System and method for determining intensity level and signal-to-noise ratio from sample scanning spectroscopy

Abstract

Methods and systems for predicting signal quality. An analytical instrument support system receives preliminary sample data collected from a short scan of a sample. The analytical instrument support system determines a maximum brightness intensity level from the preliminary sample data. The analytical instrument support system determines a performance level based at least on the maximum brightness intensity level. The analytical instrument support system determines an intensity-time model including a plurality of intensity levels and a plurality of exposure times, based at least on deviations of one or more intensity linear models, the one or more deviations associated with the determined performance level. The analytical instrument support device determines a first maximum intensity level based at least on a first corresponding exposure time and the intensity-time model, or determines a first parametric exposure time based at least on a first corresponding intensity level and the intensity-time model.

Description

[0001] Related applications

[0002] This application claims the benefit of U.S. Provisional Patent Application No. 63 / 504,670, filed May 26, 2023, the contents of which are incorporated herein by reference in their entirety. Technical Field

[0003] This disclosure generally relates to systems and methods for performing spectroscopic analysis techniques, such as Raman spectroscopy. Specifically, systems and methods for determining the spectral intensity level and signal-to-noise ratio of a sample based on a short scan of the physical sample are disclosed. Background Technology

[0004] Raman spectroscopy is an effective tool for identifying and characterizing various sample compounds and substances. In Raman spectroscopy, light of a known wavelength (usually infrared or near-infrared) from a laser is directly irradiated onto the sample compound or substance. The laser (sometimes called a Raman pump source) interacts with the electron cloud in the molecules of the sample compound or substance, producing a characteristic wavelength shift. The precise nature of this wavelength shift depends on the materials present in the sample compound or substance. Each sample compound or substance produces a unique wavelength characteristic (often called a Raman feature). This unique Raman spectral feature allows for the identification and characterization of the sample compound or substance. More specifically, a spectrometer is used to analyze the spectrum returned from the sample compound or substance to identify the Raman-induced wavelength shift in the Raman pump source, and then this wavelength feature (e.g., via a computing device) is compared with a known library of Raman features. This allows for the determination of the precise properties of the sample compound or substance.

[0005] Raman spectroscopy is widely used in scientific, commercial, and public safety fields. Current state-of-the-art methods and systems assume a linear relationship between exposure time and intensity level, and a quadratic relationship between intensity level and signal-to-noise ratio (SNR). Furthermore, these assumptions are used to determine the time required to identify specific compounds or substances from a sample. However, these assumptions lead to unpredictability and a lack of reliability in existing state-of-the-art systems and methods.

[0006] For example, ideally, the intensity of a bright signal produced by a Raman system should increase linearly with exposure time within a dynamic range—where dynamic range refers to the interval between a base signal with (theoretically) 0 ms exposure time and a saturation point, after which further increases in exposure time no longer increase signal intensity. However, the linearity of the signal depends on various factors, such as the detector used in the system, the chemical sample, and the exposure time. Due to these factors, linearity may not always remain within the dynamic range, making it difficult to accurately predict signal quality at different exposure times. Summary of the Invention

[0007] Therefore, the examples described herein accurately predict signal quality at specific exposure times, and their value proposition lies in the ability to precisely predict the signal quality of any quality chemical substance within a short exposure time. This prediction improves system efficiency, resulting in more accurate results in a shorter time and fully utilizing the system's untapped potential. For example, the examples described herein predict signal strength at a target exposure time using short scans, where the predicted signal strength can be further used to extrapolate signal quality (e.g., through quantification via signal-to-noise ratio (SNR)). Conversely, the examples described herein can be used to predict exposure times to achieve the desired signal quality based on short scans.

[0008] In one aspect, a computer-executed method is disclosed. One or more processors receive preliminary sample data collected via a short scan of the sample; the one or more processors determine a maximum brightness intensity level based on the preliminary sample data; the one or more processors determine a performance level based at least on the maximum brightness intensity level; the one or more processors determine an intensity-time model, the intensity-time model including multiple intensity levels and multiple exposure times based on at least one or more deviations from an intensity linear model, the one or more deviations being associated with the determined performance level. The one or more processors determine a first maximum intensity level based at least on a first corresponding exposure time and the intensity-time model, or determine a first parameter exposure time based at least on the first corresponding intensity level and the intensity-time model.

[0009] The first maximum intensity level or the first parameter exposure time is stored in one or more computer-readable storage devices.

[0010] In another aspect, an analytical instrument support system is disclosed. The analytical instrument support system includes one or more computer processors, one or more non-transitory computer-readable storage media, and at least one program instruction stored in one or more non-transitory computer-readable storage media and executed by at least one of the one or more processors; the program instructions include instructions to perform the following operations: (i) Receive preliminary sample data acquired through a short sample scan; (ii) Determine the maximum luminance intensity level based at least on the preliminary sample data; (iii) The performance level shall be determined at least based on the maximum luminance intensity level; (iv) Determine an intensity-time model, the intensity-time model comprising multiple intensity levels and multiple exposure times based on at least one or more intensity linear model deviations, the one or more deviations being associated with a determined performance level; (v) Determine a first maximum intensity level based at least on a first corresponding exposure time and the intensity-time model, or determine a first parameter exposure time based at least on a first corresponding intensity level and the intensity-time model; and (vi) Store the first maximum intensity level or the first parameter exposure time in one or more computer-readable storage devices.

[0011] In another aspect, an analytical instrument is disclosed. The analytical instrument includes: a light source configured to irradiate a sample surface with light, and a spectrometer for acquiring the Raman spectrum of the sample surface based on the irradiation of the sample surface by the light source. The analytical instrument also includes one or more computer processors and one or more non-transitory computer-readable storage media. Following at least one execution program instruction in one or more processors, the analytical instrument performs the following actions, including (i) analyzing sample surface-related Raman spectral data from acquired Raman spectra; (ii) determining a maximum brightness intensity level based at least on the acquired Raman spectra; (iii) determining a performance grade based on the maximum brightness intensity level associated with the acquired Raman spectra; (iv) determining an intensity-time model, the intensity-time model including multiple intensity levels and multiple exposure times based at least on one or more deviations from an intensity linear model, the one or more deviations being associated with the determined performance grade; (v) determining a first maximum intensity level based at least on a first corresponding exposure time and the intensity-time model, or determining a first parameter exposure time based at least on a first corresponding intensity level and the intensity-time model; and (vi) storing the first maximum intensity level or the first parameter exposure time in one or more non-transitory computer-readable storage media.

