Spectroscopic device

Abstract

The invention is in the field of spectroscopic devices. The invention relates to computer-implemented method for determining chemometric data of an object comprising: a) receiving spectroscopic data associated with a spectroscopic measurement on the object, object data associated with a characteristic of the object and environmental data associated with a characteristic of the surrounding of the object, b) determining chemometric data of the object using the spectroscopic data, the object data and the environmental data, c) outputting the chemometric data of the object.

Description

[0001] Spectroscopic Device

[0002] The invention is in the field of spectroscopic devices. The invention relates to a computer-implemented method for determining chemometric data of an object, the use of the chemometric data of the object obtained from the method of any of the previous claims for fitness, health, nutrition, agricultural or recycling applications, a spectroscopic device for determining chemometric data of an object, a system for generating a chemometric model for an infrared spectrometer and a non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform the method.

[0003] Background

[0004] Spectroscopy is a powerful tool for analyzing objects, for example its chemical composition. Traditionally, spectroscopy has been employed in laboratories with benchtop devices. Recently, portable spectrometers have become commercially available. Miniaturized spectrometers integrated into portable devices like smartphones will become available soon. Such miniaturized spectrometers do not yet reach the performance of benchtop devices due to severe space restrictions. In addition, portable spectrometers are exposed to environments which disturb the spectroscopic measurement and hence may decrease accuracy and precision of the measured result. Furthermore, less experienced users operate the spectrometer in comparison to laboratory spectrometers which are operated by professionals.

[0005] US 11 448 594 discloses a method to determine quality and authenticity of agricultural commodities based on nearinfrared spectrometers. Environmental parameters as well as product properties are used to iteratively improve the chemometric model. However, this data is not used to improve the measurement result upon inference of the chemometric model, but only for its training.

[0006] US 2014 / 155760) discloses the use of quantitative spectroscopy for measuring water in humans by incorporating additional information. However, no specific instructions of how to combine the additional information is given.

[0007] US 2019 / 223780 discloses a wearable device comprising a plurality of sensors. However, no specific instructions of how to combine the sensor data is given.

[0008] US 2017 / 160131 discloses a method to determine an object attribute with a spectrometer and receiving additional data. However, no specific instructions of how to use the additional data is given.

[0009] US 2018 / 172510 discloses a system for analyzing food in a kitchen appliance comprising a sensor configured to generate a non-spectroscopic signal. However, no specific instructions of how to use the non-spectroscopic signal is given.

[0010] US 2018 / 120155) discloses a method to determine an object attribute with a spectrometer comprising a sensor configured to generate a non-spectroscopic signal. However, no specific instructions of how to use the signal is given. Summary

[0011] In one aspect the invention relates to a computer-implemented method for determining chemometric data of an object comprising: a) receiving spectroscopic data associated with a spectroscopic measurement on the object, object data associated with a characteristic of the object and environmental data associated with a characteristic of the surrounding of the object, b) determining chemometric data of the object using the spectroscopic data, the object data and the environmental data, c) outputting the chemometric data of the object.

[0012] In another aspect the invention relates to the use of the chemometric data of the object obtained from the method of any of the previous claims for fitness, health, nutrition, agricultural or recycling applications.

[0013] In another aspect the invention relates to a spectroscopic device for determining chemometric data of an object comprising: a) a spectroscopy module for measuring an object and thereby generating spectroscopic data, b) an input for receiving object data associated with a characteristic of the object and environmental data associated with a characteristic having an influence on the measurement, c) a processor for determining chemometric data of the object using the spectroscopic data, the object data and the environmental data, and d) an output for outputting the chemometric data of the object.

[0014] In another aspect the invention relates to a non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising: a) receiving spectroscopic data associated with a spectroscopic measurement on the object, object data associated with a characteristic of the object and environmental data associated with a characteristic having an influence on the measurement, b) determining chemometric data of the object using the spectroscopic data, the object data and the environmental data, c) outputting the chemometric data of the object.

[0015] By making use of both object data and environmental data for determining the chemometric data from the spectroscopic data, chemometric data with higher accuracy and / or precision can be achieved. This enables the use of miniaturized spectrometers which yield spectra of lower precision and lower resolution. In particular, for a spectrometer integrated into a portable device, the object data and environmental data may be easily available, for example because the portable device is equipped with respective sensors anyway, so no sophisticated hardware adjustments are required. The invention further relieves requirements for calibration of the spectrometer as minor spectrometer drifts can be computationally compensated using object data and environmental data. Furthermore, the required amount of training data for the chemometric model can be decreased making it easier to develop applications with limited availability of training data. The term "spectroscopic device” may refer to an apparatus which is capable of recording spectroscopic data of an object. The spectroscopic device may be a spectrometer or a device into which a spectrometer is integrated. The spectroscopic device may be portable or stationary, for example a laboratory device. A portable spectroscopic device may be a hand-held spectrometer or a module which is integrated into a portable device like a smartphone, a tablet or a wearable like a smartwatch. A portable spectroscopic device may be communicatively coupled to a computer device, for example a cloud computer or a smartphone. Such computer device may be configured to execute a chemometric model. The computer device may further be configured to receive spectroscopic data from the spectroscopic device. The computer device may store such spectroscopic data, or send it to a system for determining chemometric data. The computer system may further be configured to use the chemometric data to derive instructions for action, for example to drink water if the skin was found to contain a low water content.

[0016] A spectroscopic device may comprise:

[0017] - an optical element configured for separating incident optical radiation provided by the measurement object into a spectrum of constituent wavelength components;

[0018] - a photosensor comprising at least one photosensitive region configured for receiving the optical radiation from the optical element, wherein the photosensor is configured for generating at least one photosensor signal dependent on an illumination of the photosensitive region by the optical radiation;

[0019] - a processor to process the photosensor signals into a spectrum.

[0020] The term "optical element” may refer to an arbitrary element configured for influencing optical radiation. The optical element may be configured for at least one of at least partially dispersing the optical radiation, at least partially filtering the optical radiation, at least partially reflecting the optical radiation, e.g. diffusely or directly, at least partially deflecting the optical radiation, at least partially transmitting the optical radiation and at least partially absorbing the optical radiation. The optical element may comprise at least one of a prism, a grating, a beam splitter, or an interferometer, for example a Michelson interferometer. The optical element may be configured for being used in mobile applications, for example for being used in handheld spectrometer devices and / or in spectrometer devices comprised by electronic communication devices, such as a smartphone or a tablet. As another example, the optical element may comprise at least one optical filter element. The optical filter element may be configured for filtering the optical radiation or more specifically at least one selected spectral range of the optical radiation. The optical filter element may specifically be positioned in a light path before the photosensor. As an example, the portable spectrometer may comprise a plurality of a photosensors, for example 5 to 20, such as 8 to 12. The photosensors may be arranged as pixels in an array or in a matrix. The portable spectrometer may comprise a plurality of optical filter elements. An optical filter element may be positioned in a beam path before a photosensor. The optical filter elements may be transmissive at different wavelengths of different wavelength regions. For example, each photosensor may be positioned behind an optical filter with regard to the beam path, wherein each optical filter is transmissive at different wavelength or different wavelength region to the other optical filters.

[0021] The spectroscopic device may comprise one or more than one photosensor. The photosensor may comprise at least one photosensitive region. The photosensitive region may be configured for receiving the optical radiation from the optical element. The photosensor may be configured for generating at least one photosensor signal dependent on an illumination of the photosensitive region by the optical radiation. The term "sensor” may refer to a device configured for detecting at least one condition or for measuring at least one measurement variable. The sensor may be capable of generating at least one signal, such as a measurement signal, which is a qualitative or quantitative indication of the measurement variable and / or measurement property, e.g. of an illumination of the sensor or a part of the sensor. The signal may be or comprise an electrical signal, such as a current, specifically a photocurrent. The term "photosensor” may refer to a sensor or a detector configured for detecting or measuring optical radiation, such as for detecting an illumination and / or a light spot generated by at least one light beam, e.g. by using the photoelectric effect. The photodetector may comprise at least one substrate. As an example, a single photosensor may be a substrate with at least one single photosensitive region, which generates a physical response, e.g. an electronic response, to the illumination for a given wavelength range.

