (110) according to any one of the preceding claims, wherein The material-related image filter is at least one filter that passes a hypothesis test, wherein the hypothesis test uses a null hypothesis that the filter does not distinguish between material classifiers and an alternative hypothesis that the filter distinguishes between at least two material classifiers, wherein the filter passes the hypothesis test if the p-value is less than or equal to a predefined significance level, wherein p≤0.

075.

10. The spectrometer device (110) according to claim 9, wherein p≤0.05。 11. The spectrometer device (110) according to claim 9, wherein: p≤0.025。 12. The spectrometer device (110) according to claim 9, wherein p≤0.01。 13. The spectrometer device (110) according to any one of the preceding claims, wherein The evaluation device (120) is configured to determine the material information by one or more of: comparative image analysis, such as based on comparison of an image of the object (112) with an object library; material property analysis, such as by comparing parameters determined from an image of the object (112) with a database of stored parameters such as color, translucency, material state, etc.

14. The spectrometer device (110) according to any one of the preceding claims, wherein The spectrometer device (110) includes at least one display device (142) configured to display the material information.

15. The spectrometer device (110) according to any one of the preceding claims, wherein The spectrometer device (110) is configured to select at least one analyte of interest depending on the material information, wherein the spectrometer device (110) is configured to perform at least one spectroscopic analysis on the selected analyte of interest.

16. The spectrometer device (110) according to any one of the preceding claims, wherein The pixelated imaging detector (121) is at least one detector selected from the group consisting of: at least one CCD detector; at least one CMOS detector; at least one InGaAs detector.

17. A method for determining at least one difference in at least one light characteristic of at least one light beam originating from at least one object (112), wherein In the method, a spectrometer device (110) according to any one of the preceding claims relating to a spectrometer device is used, the method comprising the following steps: - determining the intensity of a component wavelength signal of at least one light beam propagating from the object (112) to the spectrometer device (110); - determining at least one item of distance information between at least one object (112) and the spectrometer device (110) by using at least one distance detector (134); - determining at least one item of material information of the object (112) by evaluating at least one image of the object (112) determined by at least one pixelated imaging detector (121) of the spectrometer device (110) using at least one evaluation device (120), wherein the material information is determined by applying at least one material-dependent image filter φ to the image of the object (112) determined by the pixelated imaging detector (121), wherein the material-dependent image filter is an image filter having a material-dependent output, wherein the material-dependent image filter comprises a brightness filter, wherein the brightness filter returns a brightness measure of a spot as a material feature, wherein the material feature φ m Determined by the following formula: Where f is the speckle image, x represents the pixel of the pixelated imaging detector, n is the surface normal of the material, z is the distance of the spot, the position of the light source is a parameter of the spectrometer device, and d ray is the difference vector between the spot and the light source position; - performing at least one spectral analysis on the determined intensities of the component wavelength signals taking into account the determined distance information and the material information, the evaluation device (120) being configured to use the material information to pre-classify the object before performing a spectral measurement and / or evaluating the determined spectrum.

18. Use of the spectrometer device (110) according to any of the preceding claims relating to spectrometer devices, for a purpose selected from the group consisting of: infrared detection applications; spectroscopy applications; exhaust gas monitoring applications; combustion process monitoring applications; pollution monitoring applications; industrial process monitoring applications; food processing process monitoring applications; water quality monitoring applications; air quality monitoring applications; quality control applications; temperature control applications; exhaust gas control applications; gas sensing applications; gas analysis applications; motion sensing applications; chemical sensing applications; mobile applications; medical applications; food analysis applications; agricultural applications, for example; plastics marking and / or recycling applications.

19. Use of the spectrometer device (110) according to claim 18 for a purpose of use selected from the group consisting of: mobile spectroscopy applications.

20. Use of the spectrometer device (110) according to claim 18, for a purpose selected from the group consisting of: motion control applications.

21. Use of the spectrometer device (110) according to claim 18, for a purpose selected from the group consisting of: chemical process monitoring applications.

22. Use of the spectrometer device (110) according to claim 18 for a purpose selected from the group consisting of: properties of soil, silage, feed, crops or agricultural products.

23. Use of the spectrometer device (110) according to claim 18, for a purpose selected from the group consisting of: monitoring plant health.

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