[0012] According to this disclosure, it is not necessary for any system, method, or technique involving measurement-based spectroscopic techniques to include all the details characterized herein in order to obtain the corresponding benefits. Therefore, the specific examples described herein are intended as exemplary applications of the techniques, and alternatives are possible. Brief description of the attached diagram

[0014] The features and advantages of the present technology will become more apparent from the following detailed description of exemplary embodiments in conjunction with the accompanying drawings, wherein: Appendix Figure 1This is a block diagram of an exemplary analysis system according to some embodiments of the present disclosure.

[0015] Figure 2 According to some embodiments of this disclosure, it is shown that Figure 1 The optical architecture of the spectrometer included in the analysis system.

[0016] Figure 3 According to some embodiments of this disclosure, it is shown that Figure 1 Another optical architecture of the spectrometer included in the analysis system.

[0017] Figure 4 According to some embodiments of this disclosure, it is shown that Figure 1 Another optical architecture of the spectrometer included in the analysis system.

[0018] Figure 5 According to some embodiments of this disclosure, it is shown that Figure 1 Another optical architecture of the spectrometer included in the analysis system.

[0019] Figure 6A This is a flowchart illustrating an exemplary process for determining a first maximum intensity level or a first parameter exposure time, according to some embodiments of the present disclosure.

[0020] Figure 6B An exemplary process for determining deviations from a linear straight line is shown.

[0021] Figure 7A This is a flowchart illustrating an exemplary process for determining a first SNR value or a second maximum intensity level, according to some embodiments of this disclosure.

[0022] Figure 7B An exemplary process for determining deviations from a linear straight line is shown.

[0023] Figure 8 This is a flowchart illustrating an exemplary process for determining a second SNR value or a second parameter exposure time, according to some embodiments of this disclosure.

[0024] Figure 9 An exemplary performance rating table associated with a range of maximum luminance intensity values ​​is shown according to certain embodiments of this disclosure.

[0025] Figure 10A An exemplary intensity-time model of an exemplary sample compound according to some embodiments of the present disclosure is shown.

[0026] Figure 10B It shows Figure 10A An exemplary sample compound with an SNR-intensity model.

[0027] Figure 11A Another exemplary intensity-time model of another exemplary sample compound according to some embodiments of this disclosure is shown.

[0028] Figure 11B It shows Figure 11A Another SNR-intensity model for an exemplary sample compound.

[0029] Figure 12A Another exemplary intensity-time model of another exemplary sample compound according to some embodiments of this disclosure is shown.

[0030] Figure 12B Some embodiments according to this disclosure are shown. Figure 12A Another SNR-intensity model for an exemplary sample compound.

[0031] Figure 13A Another exemplary intensity-time model of another exemplary sample compound according to some embodiments of this disclosure is shown.

[0032] Figure 13B It shows Figure 13A Another SNR-intensity model for an exemplary sample compound.

[0033] Figure 14A Another exemplary intensity-time model of another exemplary sample compound according to some embodiments of this disclosure is shown.

[0034] Figure 14B It shows Figure 14A Another SNR-intensity model for an exemplary sample compound.

[0035] Figure 15A A block diagram is shown of an exemplary method for operating an exemplary Raman spectrometer according to some embodiments of the present disclosure.

[0036] Figure 15B A block diagram is shown of another exemplary method for operating an exemplary Raman spectrometer according to some embodiments of the present disclosure.

[0037] While this technology is readily adaptable to various modifications and alternatives, specific embodiments have been illustrated by way of example in the accompanying drawings and will be described in detail herein. However, it should be understood that the invention is not limited to the specific forms disclosed. Rather, the purpose of this invention is to cover all modifications, equivalents, and alternatives falling within the spirit and scope of the invention as defined in the appended claims.

[0038] Detailed description

[0039] The systems, methods, and techniques disclosed herein provide improved modeling, enabling more accurate determination of intensity levels, exposure times, and signal-to-noise ratios (SNR) in spectral results. For example, this disclosure, through an improved method, enables the acquisition of precise intensity levels and SNR values ​​from short scans of a sample to obtain preliminary sample data, thereby improving the accuracy of Raman spectroscopy results compared to linearly correlated exposure time and intensity level models. Similarly, this disclosure, through an improved method—correcting previous assumptions that intensity levels increase linearly with exposure time and SNR increases quadratically with intensity levels—improves the accuracy of Raman spectroscopy results compared to quadratically correlated intensity level and SNR models. The improvements of this disclosure are desirable because they increase the confidence level in determining the measurement parameters used to obtain the spectrum after short scans of the sample to obtain preliminary sample data. This increased confidence in parameter determination is particularly beneficial because it enhances the reliability of the spectrometer in identifying compounds or substances based on sample scans.

[0040] As described herein, the Raman measurement parameters of the analytical system are initial targets provided as instructions for the analytical instrument to acquire spectra. Raman measurement parameters may include scan time, peak intensity (such as the strongest peak), or signal-to-noise ratio (SNR). The systems and methods described herein model these Raman measurement parameters so that, when one parameter is selected, other parameters can be determined as outputs. For example, if the Raman measurement parameters provided as initial instructions are for scanning within a specific scan time, the peak intensity and SNR of the spectrum acquired within the target scan time are determined. In some embodiments, if the provided Raman measurement parameters are initial instructions for scanning a target SNR, the scan time for achieving the target SNR and peak intensity can be determined. In some embodiments, if the provided Raman measurement parameters are initial scan instructions for achieving a target peak intensity, the scan time required to achieve that target peak intensity and the signal-to-noise ratio (SNR) of the final spectrum can be determined.

[0041] As described above, current state-of-the-art systems and methods assume a linear correlation between exposure time and intensity level, and a quadratic correlation between intensity level and SNR. These assumptions are used to predict the total time required to accurately scan a sample after an initial short scan. However, current state-of-the-art systems and methods do not produce accurate results based on the assumption of a linear correlation between exposure time and intensity level. Furthermore, the assumptions used in current state-of-the-art systems lead to inaccurate sample determinations. This disclosure overcomes the shortcomings of current state-of-the-art techniques by providing an improved, accurate prediction of total scan time that is reliable and delivered faster than current state-of-the-art systems and methods. This disclosure further overcomes the shortcomings of earlier systems by using intensity level and SNR value predictions that deviate from linear and / or quadratic models, respectively, to identify compounds or substances through sample scanning.