[0022] The term "photosensitive region” may refer to a unit of the photosensor, specifically to a spatial area or volume being part of the photosensor, configured for being illuminated, or in other words for receiving optical radiation, and for generating at least one signal, such as an electronic signal, in response to the illumination. The photosensitive region may be located on a surface of the photosensor. The photosensitive region may specifically be a single, closed, uniform photosensitive region. However, other options may also be feasible.

[0023] The spectroscopic device may comprise at least one light emitting element configured for emitting illumination light for illuminating the object in order to generate detection light from the object. The light emitting element may be an incandescent lamp, for example a tungsten filament lamp or a tungsten halogen lamp, a light-emitting diode (LED), a laser diode, a gas-discharge lamp, for example a xenon lamp, a mercury vapor lamp, or a deuterium lamp.

[0024] The term "light” may refer to electromagnetic radiation in one or more of the infrared, the visible and the ultraviolet spectral range. The term "ultraviolet spectral range” may refer to electromagnetic radiation having a wavelength of 1 nm to 380 nm, preferably of 100 nm to 380 nm, for example 280 nm to 315 nm (UV-B) or 315 nm to 380 nm (UV-A). Further, in partial accordance with standard ISO-21348 in a valid version at the date of this document, the term "visible spectral range” may refer to a spectral range of 380 nm to 760 nm. The term "infrared spectral range” (IR) may refer to electromagnetic radiation of 760 nm to 1000 m, wherein the range of 760 nm to 1.5 pm is usually denominated as "near infrared spectral range” (NIR) while the range from 1.5 p to 15 pm is denoted as "mid infrared spectral range” (MidlR) and the range from 15 pm to 1000 pm as "far infrared spectral range” (FIR). Preferably, light used for the typical purposes of the present invention is light in the infrared (IR) spectral range, more preferred, in the near infrared (NIR) and / or the mid infrared spectral range (MidlR), especially the light having a wavelength of 780 nm to 3 pm, for example 780 nm to 1.4 pm or 1.4 pm to 3 pm. These wavelength regions are particularly suitable for obtaining material properties of an object.

[0025] The spectroscopic device may comprise a processor to process the photosensor signals into spectroscopic data, for example an infrared spectrum. The term "processor” may refer to a device configured to processing, manipulating, or transforming data according to a set of instructions. The processor may be capable of executing software, algorithms, or computational tasks to achieve a desired output. Examples of a processor may include a central processing unit (CPU), a graphics processing unit (GPU), a digital signal processor (DSP), a microcontroller, a field- programmable gate array (FPGA), or an application-specific integrated circuit (ASIC). The processor may be a component of a larger system, such as an embedded system, a computer, or a mobile device, and may interact with other components such as memory, input / output devices, and sensors. The processor may output the spectroscopic data, for example to an interface for further processing or to a user interface. The processor may further be configured to apply a chemometric model and output the chemometric data obtained by the chemometric model. The spectroscopic device may further comprise a memory. The memory may be configured to store the chemometric model. The memory may be configured to store spectroscopic data.

[0026] The term "spectroscopic data” may refer to data associated with a spectroscopic measurement of an object, in particular with optical spectroscopic measurement of an object. Spectroscopic data may be or may comprise one or more than one spectrum. The term "spectrum” may refer to a data structure in which several intensity values or values derived thereof such as absorbance of radiation are associated with wavelengths or wavelength ranges of the radiation. The wavelength or wavelength ranges may be those described above. The data structure may be a vector, wherein each element represents an intensity and the position in the vector represents a certain wavelength or wavelength range, so the value at a certain position represents the intensity of that wavelength or wavelength range. The data structure may be a vector or matrix containing value pairs, wherein one value represents the wavelength or wavelength range and the other value the intensity at this wavelength or wavelength range. The spectrum recorded by the spectrometer may be corrected by calibration coefficients to compensate for sensor imperfections or drifts. The spectrum may represent the absorbance or transmittance of radiation after having penetrated an object.

[0027] The term "object” may refer to any object which can be measured by a spectrometer. An object can be a living body, for example a human, or a non-living object. An object may refer to a complete object or a small piece thereof, for example a sample extracted from the object. In case of a living body, the object may refer to a body part, for example face or hand. The object may have any aggregate state, e.g. solid, liquid, gaseous, or supercritical. The object may be homogeneous, i.e. it does not contain internal phase boundaries between domains of the size of the illuminated radiation or larger, for example a plastic bottle or a blood sample. The object may be heterogeneous, it contains internal phase boundaries between domains of the size of illuminated radiation or larger, for example a soil sample containing sand.

[0028] The term "object data” may refer to data associated with a characteristic of the object such as a physical or chemical characteristic of the object. Object data may refer to any data associated with a characteristic of the object which has been obtained with a method other than spectroscopy. Object data may correlate with the chemometric data to be determined. Object data may be associated with the same characteristic as the chemometric data to be determined or with a different characteristic as the chemometric data to be determined. Physical characteristics may comprise thermal characteristics, for example the temperature, the thermal conductivity or the specific heat capacity; the macroscopic or microscopic state of matter, for example the shape or topology of the object or the degree of crystallinity; mechanical characteristics, for example pressure, compressibility or mechanical elasticity; optical characteristics, for example the color, refractive index, optical conductivity or absorption coefficients; electromagnetic characteristics, for example electrical conductivity, dielectric constant, radio frequency-based permittivity, microwave complex permittivity, millimeter wave complex permittivity, magnetic permittivity or susceptibility. Chemical characteristics of an object typically refer to the chemical composition of the object comprising the material composition, for example the type and the concentration of certain chemical compounds such as the water content; the state of matter, for example the degree of crystallinity or the aggregate state, for example in a disperse phase.

[0029] For the case that the object is a living body, in particular a human, the object data may contain or may be a biomarker. The term "biomarker” may refer to a measurable substance, process or characteristic that is indicative of a biological state or condition. A biomarker may refer to a specific molecule, protein, genetic sequence, or other measurable feature that is associated with a particular disease, condition or treatment response. Examples for biomarkers are body dimensions such as size, head circumference, chest girth, abdominal girth, crotch length, arm length; body weight or body mass index; body topology such as face topology, iris structure, finger print, palm topology; muscle measures like muscular strength, muscular endurance, muscular agility and speed, balance, coordination; cardio-vascular measures such as heart rate, heart rate variability, electrocardiogram, blood pressure, blood oxygen; skin measures such as skin conductance, skin impedance, skin moisture level, skin sebum level, skin roughness, skin elasticity, skin pH, skin blood flow, skin sweat rate; blood metabolites such as blood glucose, blood cholesterol, blood triglycerides, blood urea, blood creatinine, blood lactate, blood bilirubin, blood pH; urine metabolites such as urine glucose, urine urea, urine creatinine, urine ketones, urine pH, urine protein content; hormone levels such as thyroid hormone level, insulin level, growth hormone level, cortisol level, estrogen level, progesterone level, testosterone level, prolactin level; drug levels or levels of drug metabolites such as alcohol, amphetamines, opioids, cocaine, marijuana, benzodiazepines, barbiturates.

[0030] Object data may be obtained from sensors other than a spectrometer, for example a thermometer, a scale, a balance, an optical camera, an optical 3D scanner system, conductance or impedance gauge such as a corneometer, a sweat rate monitor or sweat patch, a liquid or gas chromatograph, a mass spectrograph, a nuclear magnetic spectrometer or imager, an electrochemical sensor, an immunoassay, a polymerase chain reaction apparatus. The spectroscopic device may be integrated into a portable device which further comprises sensors from which at least parts of the object data is received.

[0031] Object data may also be obtained from a storage device or it can be obtained from a user interface, for example a graphical user interface, to which a user can enter object data, for example from observations. In case the object to be measured is a human, object data may comprise human characteristics including a physical body measure like height, waist circumference, leg length, arm length, head circumference, foot size, hand size, body shape, for example a category like ectomorph, mesomorph, endomorph, body weight, body mass index, body composition, skin color, hair color, hair type; a demographic characteristic like age, sex, origin, ethnicity; medical history including current and former medications; nutrition habits such as vegetarian or vegan diet; consumption of stimulants such as caffeine, alcohol, tobacco products, drug; physical activity level such as type of profession, i.e. office job or physically demanding job, kind of sports, average duration of sports, average sleeping hours.

[0032] The term "environmental data” may refer to data associated with a characteristic of the surrounding of the object, for example a physical or chemical characteristic of the surrounding of the object. The characteristic of the surrounding of the object may have an influence on the spectroscopic measurement of the object or on the characteristic of the object such as the physical or chemical characteristic of the object. However, environmental data may not comprise an intrinsic characteristic of the object.

[0033] Environmental data may comprise sensor data from sensors other than a spectrometer. Environmental data may comprise the location of the object, for example the geolocation such as the GPC coordinates, the height above sea level, distance to a reference point such as the spectrometer, acceleration, orientation with regard to gravity; weather conditions such air temperature, air pressure, air humidity, wind speed, wind direction, ambient light intensity; time or date; air pollutant levels like CO2 concentration, CO concentration, ozone concentration, nitrogen oxide concentration, sulfur dioxide concentration, fine dust concentration, volatile organic compounds level. Sensor data may have been recorded by a sensor capable of determining the sensor data. The sensor may be integrated into the spectrometer. The spectroscopic device may be integrated into a portable device which further comprises sensors from which at least parts of the environmental data is received. The sensor may be communicatively coupled to the spectrometer, for example via a wireless communication or via internet. Examples for sensors may be a GPC receiver, an accelerometer, a gyroscope, an altimeter, a goniometer, a distance sensor like a time-of-flight sensor, a radar or a LiDaR, a pressure sensor such as a MEMS sensor, a piezo sensor or a capacitive sensor, a magnetometer, a barometer, a light sensor, a thermometer, a gas sensor.

[0034] Environmental data may comprise data associated with the spectrometer, for example a spectrometer ID, a version number of the spectrometer, the spectrometer settings, the temperature of the spectrometer, the age of the spectrometer, time since the last calibration was performed, age of the illumination source, number of measurements the spectrometer has already performed in its lifetime or within a certain time such as the last week or the last month. Environmental data may further comprise data associated with the spectroscopic measurement of the object, for example the sampling time, the illumination strength with which the spectrometer illuminates the object, or the distance of the object to the spectrometer.

[0035] Environmental data may be received from a data storage medium. The data storage medium may be part of the spectroscopic device, or it may be a remote storage device, for example a computer system or a cloud system. Environmental data may be received from a database, for example from a database on a remote storage system, in response to a request containing time and / or geographic location. A remote storage system may refer to a system which is far from the object of the measurement, for example a cloud server or a database server. For example, a request containing the GPS coordinates of the object and the time of the spectroscopic measurement may be sent to a cloud server having a weather database. The cloud server may in response to the request send weather data corresponding to the time and location of the request.

[0036] The spectroscopic data, the object data and the environmental data may be received through an input or an input interface. The spectroscopic data, the object data and the environmental data may be received through the same input interface or through different ones, for example the spectroscopic data may be received through an input interface communicatively coupled with a spectrometer module and object data and environmental data may be received through an input interface communicatively coupled to sensors. The input or an input interface may be a hardware or software component that facilitates the transmission of data from the computer system to external devices or systems. It may include ports, connectors, network cards, and wireless modules supporting various data transfer protocols like USB, HDMI, Ethernet, Bluetooth, and Wi-Fi. The output interface manages the encoding, formatting, and transmission of data, ensuring compatibility with the receiving device. It may comprise a USB port for peripherals, an HDMI port for video and audio signals, or network interface cards for LAN or internet communication. Software components, such as drivers and communication protocols, may also be included to ensure proper data transmission. In cloud-based environments, the input interface may include virtual interfaces like APIs and web services for data exchange between local systems and cloud platforms.

[0037] Chemometric data of the object using the spectroscopic data, the object data and the environmental data may be determined. The term "chemometric data” may refer to data associated with any characteristic of the object which can be derived from spectroscopic data. Chemometric data may be data associated with a physical or chemical characteristic of the object. Physical characteristics may comprise thermal characteristics, for example the temperature, the thermal conductivity or the specific heat capacity; the macroscopic or microscopic state of matter, for example the degree of crystallinity; optical characteristics, for example refractive index, optical conductivity or absorption coefficients; electro-magnetic characteristics, for example electrical conductivity, dielectric constant, magnetic permittivity or susceptibility. Chemical characteristics of an object typically refer to the chemical composition of the object comprising the material composition, for example the type and the concentration of certain chemical compounds such as the water content; the molecular structure of an object, for example the presence of functional groups like carbonyl groups or hydrogen bonds; the molecular weight, for example the degree of polymerization of a polymer; the state of matter, for example the degree of crystallinity or the aggregate state, for example in a disperse phase. Chemometric data may comprise biomarkers as defined above.

[0038] Chemometric data may be determined by employing a chemometric model. The term "chemometric model” may refer to a model which is parameterized to receive spectroscopic data, object data and environmental data as input and output the chemometric data. The chemometric model may be parameterized to receive spectroscopic data as input and output intermediate chemometric data. Intermediate chemometric data may be chemometric data which has been determined without taking into account the object data or the environmental data. The intermediate chemometric data may be adjusted or corrected using the object data and environmental data, for example by employing a refining model. The refining model may be a data-driven model which may be trained with historic data for adjusting or correcting intermediate chemometric data. A refining model may be a multivariate linear or polynomial regression model or it may be an artificial neural network.

[0039] The chemometric data obtained from the chemometric model may be associated with one or more than one characteristic, for example at least two or at least three characteristics. A chemometric model which outputs more than one characteristic may be referred to as multilabel chemometric model. A multilabel chemometric model may contain several partial chemometric models, wherein each partial chemometric model may be configured to translate a spectrum to one characteristic. The multilabel chemometric model may be configured to merge the chemometric data obtained from the at least two partial chemometric models, for example to output one vector comprising values for each characteristic.

[0040] A chemometric model may comprise a pre-processing method and a machine learning model to obtain the chemometric data. A chemometric model may comprise a pre-processing method, a feature selection filter and a machine learning model. If the chemometric model comprises two or more partial chemometric models, each partial chemometric model may comprise a separate pre-processing method, a feature selection filter and a machine learning model. Alternatively, the partial models may use the same pre-processing method or feature selection filter.

[0041] The term "pre-processing” may refer to a method to reduce or eliminate interferences from a spectrum such as stray light, noise or baseline drift to enhance the subsequent machine learning. Hence, the pre-processing method may be applied before the machine learning method. Pre-processing may include one or more of baseline correction, scatter correction, smoothing, scaling, aggregation.

[0042] The term "machine learning method” may refer to a model which translates spectra into corresponding object data. The machine learning method hence may use a spectrum as input and derive object data therefrom. The machine learning method may be considered as an integral part of the chemometric model. Machine learning methods may be supervised, semi-supervised or unsupervised. Machine learning methods may include multivariate calibration, classification, pattern recognition, clustering, ensemble methods, neural nets and deep learning, or multivariate curve resolution.