[0042] In some embodiments, this disclosure provides for predicting accurate scan times, including predicting accurate intensity levels and SNR values ​​based on an initial short scan. Unlike the approximation that intensity levels increase linearly with exposure time and that the signal-to-noise ratio increases quadratically with intensity levels, some embodiments of this disclosure deviate from a linear model of a sample compound or substance—in an exponential, logarithmic, or both manner. These exponential and logarithmic deviations from the linear model provide more accurate predictions for the determination of compounds or substances during sample scanning.

[0043] In some embodiments, the systems and methods of this disclosure include: (i) determining an intensity level based on exposure time; (ii) determining an exposure time based on the intensity level; (iii) determining an SNR value based on the intensity level; (iv) determining an intensity level based on the SNR value; (v) determining an SNR value based on exposure time; and / or (vi) determining an exposure time based on a signal-to-noise ratio (SNR) value. Such determinations are based on intensity-time models and signal-to-noise ratio-intensity models for identifying compounds or substances. These two models can effectively achieve the above predictions with a preliminary short scan when signal intensity increases with exposure time or scan time, and the signal-to-noise ratio increases with signal intensity.

[0044] While some examples described herein calculate the intensity level as the maximum value of the signal's highest peak, in some implementations, the intensity level of each pixel can be calculated individually. By employing this approach, such implementations can predict the complete signal at different exposure times.

[0045] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. In case of conflict, this document (including the definitions) shall prevail. Exemplary methods and systems are described below; however, similar or equivalent methods and systems may be used in practice or testing with respect to the methods and systems described herein. All publications, patent applications, patents, and other references mentioned herein are incorporated herein by reference in their entirety. The systems, methods, and examples disclosed herein are illustrative and not restrictive.

[0046] The terms “comprising,” “having,” “having,” “may,” “containing,” and their variations, as used herein, are open-ended transitional phrases, terms, or words that do not exclude the possibility of other actions or structures. Unless the context clearly indicates otherwise, the singular form “a” and “the” include multiple references.

[0047] The modifier “about” used in relation to quantity includes the numerical value and has a meaning specified by the context (e.g., at least the degree of error associated with the measurement of a particular quantity). The modifier “about” should also be considered to disclose a range defined by the absolute values ​​of the two endpoints. For example, expressing “about 2 to about 4” also discloses the range “2 to 4”. The term “about” can refer to the indicated number plus or minus 10%. For example, “about 10%” can represent a range of 9% to 11%, and “about 1” can refer to 0.9–1.1. Other meanings of “about” are obvious from the context, such as rounding; therefore, for example, “about 1” can also refer to 0.5 to 1.4.

[0048] As used herein, the term “or” is intended to mean inclusive “or” rather than exclusive “or.” That is, unless otherwise specified or clearly apparent from the context, “X uses A or B” is intended to mean any natural inclusive permutation. That is, if X uses A, X uses B, or X uses both A and B, then “X uses A or B” is satisfied in any of the foregoing examples. Furthermore, unless otherwise specified or clearly apparent from the context that a singular form is involved, the article “a” as used in the subject matter specification and accompanying figures should generally be interpreted as meaning “one or more”.

[0049] The definitions of specific functional groups and chemical terms are described in more detail below. For the purposes of this disclosure, chemical elements are identified according to the CAS version of the periodic table of elements as contained on the back cover of the 75th edition of the Handbook of Chemistry and Physics, and the definitions of specific functional groups are generally also based on that document.

[0050] For the numerical ranges listed herein, every intermediate value between the stated values ​​and having the same precision is explicitly included. For example, for the range of 6–9, the numbers 7 and 8 are considered in addition to 6 and 9; for the range of 6.0–7.0, the numbers 6.0, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, 6.7, 6.8, 6.9, and 7.0 are explicitly included.

[0051] "Raman measurement" refers to a Raman system in which the diameter of the illumination spot remains constant and has a uniform radial distribution.

[0052] "Optical device for generating aspherical diffuse rings" refers to various embodiments for generating distributed light spots, including optical devices for generating aspherical diffuse rings (ADRPO). In some embodiments, the aspherical optics may include so-called axial cone or conical optics that generate intensity rings but have higher-order aspherical terms to generate extended patterns. In some embodiments, the aspherical optics may have coefficients A1 = 0.01, A2 = 0.06, and A4 = 0.002, with all other terms being zero.

[0053] A collimating lens is an optical element that transforms the direction of incident light into a parallel path.

[0054] A "filter" is an optical element that removes certain wavelengths of incident light.

[0055] "Focusing optics" refers to optical elements that transform the direction of incident light into a spatial point.

[0056] "Light source" refers to the light source used for excitation in spectroscopic applications. Exemplary systems and methods may include lasers suitable for Raman spectroscopy, such as 785 nm or 1064 nm. Example light sources may also include broadband sources such as LEDs.

[0057] "Sample surface plane" refers to the surface to which the irradiated area of ​​the sample being tested points.

[0058] A "steering mirror" is an optical element used to change the direction of light paths.

[0059] "Raman spectroscopy" refers to a spectrum of data values ​​that may include bright and / or dark spectra. The bright spectrum is the scattered light when the sample illuminates the detector. The dark spectrum is the spectrum received when no light illuminates the detector. The dark spectrum captures the shape of the baseline shift.

[0060] In some implementations, the exposure durations of the bright Raman and dark Raman spectra included in the short scan are between 1 millisecond (ms) and 10 seconds. In some implementations, a short scan includes a duration between 1 millisecond (ms) and 10 seconds; 1 ms to 9 seconds; 1 ms to 8 seconds; 1 ms to 7 seconds; 1 ms to 6 seconds; 1 ms to 5 seconds; 5 ms to 5 seconds; 25 ms to 5 seconds; 50 ms to 5 seconds; 100 ms to 5 seconds; 100 ms to 4.5 seconds; 100 ms to 4 seconds; 100 ms to 3.5 seconds; 100 ms to 3 seconds; 100 ms to 2.5 seconds; 100 ms to 2 seconds; 100 ms to 1.5 seconds; 100 ms to 1 second; 100 ms to 900 ms; 100 ms to 800 ms; 100 ms to 700 ms; 100 ms to 600 ms; 100 ms to 500 ms; 100 ms to 400 ms; 100 ms to 300 ms; 100 ms to 200 ms; or approximately 100 ms. In some implementations, the duration of a short scan includes not less than 100 milliseconds; not less than 200 milliseconds; not less than 300 milliseconds; not less than 400 milliseconds; not less than 500 milliseconds; not less than 600 milliseconds; not less than 700 milliseconds; not less than 800 milliseconds; not less than 900 milliseconds; not less than 1 second; not less than 2 seconds; not less than 3 seconds; or not less than 4 seconds. In some implementations, the duration of a short scan includes not more than 5 seconds; not more than 4.5 seconds; not more than 3.5 seconds; not more than 2.5 seconds; not more than 1.5 seconds; not more than 950 milliseconds; not more than 850 milliseconds; not more than 750 milliseconds; not more than 650 milliseconds; not more than 550 milliseconds; not more than 450 milliseconds; not more than 350 milliseconds; not more than 250 milliseconds; not more than 150 milliseconds; or about 100 milliseconds.