[0043] The term "feature selection filter” may refer to a method to select those parts of the spectrum with a correlation to the object data. A feature selection filter may facilitate the machine learning method of the chemometric model and thus avoid overfitting and reduce the number of required training datasets. A feature selection filter may use a spectrum as input, remove all unselected parts and output a spectrum with only the selected parts left. Hence, the output of the feature selection filter may be a spectrum in form of a vector of lower dimensionality than the input vector. The output of the feature selection filter can be used as input for the machine learning method. Hence, the feature selection filter may be applied before the machine learning method. The input of the feature selection filter may be the received spectrum or it may be the pre-processed spectrum, preferably the pre-processed spectrum. Hence, the feature selection filter may be applied after the pre-processing method.

[0044] A chemometric model may be or may contain a data-driven model. The chemometric model may be a trained data- driven model. Training may comprise adjusting parameters of the chemometric model such that the output of the chemometric model most closely fits to the provided training data. Often, training comprises minimizing a loss or cost function, for example a least mean square value of chemometric model output to provided training data. The complete set of training data may be used for training or parts thereof. Parts of the received training data may be used for training and the remainder may be used for determining the prediction accuracy of the trained chemometric model. Alternatively, cross-validation can be applied, for example K-fold cross-validation, leave-one-out cross- validation, stratified cross-validation.

[0045] A chemometric model may be configured to determine fitness and health information from a spectrum of the skin of a person, for example the hydration level of the skin or the blood glucose or lactose content of the skin, or a disease like diabetes or skin cancer. The result may be used to make recommendations to the user, for example to drink water according to the hydration level of the skin or adjust the training program according to the lactose level in the blood.

[0046] A chemometric model may be configured to analyze agricultural products, food or feed from a spectrum of an agricultural product, for example a fruit, vegetable, meat, dairy product; food, for example bread, sauces, sausages, sweets; feed like fodder or forage. For example the chemometric model may determine a relevant content, for example the sugar or protein content. The result may be used to make recommendations, for example the expected date for harvesting to a farmer or a suitable recipe for a cook.

[0047] A chemometric model may be configured to determine information related to recycling from a spectrum of an object to be recycled, for example a plastic object like a bottle or a metal object like a can. For example, it may be determined which material the object is made of, for example polyethyleneterephthalate (PET) for a bottle. The result may be used to make recommendations, for example the most suitable way for recycling the object or the most convenient place put the object so it can be recycled.

[0048] A chemometric model may be configured to determine product quality in a production process. Determining product quality may mean determining parameters of a specification, for example the concentration of an ingredient. Product quality may refer to the quality of the input material, for example as received from a supplier, of an intermediate, i.e. a material which as undergone some processing steps and will undergo further processing steps, and an output material, for example before it is packaged and shipped to a customer.

[0049] The chemometric data generated by the chemometric model may be output. The term "outputting” may refer to writing the chemometric data to a non-transitory data storage medium, for example into a file or database, display it on a user interface, for example a screen, or both. It is also possible to output the chemometric data through an interface to a cloud system for storage and / or further processing. Outputting may be effected through an output or an output interface for outputting the chemometric data of the object. An output or output interface may be a hardware or software component that facilitates the transmission of data from the computer system to external devices or systems. It may include ports, connectors, network cards, and wireless modules supporting various data transfer protocols like USB, HDMI, Ethernet, Bluetooth, and Wi-Fi. The output interface manages the encoding, formatting, and transmission of data, ensuring compatibility with the receiving device. It may comprise a USB port for peripherals, an HDMI port for video and audio signals, or network interface cards for LAN or internet communication. Software components, such as drivers and communication protocols, may also be included to ensure proper data transmission. In cloud-based environments, the output interface may include virtual interfaces like APIs and web services for data exchange between local systems and cloud platforms.

[0050] The present invention further relates to a non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform the method according to the present invention. The term "computer-readable data medium” may refer to any suitable data storage device or computer readable memory on which is stored one or more sets of instructions (for example software) embodying any one or more of the methodologies or functions described herein. The instructions may also reside, completely or at least partially, within the main memory and / or within the processor during execution thereof by the computer, main memory, and processing device, which may constitute computer-readable storage media. The instructions may further be transmitted or received over a network via a network interface device. Computer-readable data medium include hard drives, for example on a server, USB storage device, CD, DVD or Blue-ray discs. The computer program may contain all functionalities and data required for execution of the method according to the present invention or it may provide interfaces to have parts of the method processed on remote systems, for example on a cloud system.

[0051] Brief Description of the Figures

[0052] Figure 1 illustrates the acquisition of spectroscopic data, object data and environmental data from an object.

[0053] Figure 2 illustrates two examples of how chemometric data can be determined from spectroscopic data, object data and environmental data.

[0054] Figure 3 illustrates an example for a spectroscopic device of the present invention.

[0055] Figure 4 illustrates another example for a spectroscopic device of the present invention.

[0056] Figure 5 illustrates an exemplary user interface for the system of the invention. Description of Embodiments

[0057] Figure 1 illustrates the acquisition of spectroscopic data, object data and environmental data from an object. A spectrometer 101 may illuminate the object 103, for example a nutrient, a pharmaceutical or a body part, with a light beam 102. The spectrometer may contain an infrared light source, for example an incandescent lamp or an LED, and illumination optics. The infrared light beam 102 may contain light in the infrared wavelength range, for example 780 nm to 3 m. The infrared light beam may hit the object and be reflected back to the spectrometer. This measurement mode is often referred to as reflectance mode. Alternatively, the light beam 102 may penetrate the object 103 and be directed back to the spectrometer 101, for example with mirrors. This measurement mode is often referred to as transmission mode. The spectrometer 101 may comprise light collection optics to capture the light beam 102 travelling from the object 103 to the spectrometer 101 . The spectrometer 101 may comprise an optical element which separates different wavelengths of the infrared light beam 102 travelling from the object 103 to the spectrometer 101 in space or in time, for example a linear variable filter, an interferometer or a dedicated filter for each sensor element. The spectrometer 101 may contain one or multiple optical sensor elements which generate an electrical signal depending on the intensity of infrared light impinging on the optical sensor. The intensity data may be associated with the wavelength data, for example by a processor or microcontroller in the spectrometer. The spectrometer 101 may hence output spectroscopic data 104, for example as vector of wavelength values and an associated vector of intensity values. The spectroscopic data 104 may be raw data or it may be corrected data, for example by multiplication with calibration coefficients.

[0058] Sensor 105 may measure the object 103 and output object data 106. For example, the sensor 105 may be an RGB camera. In this case, the object data 106 may be an RGB image of the object 103 or a characteristic which is derived from the RGB image, for example its luminance, an average color or its orientation. Deriving such information may include a processor which applies an appropriate algorithm to the image. The processor may be part of the sensor 105 or it may be part of a computer system which is communicatively coupled with the sensor 105.

[0059] Sensor 107 may record a physical or chemical characteristic of the environment of the object 103 which may have an influence on the measurement of the spectrometer 101. For example, the sensor 107 may be a thermometer, a hygrometer or a pressure sensor measuring the air ambient to the object 103. In this case, the environmental data 108 may be the temperature, the air humidity or the atmospheric pressure surrounding the object.

[0060] Figure 2 illustrates two examples of how chemometric data can be determined from spectroscopic data, object data and environmental data. In Figure 2a spectroscopic data 211 may be input to a chemometric model comprising preprocessing 221, feature selection 222 and a machine learning model 223. Spectroscopic data 211 may comprise a spectrum, for example a near infrared spectrum, obtained from a measurement with a spectrometer. Pre-processing 221 may comprise baseline correction, for example first-order derivation, scatter correction, for example standard normal variate, smoothing, for example moving average filtering, scaling, for example Pareto scaling, aggregation, for example spatial median. The pre-processed spectroscopic data may subsequently undergo feature selection 222. Feature selection 222 may reduce the dimensionality of the spectroscopic data 211 , so training the machine learning model 223 requires less training data. Feature selection 222 may for example involve principle component regression (PGR). The thus pre-processed and feature-selected spectroscopic data may be passed as input to a machine leaning model 223, for example an artificial neural network. The machine leaning model 223 may be parametrized to further receive the object data 212 and the environmental data 213 as further input. The object data 212 and the environmental data 213 may be pre-processed before inputting into the machine learning model 213, for example to adjust the format and the units of the data. The machine leaning model 223 may be trained with historic data comprising spectra, object data and environmental data. The machine leaning model 223 may output chemometric data 215. Using a chemometric model which uses spectroscopic data, object data and environmental data as input has the advantage that complex interplays between spectroscopic data, object data and environmental data can be taken into account.

[0061] Figure 2b shows an alternative example for determination of chemometric data from spectroscopic data, object data and environmental data. Spectroscopic data 211 may be input to a chemometric model 231 which outputs intermediate chemometric data 232. The chemometric model 231 may not be parametrized to take object data 212 and environmental data 213 into account. Hence, the intermediate chemometric data 232 only depends on the spectroscopic data 232. In order to obtain the desired chemometric data 234, a refining model 233 may be employed. The refining model 233 may be parametrized to receive the intermediate chemometric data 232, the object data 212 and the environmental data 213 and to output the chemometric data 234. The refining model 233 may comprise to sub-models, one which processes the object data 212 and one which processes the environmental data 213. For example, the first sub-model may receive the intermediate chemometric data 232 and the object data 212 as input and output a refined chemometric data. A second sub-model may use the refined chemometric data and the environmental data 213 as input and output the chemometric data 234. The refining model 233 may be a multivariate polynomial regression model which adjusts the intermediate chemometric data 232 according to the object data 212 and the environmental data 213 to arrive at the chemometric data 234. A refining model 233 has the advantage that the chemometric model 231 does not need a retraining for new object data types or environmental data types.

[0062] Figure 3 illustrates an example for a spectroscopic device of the present invention. The spectroscopic device 300 may be a smartphone, a tablet or a wearable such as a smartwatch. The spectroscopic device 300 may comprise a spectrometer module 310. The spectrometer module 310 may comprise an illumination 311. The illumination 311 may be a light source, for example an incandescent lamp or an LED. The light source may produce electromagnetic radiation in the desired range, for example in the near infrared range. The illumination 311 may further contain optics to direct the electromagnetic radiation from the light source to the object, for example lenses, mirrors and / or apertures. The spectrometer module 310 may further comprise a controller 313, for example an ASIC. The illumination 311 may be operatively coupled to the controller 313. The controller 313 may supply electric power, e.g. from the battery of the portable device 300, and switch the light source of the illumination 311 on and off when required.

[0063] The spectrometer module may further comprise a detector 312. The detector 312 may generate electric signals in response to electromagnetic irradiation impinging on the detector 312. The detector 312 may contain an array of photosensitive regions. Each photosensitive region may be covered by a filter such that electromagnetic radiation of a dedicated wavelength or wavelength range impinges on a photosensitive region. The photosensitive region may be sensitive in the wavelength region of interest, for example in the near infrared region. The photosensitive region may contain a photoconductor, for example PbS or PbSe. The photosensitive region may generate an electric current which is indicative of the intensity of the electromagnetic radiation impinging on the photosensitive region. The detector 312 may contain optics to collect a maximum of incoming electromagnetic radiation. The optics may include mirrors, lenses and / or apertures. The detector 312 may be operatively coupled to the controller 313. The controller 313 may collect the signal or the signals from the detector 312 and forward them to the processor 330. The controller 313 may convert the signal or signals from analog to digital. This may, for example, be accomplished by integrating the electric current obtained from each photosensitive region and providing a value of the result in a digital form. By combining these values with the origin of the photosensitive region each of which measures the electromagnetic radiation at a particular wavelength or wavelength region, the controller 313 may gather spectroscopic data and forward these to the processor 330.

[0064] The spectroscopic device 300 may further comprise memory 320, for example RAM or flash memory. The memory 320 may store spectroscopic data, for example obtained from the spectrometer module 310. The memory may store object data, for example obtained from a different sensor of the spectroscopic device 300 or from a user interface to which a user has entered object data. The memory 320 may store environmental data, for example obtained from a different sensor of the spectroscopic device 300 or from a user interface to which a user has entered environmental data. The memory 320 may be operatively coupled to the processor 330, so the processor 330 may receive spectroscopic data, object data and / or environmental data from the memory 320.

[0065] The spectroscopic device 300 may further comprise a communication interface 340, for example a Wi-Fi connection to a network or a connection to a telecommunication network. The communication interface 340 may be operatively coupled to the processor 330, so the processor 330 may receive spectroscopic data, object data and / or environmental data from the communication interface 340, for example from a cloud computer system.

[0066] The spectroscopic device 300 may further comprise a camera 350, for example an RGB camera or an infrared camera. The camera 350 may be used for capturing an image of the object or its environment in order to obtain object data or environmental data. The camera 350 may be operatively coupled to the processor 330, so the processor 330 can receive image data from the camera 350. The processor 330 may execute code which is configured to extract object data or environmental data from the image received from the camera 350.

[0067] The processor 330 may execute the code for the method described above. The processor 330 may obtain the code from memory 320. The processor 330 may in this example execute both code of the spectrometer, in particular the determination of chemometric data using spectroscopic data, object data and environmental data, for example by executing a chemometric model. The portable device 300 may further contain a display (360) for collecting user input and display measurement results, for example via a graphical user interface (GUI).

[0068] Figure 4 illustrates another example for a spectroscopic device of the present invention. The spectroscopic device 420 may be a hand-held spectrometer. The spectroscopic device 420 may comprise an illumination 421, a detector 422, a controller 423 and a communication interface 424. The illumination 421 may be a light source, for example an incandescent lamp or an LED. The light source may produce electromagnetic radiation in the desired range, for example in the near infrared range. The illumination 421 may further contain optics to direct the electromagnetic radiation from the light source to the object, for example lenses, mirrors and / or apertures. The controller 423 may be an ASIC or a microcontroller. The illumination 421 may be operatively coupled to the controller 423. The controller 423 may supply electric power, e.g. from the battery of the spectroscopic device 420, and switch the light source of the illumination 421 on and off when required. The detector 422 may generate electric signals in response to electromagnetic irradiation impinging on the detector 422. The detector 422 may contain an array of photosensitive regions. Each photosensitive region may be covered by a filter such that electromagnetic radiation of a dedicated wavelength or wavelength range impinges on a photosensitive region. The photosensitive region may be sensitive in the wavelength region of interest, for example in the near infrared region. The photosensitive region may contain a photoconductor, for example PbS or PbSe. The photosensitive region may generate an electric current which is indicative of the intensity of the electromagnetic radiation impinging on the photosensitive region. The detector 422 may contain optics to collect a maximum of incoming electromagnetic radiation. The optics may include mirrors, lenses and / or apertures.

[0069] The detector 422 may be operatively coupled to the controller 423. The controller 423 may convert the signal or signals from analog to digital. This may, for example, be accomplished by integrating the electric current obtained from each photosensitive region and providing a value of the result in a digital form. By combining these values with the origin of the photosensitive region each of which measures the electromagnetic radiation at a particular wavelength or wavelength region, the controller 423 may gather spectroscopic data.