[0061] This disclosure is now described with reference to the accompanying drawings, wherein like reference numerals are used throughout to refer to like elements. In the following description, numerous specific details are set forth for purposes of explanation in order to provide a more thorough understanding of this disclosure. However, it may be apparent, however, that the systems and methods of this disclosure can be practiced without one or more of these specific details. In other instances, well-known structures and apparatus are shown in block diagram form to facilitate the description of the systems and methods of this disclosure.

[0062] It should be understood that although the embodiments described herein are used with a spectrometer or other optical instrument, they can also be constructed as stand-alone devices for measuring the electrochemical properties of sample compounds or substances. Furthermore, while some embodiments are described herein with respect to measuring the electrochemical properties of sample compounds or substances, the exemplary methods and systems described herein can be used to measure other electrochemical properties, such as Raman spectra of sample compounds or substances.

[0063] Exemplary analytical systems, such as Raman spectroscopy systems, can be used to identify unknown materials in a variety of environments, assess the threats posed by unknown materials, provide confirmatory identification of packaging raw materials, and offer general safety screening capabilities for a wide range of substances. Exemplary analytical systems can range in size from portable handheld instruments to larger systems permanently installed in laboratories.

[0064] I. Exemplary Analysis System

[0065] Those skilled in the art will understand that a variety of different optical architectures and arrangements are used in the field of Raman spectroscopy. Figure 1 An illustrative example of an analytical system 100 (also referred to herein as “analyzer 100”) is provided, which includes an optical architecture and other components, and is operated to measure one or more Raman spectra from a sample by one or more methods described herein.

[0066] Figure 1 The analyzer 100 shown includes a spectral system 110, which is communicatively coupled to a computing device 120 via a network 130. For example... Figure 1 As shown, the spectral system 110 includes a controller 111, an electronic signal processor 113, and a spectrometer 140 (e.g., a Raman spectrometer).

[0067] It should be understood that in some embodiments, at least a portion of the computing device 120 may be decoupled from the spectral system 110, providing opportunities to increase computing power at a central location or across multiple locations. Those skilled in the art can envision various physical and wireless interconnections between the components of the analyzer 100. It should also be understood that in some embodiments, the spectral system 110 and the computing device 120 may be communicatively coupled without network 130 (e.g., via a dedicated wired or wireless connection). Alternatively, some embodiments of the analyzer 100 may not require the resources of the computing device 120, but may instead utilize resources within the spectral system 110 to perform the methods described herein. Therefore, the computing device 120 may not be essential for the operation of the analyzer 100 and / or the spectral system 110, and Figure 1 The examples provided should not be considered limiting. As described herein, the analyzer 100 can be used to measure one or more Raman spectra of a sample compound or a plurality of substances using one or more methods described herein.

[0068] It should be understood that in some implementations, Figure 1The analyzer 100 and / or spectroscopic system 110 shown may be housed in a common housing forming an analytical instrument, which may include a benchtop or portable Raman spectrometer device (e.g., a handheld device). However, in other embodiments, one or more components of the analyzer 100 and / or spectroscopic system 110 may be contained in separate housings or devices and may be coupled as needed (e.g., communicative, electrical, mechanical, etc.) to perform the methods described herein. Furthermore, in some embodiments, the operations described herein as being performed by components of the analyzer 100 and / or spectroscopic system 110 may be combined and distributed in various ways. For example, in some embodiments, the electrical signal processor 113 may be part of a controller 111, wherein the controller 111 is configured to perform the operations of the electrical signal processor 113 as described herein. Furthermore, the operations described herein as being performed by the controller 111 may be distributed across multiple controllers. In the same or alternative examples, the operations described herein as being performed by the controller 111 may be distributed among one or more computing devices (e.g., the electronic signal processor 113, computing device 120, or multiple computing devices). In some embodiments, controller 111 is configured to control the operation of spectrometer 140, while electronic signal processor 113 is configured to control other components of the spectral system 110 (e.g., communication with computing device 120). However, these roles of controller 111 and electronic signal processor 113 can be combined and distributed in various ways, and in some embodiments, the spectral system 110 includes only controller 111 or electronic signal processor 113, and as described herein, the included device performs the functions of both controller 111 and electronic signal processor 113.

[0069] The spectral system 110 may also include additional components (such as a power supply assembly), a user interface 114 (such as a display 112 and / or a user input and / or output (“I / O”) interface 109, e.g., a keyboard, mouse, touchscreen, etc.), optical components (e.g., mirrors, lenses, fiber optic cables, gratings, and filters), etc. The spectrometer 140 included in the spectral system 110 includes one or more optical components 145, a detector 147 (e.g., a CCD detector, a PMT detector, or other detectors known in the art), and a light source 149. The light source 149 directs light onto the sample (…). Figure 1 (not shown) provides an excitation beam (e.g., an excitation laser that provides 785 nm or 1064 nm light).

[0070] As described above, the spectroscopic system 110 and / or spectrometer 140 may comprise a fully integrated portable system operated by a user using battery power to perform Raman spectroscopy measurements in a variety of environments, such as laboratory environments, manufacturing (e.g., bioreactor-based) environments, remote environments, etc. Furthermore, in the same or alternative embodiments, elements of the spectroscopic system 110 may serve as a standalone system operating using a communication connection (e.g., optical, wireless, electrical, mechanical, etc.) and a power outlet connected to a central power source to perform Raman spectroscopy measurements in said various environments.