[0070] The communication interface 340 may be a wireless connection to a portable device 410. The communication interface 424 may be operatively coupled to the controller 423 so the communication interface 424 may transfer the spectroscopic data from the controller 423 to the portable device 410. The portable device 410 may be a smartphone or a tablet. The portable device 410 may comprise a communication interface 413 to receive the spectroscopic data from the spectroscopic device 420. The portable device 410 may comprise a processor 411, for example a CPU. The processor 411 may be operationally coupled to the communication interface 413. The processor 411 may receive the spectroscopic data form the communication interface 413. The processor 411 may be operationally coupled to memory 412. Memory 412 may store a chemometric model, object data and environmental data. The processor 411 may apply the chemometric model retrieved from memory 412 using the spectroscopic data, the object data and the environmental data and thereby determine chemometric data. The portable device 410 may further comprise a camera 414, for example an RGB camera, which operationally coupled to the processor 411 . The processor 411 may receive an image of the object from the camera 414 and extract object data from the image which may be used to determine chemometric data. The determined chemometric data may be stored in memory 412 or it may be displayed on display 414, for example on a user interface.

[0071] Figure 5 illustrates an exemplary user interface for the system of the invention. The user interface may be an application on a computer, a web interface, or on the spectroscopic device, for example a smartphone with an integrated spectroscopy module. The interface may display a window for a spectroscopy app, for example a fitness app 500 designed to measure and track the fitness level of a user. The interface may have an NIR spectrum section 510, in which the last measured spectrum is displayed, for example an NIR spectrum of the users skin. The user may hit the button labelled "Measure” 512 to trigger a new measurement which may subsequently be displayed in the NIR Spectrum section 510. The fitness app 500 may display a section additional data 520 designed to enter or display object data 521 and environmental data 523. In this example, object data 521 may be the body weight of the user and his heart rate. These values may either be entered manually, or they may be imported from memory or from a sensor, for example a pulse oximeter. The import may be automatic or manual, for example by hitting the button import 522. In this example, environmental data 523 are air temperature and humidity. These values may either be entered manually or they may be imported from memory, for example a cloud server providing weather data, or from a sensor, for example a thermometer or hygrometer. The import may be automatic or manual, for example by hitting the button import 524. The fitness app 500 may display a section fitness score 530 displaying the chemometric data obtained from a chemometric model which has received the spectroscopic data 511, the object data 521 and the environmental data 523 as input und which outputs the fitness score.

[0072] Examples

[0073] Example 1 : Skin Health

[0074] The health state of the skin of a person may be determined. The skin of a body part, for example the face or the hand, may be analyzed. The health state of skin may be determined by determining the skin moisture and / or skin lipid concentration as the skin barrier function mainly depend on these values. A spectroscopic measurement, for example by near-infrared spectroscopy, may be performed on the skin of the respective body part to collect spectroscopic data. The skin impedance of the respective body part may be measured with a corneometer to collect object data. The skin impedance is known to correlate with the skin hydration. Alternatively, or additionally, the skin temperature of the respective body part may be measured with a thermometer, for example an IR thermometer to collect object data. Alternatively, or additionally, the skin color and / or homogeneity may be determined with an RGB camera, for example a CMOS camera, to collect object data. The environmental temperature and / or humidity may be measured with a thermometer or a hygrometer to collect environmental data.

[0075] The spectroscopic data, the object data and the environmental data may be input to a chemometric model which outputs the skin moisture and / or skin lipid concentration. Alternatively, the chemometric model may receive the spectroscopic data as input and output intermediate values for the skin moisture and / or skin lipid concentration which may be adjusted by a refining model using the object data and the environmental data. The skin moisture and / or skin lipid concentration may be output, or these values may be used to further determine a skin health condition, for example a numeric score or a classifier indicating the health status. Such determination may be done by comparing the determined skin moisture and / or skin lipid concentration with reference values. The result may be output, for example on a user interface of an app on a portable device.

[0076] Example 2: Skin Pathology

[0077] The pathology of skin of a patient may be determined, for example to diagnose a disease or document its progression. Examples for skin diseases may be skin cancer, psoriasis or atopic dermatitis. An area of interest of the skin of the patient, for example a spot on the skin, may be subject to NIR spectroscopy to determine spectroscopic data of the skin area of interest. As object data skin parameters correlating with the disease or its state may be collected: the skin impedance may be measured with a corneometer, the skin temperature may be measured with a thermometer, the skin color or homogeneity may be measured with a camera, for example a RGB camera, or the size of an area of interest of the skin may be measured with a camera or a scale. As environmental data, the geographic location may be determined by a GPS sensor or input manually via a user interface or the concentration of hazardous gases, for example benzene or formaldehyde, in the air the patient is regularly exposed to, for example at a working place, may be determined with gas sensors. The spectroscopic data and the object data may be provided to a chemometric model which determines a probability for a certain disease from the spectroscopic data and the object data. The probability may be adjusted using the environmental data, for example using statistical correlations of the geographic location or the hazardous gas expositions with the disease. The thus adjusted probability or a classifier indicating a positive or a negative result based on the probability may be output.

[0078] Example 3: Sports and Fitness Tracking

[0079] The fitness level of a person may be determined, for example in order to track the training process and thus optimize the training program. Biomarkers indicative for the fitness level may be measured by spectroscopy, for example by Raman spectroscopy. Examples for such biomarkers are lactate concentration in blood, body hydration level or sweat quantity. Spectroscopic data may be collected by measuring a particular body part with a spectrometer. The spectroscopic data may be processed with a chemometric model to determine the biomarker.

[0080] Object data correlating to the fitness level of a person may be collected with sensors other than spectroscopy. For example, the body or skin temperature may be measured with a thermometer, the heart rate, the heart rate variability or the peripheral capillary oxygen saturation (spO2) may be measured with a pulse oximeter, the muscle power may be measured with a pressure sensor or an acceleration sensor, for example in a step counter or in a stationary bike, the electro cardiac activity may be measured with a electro cardio meter, the blood pressure may be measured with a sphygmomanometer, the body weight may be measured with a balance, the body height or the arm, leg, stomach girth may be measured with a scale, the sweat rate with a sweat rate monitor.

[0081] Environmental data correlating to the fitness level of a person may be collected with sensors other than spectroscopy. For example, the geographic location including the altitude may be measured with a GPS sensor or an altimeter, the ambient air temperature may be measured with a thermometer, the ambient air pressure may be measured with a barometer, the ambient air moisture may be measured with a humidity sensor, the ambient air composition, i.e. the oxygen or CO2 content, may be measured with a gas sensor, for example an electrochemical oxygen sensor.

[0082] The object data and the environmental data may be used to translate the lactate concentration in blood or the skin moisture level obtained from the spectroscopic data into the fitness level. For example, the lactate level at a certain muscle power taking into account the ambient air conditions may yield a reliable fitness level result.

[0083] Example 4: Nutrition based on Physical Condition

[0084] It may be desired to monitor and recommend nutrients depending on the fitness condition or the physical activity of a person. The fitness condition or the physical activity of a person may be determined. For this purpose, spectroscopic data of the person may be recorded using a spectrometer, for example an infrared spectrometer. One or more than one characteristics relating to the fitness condition or the physical activity of the person may be obtained by providing the spectroscopic data to a chemometric model. The chemometric model may, for example, output body hydration, the blood lactate value, the hemoglobin concentration, the blood oxygen saturation, the blood glucose level or the body core temperature. The object data may relate to the fitness condition or the physical activity of a person determined with a method other than spectroscopy. For example, the body or skin temperature may be measured with a thermometer, the heart rate, the heart rate variability or the peripheral capillary oxygen saturation (spO2) may be measured with a pulse oximeter, the muscle power may be measured with a pressure sensor or an acceleration sensor, for example in a step counter or in a stationary bike, the electro cardiac activity may be measured with a electro cardio meter, the blood pressure may be measured with a sphygmomanometer, the body weight may be measured with a balance, the body height or the arm, leg, stomach girth may be measured with a scale, the sweat rate with a sweat rate monitor.