[0071] Referring now to the light source 149 of the spectrometer 140, it should be understood that the implementation of the light source 149 may emit light of a specific wavelength as required by the application, for example, including or between the following wavelength bands: about 400 nm to about 1064 nm, about 400 nm to about 750 nm, about 400 nm to about 600 nm, about 400 nm to about 500 nm, about 600 nm to about 900 nm, about 700 nm to about 850 nm, about 600 nm to about 1064 nm, 750 nm to 1064 nm, 850 nm to 1064 nm, 950 nm to 1064 nm, and a wavelength of about 785 nm, or a wavelength of about 1064 nm.

[0072] Figure 2 Provided including spectrometer 140 (see Figure 1 This is an illustrative example of one implementation of the optical architecture of the optical components of an optical system 200, collectively referred to herein as optical system 200. It should be understood that different optical architectures of Raman spectrometers are known in the art, therefore... Figure 2 The examples provided should not be considered limiting. For instance, some implementations employ so-called transmission diffraction instead of reflection diffraction, and there are related differences in optical architecture.

[0073] Figure 2 The illustration shows the light source 149, which serves as the laser component 201 (see Figure 149). Figure 1 One embodiment includes a laser source that generates a beam that propagates along an optical or beam path 230 (e.g., an arrow indicating the direction of beam propagation) to a sample 260. It should be understood that the sample 260 may contain any sample type of interest to the user, including substantially dry samples (e.g., powders, solid materials), substantially fluid samples (e.g., liquids, gases), or combinations of both (e.g., gels). In response to light from the laser assembly 201, the sample 260 generates scattered light (e.g., including Raman and Rayleigh portions of the scattered light) that travels along an optional or beam path 240.

[0074] In some embodiments, the laser component 201 may generate laser power as required by the application, for example, including or within the following power ranges: about 250 mW to about 750 mW; about 250 mW to about 700 mW; about 250 mW to about 650 mW; about 250 mW to about 600 mW; about 250 mW to about 550 mW; about 250 mW to about 500 mW; about 250 mW to about 450 mW; about 250 mW to about 400 mW; about 250 mW to about 350 mW; about 250 mW to about 300 mW; or about 250 mW. Furthermore, in some embodiments, the laser power affects the values ​​of the reference value and the maximum brightness intensity value when scanning the sample 260. It should be understood that other ranges and / or levels of laser power are known in the art, and therefore the examples described with respect to the laser component 201 should not be considered limiting.

[0075] Figure 2 One embodiment of the architecture for directional control of beam paths 230 and 240 and for adjusting one or more characteristics of the beams generated from the laser assembly 201 and the sample 260 is also shown. For example, a steering mirror 202 redirects the beam path 230 to a focusing lens 203, which focuses the beam onto a waveguide phase scrambler 204 (e.g., to adjust the phase characteristics of the beam). The beam exits the waveguide phase scrambler 204 and proceeds to a collimating mirror 205 (e.g., which adjusts the collimation characteristics of the beam), and then to a broadband filter 206 that transmits light of a specific wavelength or wavelength range. The beam proceeds to a plane mirror 207, which redirects the beam path 230 to a selective element 209. The selective element 209 may include a dichroic mirror, a notch filter, or other optical elements that have substantially reflective properties for the wavelengths of the beam emitted from the laser assembly 201, while having substantially transmittive properties for the wavelengths or bands of Raman scattered light generated from the sample 260. In the described example, selective element 209 redirects optical path 230 to lens 208, which focuses the light beam onto sample 260. In the described example, lens 208 may comprise any type known in the art, such as an objective lens for focusing a light beam onto sample 260. Furthermore, as will be described below, some embodiments of lens 208 include special configurations and features that provide advantages for different types of sample 260.

[0076] In response to the beam from laser assembly 201, lens 208 collects Raman and Rayleigh scattered light generated by sample 260, creating a beam path 240 returning to selective element 209 and second selective element 210. As described above, selective elements 209 and 210 are substantially transmissive to the wavelength of the Raman scattered light, thereby allowing beam path 240 to pass through to additional optical elements that further adjust the path and modulate the characteristics of the beam propagating along beam path 240. For example, the optical elements may include focusing lens 211, plane mirror 212, baffle 213, slit 214, baffle 215, and collimating lens 216.

[0077] The light path 240 propagates from the collimating lens 216 to the reflecting mirror 220, which reflects the light path 240 towards the diffraction grating 217. It should be understood that, in Figure 2 In this example, the diffraction grating 217 is a reflective diffraction grating that produces a spectral distribution of light. The beam path 240 then travels to the focusing mirror 219, which redirects the beam path 240 to the focusing lens 221, which guides the beam to the elements of the detector 222. Figure 1 (One embodiment of detector 147).

[0078] It should also be understood that Figure 2 A baffle 218 for controlling stray light is shown in some embodiments.

[0079] As described above, it should be understood that various different lens 208 embodiments can be obtained, providing different focusing and light-collecting characteristics. For example, Figure 3 An example implementation of an optical architecture for analyzing samples contained in packaging (e.g., bags, bottles, etc.) is provided, wherein the optical architecture includes some components of an optical system 200 (see [link to implementation details]). Figure 2 ) and has lens 208 (see Figure 2 Other components of the feature, collectively referred to as optical device 300. In the described example, optical device 300 includes element 302, which may include focusing lens 203 (see [link to description]). Figure 2 The output may be from a light source 149 or a laser assembly 201 or a Raman laser 119, or from an optical fiber. Element 302 generates the light beam (e.g., from a light source 149 or a laser assembly 201 or a Raman laser 119, see [link]). Figure 1 , 25) Guided to the collimating lens 304 that generates a substantially collimated beam. In the example described, the collimating lens 304 may be movably mounted such that it can change position along the axis of the optical path. The range of motion includes approximately 0.1 mm to approximately 10 mm to allow the spot size on the sample surface to vary from approximately 10 μm to approximately 10 mm. It should also be understood that in some embodiments, any one (alone or in combination) of the collimating lens 304, the concave focusing lens 312, and / or the focusing optics 314 may be movably mounted to achieve variations in the spot size.

[0080] Collimating lens 304 guides the substantially collimated beam to optics 308, which generates an aspherical diffuse ring, to produce a radially diffused light pattern. The intensity of the output from optics 308 is stronger at the outer edges of the resulting pattern than at the center. While this pattern can be projected directly onto the sample surface 316, in practical applications, it is advantageous to use one or more steering mirrors 310, one or more filters 306, and focusing elements such as a concave focusing lens 312 and a focusing optics 314 to guide the radially diffused light pattern onto the sample surface 316.