[0085] Environmental data correlating to the nutrient demand of a person may be collected with sensors other than spectroscopy. For example, the geographic location including the altitude may be measured with a GPS sensor or an altimeter, the ambient air temperature may be measured with a thermometer, the ambient air pressure may be measured with a barometer, the ambient air moisture may be measured with a humidity sensor, the ambient air composition, i.e. the oxygen or CO2 content, may be measured with a gas sensor, for example an electrochemical oxygen sensor, the sunlight exposition of the person may be measured with a photometer.

[0086] The chemometric data, the object data and the environmental data may be used to determine the nutrient demand of the person, for example by using a model or a reference table. The nutrient demand may be translated into a recommendation of the kind of food and its amount corresponding to the nutrient demand, for example by using a database of food with their typical nutrient content or by using data giving on the package of available food, for example the stock of a supermarket or of a smart kitchen or a smart fridge which register their food stock together with the food nutrients. The food recommendation may be output to a user interface. The data may be stored for the next measurement.

[0087] Example 5: Nutrition based on Health

[0088] Nutritional recommendations may be determined based on the health condition of a person. One or more than one biomarkers indicative for a health condition may be determined by spectroscopy, for example the cholesterol level, may be measured by spectroscopy such as UV-vis spectroscopy. A blood sample may be subject to a UV-vis measurement yielding spectroscopic data. Such spectroscopic data may be input to a chemometric model which outputs the cholesterol level.

[0089] Object data correlating to the health condition of a person may be collected with sensors other than spectroscopy. For example, the body or skin temperature may be measured with a thermometer, the heart rate, the heart rate variability or the peripheral capillary oxygen saturation (spO2) may be measured with a pulse oximeter, the muscle power may be measured with a pressure sensor or an acceleration sensor, for example in a step counter or in a stationary bike, the electro cardiac activity may be measured with a electro cardio meter, the blood pressure may be measured with a sphygmomanometer, the body weight may be measured with a balance, the body height or the arm, leg, stomach girth may be measured with a scale.

[0090] Environmental data correlating to the nutrient demand of a person may be collected with sensors other than spectroscopy. For example, the geographic location including the altitude may be measured with a GPS sensor or an altimeter, the ambient air temperature may be measured with a thermometer, the ambient air pressure may be measured with a barometer, the ambient air moisture may be measured with a humidity sensor, the ambient air composition, i.e. the oxygen or C02 content, may be measured with a gas sensor, for example an electrochemical oxygen sensor, the sunlight exposition of the person may be measured with a photometer.

[0091] The biomarker, the object data and the environmental data may be provided to a model which determines the nutrient demand of the person. The nutrient demand may further be translated into food recommendations. The food recommendation may depend on the environmental data, for example the geolocation to take local dietary habits into account.

[0092] Example 6: Plant Identification

[0093] Plants or plant parts like fruit may be determined to identify the plant variety and / or determine the edibility, for example for mushrooms or berries. Spectroscopic data from the plant obtained by measuring the plant or plant part with a spectrometer, for example an infrared spectrometer.

[0094] Object data correlating with the plant variety or edibility may be recorded, for example an RGB image with a camera. Environmental data like the geographic location may be determined with a GPS sensor or an altimeter. The spectroscopic data and the object data may be provided to a chemometric model which determines the likelihood for various plant varieties. The environmental data may be used to select the most likely plant variety, for example based on the occurrence of such plant variety in the area determined by the geographic location. The resulting plant variety may be displayed to the user.

[0095] Example 7: Driver Monitoring

[0096] The driver of a vehicle such as a car, a train or a ship may be monitored, for example to ensure his fitness to drive. Spectroscopic data of the driver, for example the driver's face, may be obtained from a spectrometer, for example a UV-vis spectrometer. The spectroscopic data may be used to determine the alcohol concentration in the breath of the driver by using a chemometric model.

[0097] Object data correlating with the driver's fitness to drive may be recorded, for example movement data of the driver using an accelerometer, heart and breath rate using a pulse oximeter or a electro cardio meter, the driver's identity potentially associated with recorded driver's data by using face recognition, the optical appearance of the driver, for example face or eye reddening, using a camera, for example a CMOS camera, skin temperature or its distribution over the body using a thermometer, for example an IR thermometer, size and weight using scales or balances, for example integrated into the driver's seat.

[0098] Environmental data correlating with the driver's fitness to drive may be recorded, for example geographic location potentially combined with local regulations using a GPS sensor, time of day using a clock, ambient temperature using a thermometer, ambient gas concentrations, for example the CO2 concentration, using a gas sensor.

[0099] The data obtained from the spectroscopic data, the object data and the environmental data may be merged by a model into an indicator indicating the fitness to drive. Depending on the indicator, access to the vehicle may be granted to denied. Example 8: Diabetes Diagnostics

[0100] Diabetes in an early stage or the tendency to develop diabetes may be diagnosed on a patient. Spectroscopic data can be obtained from the fingernails, from hair or from skin with a spectrometer, for example a NIR spectrometer. The spectroscopic data can be used to determine the level of glycated keratin using a chemometric model.

[0101] Object data correlating with the risk factors for diabetes or with diabetes in an early phase may be obtained, for example skin color and structure using a camera, for example a CMOS camera, the skin conductivity using a corneometer, the skin temperature using a thermometer, the heart rate variability or the peripheral capillary oxygen saturation (spO2) may be measured with a pulse oximeter.

[0102] Environmental data correlating with the risk factors for diabetes or with diabetes in an early phase may be obtained, for example geographic location using a GPS sensor, time of day using a clock, ambient temperature using a thermometer, ambient gas concentrations, for example the 002 concentration, using a gas sensor.

[0103] The spectroscopic data may be used to determine an indicator for the risk for diabetes or the probability of having diabetes. Such indicator may be adjusted using the object data and environmental data which represents risk factors or environmental stress which can be used to optimize the prediction accuracy.

[0104] Example 9: Diabetes Treatment

[0105] The blood glucose level may be determined to monitor or adjust the treatment of a diabetes patient. Spectroscopic data may be acquired by a spectrometer, for example by a NIR spectrometer, measuring a body part of the patent, for example skin, hair or fingernails. The spectroscopic data may be translated to the blood glucose level with a chemometric model. In case of skin measurements, the glucose may be directly detected from blood. In case of fingernails, the glycation level of the keratin may be determined which enables an inference of the blood glucose level.

[0106] Object data correlating with the blood glucose level may be obtained, for example the glucose level measured by an invasive measurement using a glucometer, skin color and structure using a camera, for example a CMOS camera, the skin conductivity using a corneometer, the skin temperature using a thermometer, the heart rate variability or the peripheral capillary oxygen saturation (spO2) may be measured with a pulse oximeter, blood pressure using a sphygmomanometer.

[0107] Environmental data correlating with the blood glucose level may be obtained, for example geographic location using a GPS sensor, time of day using a clock, ambient temperature using a thermometer, ambient gas concentrations, for example the 002 concentration, using a gas sensor.

[0108] The spectroscopic data and those object data closely related to the glucose level may be used to determine the blood glucose level using a chemometric model. The residual object data and the environmental data may be used additionally to determine the treatment, for example the amount of insulin or the time to apply it. Such information may be displayed to the patient or may serve to apply the treatment, for example by providing it to an interface to an insulin pump. Example 10: Material Characterization

[0109] The composition of a material, for example a plastic, a fabric or wood, may be determined. Spectroscopic data may be obtained using a spectrometer, for example a NIR spectrometer, a UV-vis spectrometer or a microwave spectrometer. The material composition, for example a classification for the plastic type, may be obtained from the spectroscopic data using a chemometric model.