[0081] Figure 4 Lens 208 is provided (see Figure 2 Another example of an implementation of the method, which can be used to analyze fluid or semi-fluid samples. Figure 4 The illustrated implementation includes some components of the optical system 200 and other components that provide features commonly referred to as “immersion probes”, wherein these components are collectively referred to herein as optical device 400. Figure 4 The illustrated embodiment includes a spherical lens 440, which is positioned within a cylindrical probe tip 410 at a lens opening 418. A seal is formed between the probe tip 410 and the lens 440 at the opening by any means known in the art, including all forms of welding or sintering and the use of epoxides or other adhesives. The probe tip 410 can have any length. Optionally, the probe tip 410 may have threads 414 on its inner surface and may be extended using a probe tube 430 having a threaded collar 432 for screwing into the probe tip 410. Optionally, a seal is formed between the probe tube lip 437 and the distal end of the probe tip 410. Furthermore, in this example, the optical device 400 includes an optical fiber coupler 439 transmitting from the laser assembly 201 (see [link to example]). Figure 2 Irradiation light from sample 260 and light from sample 260 (see Figure 2 The scattered light, wherein sample 260 may include a liquid sample, wherein lens 440 is immersed in the liquid. Additionally, in this example, optical device 400 may be configured to work with spectral system 110 (see...). Figure 1The separated components enable optical communication between the spectral system 110 and the optical device 400 via optical fiber.

[0082] It should be understood that Figure 3 and Figure 4 The examples provided are for illustrative purposes only; in practical applications, some implementations may include or omit components as needed. For instance, in some implementations, in applications employing fiber optic coupling or requiring beam adjustment / internal environment protection, one or more windows, collimating lenses, or other optical elements may be added. Therefore, Figure 3 The examples provided in Rabbit 4 should not be considered limiting.

[0083] Figure 5 Provided including spectrometer 140 (see Figure 1 This is another example of an implementation of the optical architecture of the optical components, collectively referred to herein as optical system 500. It should be understood that different optical architectures of Raman spectrometers are known in the art, and therefore, with... Figures 1 to 4 Similar examples exist. Figure 5 Examples should not be considered restrictive.

[0084] Figure 5 An example is shown as a light source 149 as a Raman laser 119 (see Figure 1 One embodiment includes a laser source that generates a beam that propagates along a first optical or beam path 510 (e.g., an arrow indicating the direction of beam propagation) to sample 530. It should be understood that, with reference to sample 260 (see...),... Figure 2 Similarly, sample 530 may contain any sample type of interest to the user, including substantially dry samples (such as powders, solid materials), substantially fluid samples (such as liquids, gases), or combinations of both (such as gels). In response to light from Raman laser 119, sample 530 generates scattered light (e.g., including Raman and Rayleigh portions of the scattered light) that travels along a second optical or beam path 520.

[0085] In some embodiments, the Raman laser 119 may generate laser power as required by the application, for example including or ranging from about 250 mW to about 1050 mW, including various sub-ranges therein, such as the non-limiting sub-ranges described above with respect to the light source 149 and the laser assembly 201. It should also be understood that in some embodiments, the laser power affects the values ​​of the reference value and the maximum brightness intensity value when scanning the sample 530.

[0086] Figure 5An architecture for directional control of a first beam path 510 and / or a second beam path 520 is illustrated in some embodiments. In some embodiments, beam paths 510, 520 may be controlled by one or more optical elements, such as a steering mirror, a waveguide phase scrambler, various lenses, broadband filters, or selective elements (e.g., mirrors, notch filters, or other elements that have basic reflective properties for the wavelength of the beam emitted by the Raman laser 119 and / or basic transmission properties for the wavelength or band of the Raman scattered light generated by the sample 530). In the described example, selective element 511 may transmit the laser wavelength emitted from the Raman laser 119, thereby allowing the first beam path 510 to be directed to a lens 508 that focuses the beam onto the sample 530. In the described example, lens 508 may comprise any lens type known in the art, such as an objective lens or such as... Figure 3 and Figure 4 The lens architecture used in the optional devices 300 or 400 shown can focus the light beam onto the sample 530.

[0087] Some embodiments of lens 508 have special configurations and characteristics that can provide advantages for different types of samples. For example, lens 508 can collect Raman and Rayleigh scattered light generated by sample 530 after being excited by the Raman laser beam 119. The scattered light collected by sample 508 is reflected back from the surface of sample 530 and returns along a first beam path 510 to a selective element 511 (e.g., a beam splitter, such as a dichroic mirror), which guides the scattered light along a second beam path 520. In some embodiments, selective element 511 has a fundamentally reflective characteristic to the wavelength of the Raman scattered light, allowing the second beam path 520 to be guided to other optical elements to further adjust the path and modulate the characteristics of the beam propagating along that path. Other optical devices may also be considered for selective element 511 to guide the scattered light along the second beam path 520.

[0088] like Figure 5 As shown, the optical system 500 also includes one or more optical components 115 (referred to herein as optical components 115a-115c), which may include elements such as collimating lenses and mirrors, filters (e.g., notch filters), diffraction gratings, and / or mirror relay systems. Scattered light is guided by one or more optical components 115a-115c to detector 117. Figure 1(One embodiment of detector 147). Signal processing and / or digitization of the signal associated with the scattered light received by detector 117 is performed by an electrical signal processor associated with optical system 500, which may be, for example, electrical signal processor 113, controller 111, computing device 120, or a combination thereof. For example, in some embodiments, electrical signal processor 113 may be a suitably programmed microprocessor or application-specific integrated circuit that includes read-only or read-write memory of any known type for storing instructions and data required for the operation of the spectrometer described herein.

[0089] As described above, it should be understood that various different lens 508 embodiments are available that provide different focusing and light-collecting characteristics.

[0090] Back Figure 1 The controller 111 may also include an electronic processor, input / output (I / O) interfaces, and data storage devices (not shown); however, it should be understood that the controller 111 may include more or fewer components. The controller 111 is adaptable to the aforementioned application scenarios and setup conditions, and may be configured with multiple electronic processors, multiple input / output interfaces, multiple data storage devices, or combinations thereof. In some embodiments, some or all of the components included in the controller 111 may be attached to one or more motherboards and enclosed in a housing (e.g., including plastic, metal, and / or other materials). In some embodiments, some of these components may be fabricated onto a single system-on-a-chip (SoC) (e.g., the SoC may include one or more processing devices and one or more storage devices).

[0091] As used herein, the term "processor" or "electronic processor" refers to any device or component thereof capable of processing electronic data from registers and / or memory to convert such electronic data into data that can be stored in registers and / or memory. The electronic processor included in controller 111 may include one or more digital signal processors (DSPs), application-specific integrated circuits (ASICs), central processing units (CPUs), graphics processing units (GPUs), or encryption processors.

[0092] (A dedicated processor that executes the encryption algorithm within the hardware), a server processor, or any other suitable processing device.

[0093] The data storage device included in controller 111 may include one or more local or remote memories, such as random access memory (RAM) devices (e.g., static RAM (SRAM), magnetic RAM (MRAM), dynamic RAM (DRAM), resistive RAM (RRAM), or conductive bridged RAM (CBRAM) devices), hard disk drive-based memory devices, solid-state memory devices, network drives, cloud drives, or any combination of memory devices. In some embodiments, the data storage device included in controller 111 may include memory that shares a die with the processor. In such embodiments, the memory may be used as cache memory and may include, for example, embedded dynamic random access memory (eDRAM) or spin-transfer torque magnetic random access memory (STT-MRAM). In some embodiments, the data storage device may include a non-transitory computer-readable medium having instructions thereon that, when executed by one or more processors (e.g., an electronic processor included in controller 111), causes controller 111 to store various applications and data for performing one or more methods or portions thereof described herein. For example, one or more data storage devices included in controller 111 may store prediction programs, device characteristic data, performance level data, or combinations thereof. It should be understood that each method described in this article can be implemented by one or more applications.

[0094] Device characteristic data may include bias and gain (e.g., charge-coupled device (CCD) bias and CCD gain) and σ readings of the spectral system 110 stored on a data storage device of the controller 111. These are specific to each analytical instrument.

[0095] In some implementations, the controller 111 determines the baseline level intensity based at least on one or more analytical instrument characteristics.

[0096] In some implementations, when the light source (see, for example, light source 149) Figure 1 ), optical component 201 ( Figure 2 When the power of the light source (either a 250 mW or a Raman laser 119 (Figure 5)) changes, the bias can be changed accordingly. For example, if the power of the light source changes from 250 mW to 1064 mW, the bias will change accordingly.

[0097] The performance level data stored in one or more data storage devices of controller 111 may include several performance levels, and the range of maximum luminance intensity values ​​corresponding to each individual performance level stored in the data storage device.

[0098] In some implementations, the maximum brightness intensity value is determined based on the Raman spectrum corresponding to each individual scan of the sample. In some implementations, the maximum brightness intensity value refers to the maximum intensity value (i.e., the highest peak value) in the Raman spectrum corresponding to each individual scan of the sample.

[0099] As discussed above, in some implementations, performance levels are categorized at least based on the range of maximum luminance intensity values. In some implementations, the performance levels are categorized based on the detector (e.g., see detector 117). Figure 5 The maximum luminance intensity range before saturation is achieved by dividing it into different ranges that define performance levels. For example, if the saturation signal is a count of 1,000,000, then a specific range of values ​​is selected and assigned to different performance levels to cover the maximum luminance signal level up to the saturation value. It should be understood that the maximum saturation value depends on the specific device (such as the specific components used in the spectral system 110), and therefore the range of maximum luminance may also vary from device to device.

[0100] In some implementations, the performance level includes one or more performance levels. In some implementations, the performance level includes multiple performance levels. In some implementations, the performance level is selected from a range including performance level 0 to performance level 100. Each individual performance level includes a lower threshold maximum luminance intensity level value and an upper threshold maximum luminance intensity level value.

[0101] In a first non-limiting example, the spectral system 110 may include performance levels from 0 to 100. In the first non-limiting example, each performance level increases the range of maximum luminance intensity values ​​in a fixed step of 50 (e.g., performance level 0 covers the range of maximum luminance intensity values ​​from 0 to 49, performance level 1 covers the range of maximum luminance intensity values ​​from 50 to 99, and so on).

[0102] In a second non-limiting example, the spectral system 110 may include performance levels from 0 to 5. In the second non-limiting example, each performance level progressively increases the range of maximum luminance intensity values ​​in a fixed step of 250 (e.g., performance level 0 covers the range of maximum luminance intensity values ​​from 0 to 249, performance level 1 covers the range of maximum luminance intensity values ​​from 250 to 499, and so on).

[0103] In a third non-limiting example, the spectral system 110 may include performance levels from 0 to 49. In the third non-limiting example, each performance level increases the range of maximum luminance intensity values ​​in a fixed step of 75 (e.g., performance level 0 covers the range of maximum luminance intensity values ​​from 0 to 74, performance level 1 covers the range of maximum luminance intensity values ​​from 75 to 149, and so on).

[0104] While the examples above provide performance levels with the same span of maximum luminance intensity values, in some implementations, the performance levels may have different spans. For example, in a fourth non-limiting example, the spectral system 110 may include performance levels from 0 to 5, where the step sizes may have different magnitudes, rather than the same magnitude step sizes as in the second example above. For example, performance level 0 includes the range of maximum luminance intensity values ​​from 0 to 499, performance level 1 includes the range of maximum luminance intensity values ​​from 500 to 774, performance level 2 includes the range from 775 to 1249, performance level 3 includes the range from 1250 to 1999, performance level 4 includes the range from 1999 to 2499, and performance level 5 includes the range from 2500 and above, until the detector saturates.