[0110] Object data correlating with the material composition may be obtained, for example the color or surface structure using a camera, for example a CMOS camera, the radio-frequency, microwave or millimeter wave permittivity using a respective detector, the conductivity using a conductivity detector.

[0111] Environmental data correlating with the material composition may be recorded, for example ambient temperature using a thermometer, ambient air humidity using a humidity sensor, ambient gas concentrations, for example the 002 concentration, using a gas sensor.

[0112] Object data and environmental data may be used to increase confidence in the material detection. For example, if the material is classified, the chemometric model may output probabilities for each material type. The object data and environmental data may be used to increase or decrease each probability based on the characteristic of the material. If two classes have similar probability, the object data and the environmental data may serve to more accurately pick the right class. Also, object data and environmental data can serve to decrease measurement disturbances, for example if they are input into the chemometric model as additional parameters.

[0113] Example 11 : Nutrient Determination in Food

[0114] The nutrient content, for example the content of fat, protein, carbohydrates or water in food may be determined. This may be particularly desirous for prepared food for which no nutrient data are available from the producer, for example for self-prepared food like a smoothie or a soup or food in a restaurant. Spectroscopic data of the food may be recorded using a spectrometer, for example an infrared spectrometer. The composition of the food may be obtained by providing the spectroscopic data to a chemometric model which outputs the composition, for example the content of fat, protein, carbohydrates or water.

[0115] Object data correlating with the nutrient composition may be obtained, for example the ingredients derived from an image of the ingredients or the prepared food using a camera, for example a CMOS camera, the viscosity using a rheometer, the electrical conductivity using a conductometer, or the weight of ingredients by using a balance, for example a balance integrated into the food processor.

[0116] Environmental data having an influence on the measurement of the nutrient composition may be recorded, for example the storage temperature of the food using a thermometer, ambient air humidity using a humidity sensor.

[0117] The nutrient content of the food as determined by spectroscopy may be corrected or adjusted using the object data and the environmental data. Alternatively, the object data and the environmental data may be an additional input to the chemometric model. The nutrient content may be displayed to a user, for example on a display of a portable device.

[0118] The present disclosure has been described in conjunction with preferred embodiments and examples as well. However, other variations can be understood and effected by those persons skilled in the art and practicing the claimed invention, from the studies of the drawings, this disclosure and the claims.

[0119] Any steps presented herein can be performed in any order. The methods disclosed herein are not limited to a specific order of these steps. It is also not required that the different steps are per-formed at a certain place or in a certain computing node of a distributed system, i.e. each of the steps may be performed at different computing nodes using different equipment / data processing.

[0120] As used herein ..determining" also includes ..initiating or causing to determine", "generating" also includes ..initiating and / or causing to generate" and "providing” also includes "initiating or causing to determine, generate, select, send and / or receive”. "Initiating or causing to perform an action” includes any processing signal that triggers a computing node or device to perform the respective action.

[0121] In the claims as well as in the description the word "comprising” does not exclude other elements or steps and the indefinite article "a” or "an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation. In the claims as well as in the description the word "comprising” or "including” or similar wording does not exclude other elements or steps and shall not be construed limiting to the elements or steps lined out. The indefinite article "a” or "an” does not exclude a plurality. A single element or other unit may fulfill the functions of several entities or items recited in the claims. The mere fact that certain measures are recited in the mutual different dependent claims does not indicate that a combination of these measures cannot be used in an advantageous implementation or further elements may be included.

[0122] Providing in the scope of this disclosure may include any interface configured to provide data. This may include an application programming interface, a human-machine interface such as a display and / or a software module interface. Providing may include communication of data or sub-mission of data to the interface, in particular display to a user or use of the data by the receiving node, entity or interface.

[0123] Various units, circuits, entities, nodes or other computing components may be described as "con-figured to” perform a task or tasks. Configured to shall recite structure meaning "having circuitry that” performs the task or tasks on operation. The units, circuits, entities, nodes or other computing components can be configured to perform the task even when the unit / circuit / component is not operating. The units, circuits, entities, nodes or other computing components that form the structure corresponding to "configured to” may include hardware circuits and / or memory storing program instructions executable to implement the operation. The units, circuits, entities, nodes or other computing components may be described as performing a task or tasks, for convenience in the description. Such descriptions shall be interpreted as including the phrase "configured to.” Any recitation of "configured to” is expressly intended not to invoke 35 U.S.C. § 112(f) interpretation. In general, the methods, apparatuses, systems, computer elements, nodes or other computing components described herein may include memory, software components and hardware components. The memory can include volatile memory such as static or dynamic random-access memory and / or nonvolatile memory such as optical or magnetic disk storage, flash memory, programmable read-only memories, etc. The hardware components may include any combination of combinatorial logic circuitry, clocked storage devices such as flops, registers, latches, etc., finite state machines, memory such as static random-access memory or embedded dynamic random-access memory, custom designed circuitry, programmable logic arrays, etc.

[0124] Any disclosure and embodiments described herein relate to the methods, the systems, apparatuses, devices, chemicals, materials, computer program elements lined out above and vice versa. Advantageously, the benefits provided by any of the embodiments and examples equally apply to all other embodiments and examples and vice versa. All terms and definitions used herein are understood broadly and have their general meaning.

Claims

Claims1 . A computer-implemented method for determining chemometric data of an object comprising: a) receiving spectroscopic data associated with a spectroscopic measurement on the object, object data associated with a characteristic of the object and environmental data associated with a characteristic of the surrounding of the object, b) determining chemometric data of the object using the spectroscopic data, the object data and the environmental data, c) outputting the chemometric data of the object.

2. The method according to claim 1, wherein the spectroscopic data associated with absorption or transmission in a wavelength range of 780 nm to 3 pm.

3. The method according to claim 1 or 2, wherein the object is a human and the object data comprises a biomarker.

4. The method according to any of the claims 1 to 3, wherein environmental data is received from a database in response to a request containing time and / or geographic location.

5. The method according to any of the claims 1 to 4, wherein the chemometric data is associated with skin health or pathology.

6. The method according to any of the claims 1 to 4, wherein the chemometric data is associated with sports and fitness.

7. The method according to any of the claims 1 to 4, wherein the chemometric data is associated with nutrition.

8. The method according to any of the claims 1 to 4, wherein the chemometric data is associated with monitoring the driver of a vehicle.

9. The method according to any of the claims 1 to 4, wherein the chemometric data is associated with diagnosis or treatment of diabetes.

10. The method according to any of the claims 1 to 4, wherein the chemometric data is associated with material characterization.11 . Use of the chemometric data of the object obtained from the method of any of the previous claims for fitness, health, nutrition, agricultural or recycling applications.

12. A spectroscopic device for determining chemometric data of an object comprising: a) a spectroscopy module for measuring an object and thereby generating spectroscopic data, b) an input for receiving object data associated with a characteristic of the object and environmental data associated with a characteristic having an influence on the measurement,c) a processor for determining chemometric data of the object using the spectroscopic data, the object data and the environmental data, and d) an output for outputting the chemometric data of the object.

13. The spectroscopic device according to claim 12, wherein the spectroscopic device is a spectrometer module which is integrated into a smartphone, a tablet or a wearable.

14. The spectroscopic device according to claim 12 or 13, wherein the spectroscopic device is integrated into a portable device which further comprises sensors from which at least parts of the object data or environmental data is received.

15. A non-transient computer-readable medium including instructions that, when executed by one or more processors, cause the one or more processors to perform a method comprising: a) receiving spectroscopic data associated with a spectroscopic measurement on the object, object data associated with a characteristic of the object and environmental data associated with a characteristic having an influence on the measurement, b) determining chemometric data of the object using the spectroscopic data, the object data and the environmental data, c) outputting the chemometric data of the object.

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