[0105] The I / O interface of controller 111 may include one or more communication chips, connectors, and / or other hardware and software to manage communication between controller 111 and other components. The I / O interface may include interface circuitry for coupling to one or more components using any suitable interface, such as a Universal Serial Bus (USB) interface, a High Definition Multimedia Interface (HDMI) interface, a Controller Area Network (CAN) interface, a Serial Peripheral Interface (SPI), an Ethernet interface, a wireless interface, or any other suitable interface. For example, the I / O interface may include circuitry for managing wireless communication of data transfer to and from controller 111. The term "wireless" and its derivatives can be used to describe circuits, devices, systems, methods, techniques, communication channels, etc., that transmit data over non-solid media using modulated electromagnetic radiation. This term does not imply that the associated device contains no wires, although in some specific implementations the associated device may contain no wires. The circuitry included in the I / O interface device for managing wireless communications may implement any of a number of wireless standards or protocols, including but not limited to Institute of Electrical Engineers (IEEE) standards, including Wi-Fi (IEEE 802.11 series), IEEE 802.16 standards (e.g., IEEE 802.16-2005 amendments), Long Term Evolution (LTE) projects, and any amendments, updates, and / or revisions (e.g., Advanced LTE projects, Ultra Mobile Broadband (UMB) projects (also known as “3GPP2”), etc.). In some implementations, the circuitry included in the I / O interface for managing wireless communications may operate according to Global System for Mobile Telecommunications (GSM), General Packet Radio Service (GPRS), Universal Mobile Telecommunications System (UMTS), High Speed ​​Packet Access (HSPA), Evolved HSPA (E-HSPA), or LTE networks. In some implementations, the circuitry included in the I / O interface for managing wireless communications may operate according to enhanced data for GSM Evolution (EDGE), GSM EDGE Radio Access Network (GERAN), Universal Terrestrial Radio Access Network (UTRAN), or Evolved UTRAN (E-UTRAN). In some implementations, the circuitry included in the I / O interface for managing wireless communications may operate according to Code Division Multiple Access (CDMA), Time Division Multiple Access (TDMA), Digital Enhanced Cordless Telecommunications (DECT), Evolved Data Optimization (EV-DO) and its derivatives, as well as any other wireless protocol designated as 3G, 4G, 5G, and higher. In some implementations, the I / O interface may include one or more antennas (e.g., one or more antenna arrays) for receiving and / or transmitting wireless communications.

[0106] In some embodiments, analyzer 100 provides a standalone or dedicated analytical instrument or device (or group of instruments or devices) configured to perform short scans and analyses of samples. However, in other embodiments, analyzer 100 may be configured to perform additional scans or analyses of one or more samples. Combining these scanning or analytical capabilities within a single system (e.g., a single analytical instrument) achieves the desired efficiency and improves analytical accuracy because samples can be scanned multiple times without changing sample location, reconfiguring the analytical instrument, or performing separate scans of other samples combined with the target sample, all of which can lead to delays and potential contamination or unexpected discrepancies between scans.

[0107] II. Exemplary Operating Method

[0108] Now for reference Figure 6A This figure is a flowchart of a process 600 for determining measurement parameters of a sample compound according to some embodiments of the present disclosure. As described above, process 600 can be implemented using a spectroscopic system 110. Process 600 is described herein as being executed via a controller 111. However, it should be understood that process 600 can be executed by one or more software and / or hardware components in various combinations and configurations. Figure 6A As shown, process 600 may include operations 602, 604, 606, 608, 610, or 612. In some embodiments, process 600 is executed in the order shown in FIG. 6. In some embodiments, process 600 may be executed in the order shown in FIG. 6, except... Figure 6A One or more sequences other than the one shown are executed.

[0109] In some implementations, process 600 may begin by performing a preliminary short scan of the sample, as discussed in detail above. The sample is scanned at least using the spectroscopic system 110, as described above. Figure 1-5 As described above, the spectral system 110 directs a Raman laser beam (e.g., light) onto the surface of the sample. The resulting scattered light is guided back by the selective element 511 and travels along the scattered light path 520, passing through the optical assembly 115 to reach the detector 117. The resulting sample Raman spectrum is received by the detector 117, and the received spectrum is processed and / or digitized by the electrical signal processor 113.

[0110] In some embodiments, an initial short scan of the sample is captured during an exposure time of 1 millisecond to 20 seconds. In some embodiments, the initial short scan of the sample captures both the bright Raman and dark Raman spectra of the sample. In some embodiments, the initial short scan of the sample captures approximately 100 milliseconds each of the bright and dark Raman spectra of the sample, for example, the total short sample scan time is 200 milliseconds. The short scan provides preliminary sample data for modeling all the determinations (i)-(vi) described above, which will be described in more detail below.

[0111] In operation 602, controller 111 receives preliminary sample data collected from a short scan of the sample.

[0112] In some embodiments, the electrical signal processor 113 determines the Raman spectral data of the sample. The electrical signal processor 113 determines the bright Raman spectral data based on the received Raman spectral data and transmits the bright Raman spectral data to the controller 111. In some embodiments, the controller 111 generates a bright spectral characterization of the received Raman spectral data of the sample. This characterization may be a visual diagram (e.g., a graph) or table displayed by the display 112, or it may be an array of values ​​stored in a data storage device (not shown), which may be used by components of the controller, for example, as a prediction procedure as described above.

[0113] In some embodiments, the controller 111 may retrieve one or more device characteristics related to the analytical instrument support device from device characteristic data stored on one or more data storage devices included in the controller 111. The one or more device characteristics related to the analytical instrument support device may include bias, gain, and σ reading. In various embodiments, the controller 111 determines the σ reading and bias based at least on dark Raman spectroscopy data.

[0114] In operation 604, controller 111 determines the maximum brightness intensity level based at least on the received preliminary sample data. The controller analyzes the brightness values ​​associated with the preliminary sample data from a short scan of the sample and identifies the peak with the highest intensity. This analysis can be performed, for example, using a peak search algorithm. Controller 111 analyzes the Ming-Raman spectral data and identifies the highest bright peak in the Ming-Raman spectral data. In some embodiments, the highest bright peak in the Ming-Raman spectral data represents at least the maximum brightness intensity level of the received Ming-Raman spectral data associated with the sample after the short scan. In some embodiments, controller 111 stores the maximum brightness level on a data storage device included in controller 111. In some embodiments, the maximum brightness intensity level is a value between 0 and the saturation of detector 117 of the spectral system 110. In some embodiments, the maximum brightness intensity level is based at least on an exposure time of approximately 1 millisecond to approximately 20 seconds for the sample short scan.

[0115] In operation 606, controller 111 determines the performance level based at least on the maximum luminance intensity level.

[0116] Each individual performance level corresponds to a lower threshold maximum luminance intensity level and an upper threshold maximum luminance intensity level. For example, in some implementations, there are multiple performance levels, including performance level 0 through performance level 5. Furthermore, for example, in some implementations, performance level 0 includes maximum luminance intensity levels from 0 to 349, performance level 1 includes maximum luminance intensity levels from 350 to 499, performance level 2 includes maximum luminance intensity levels from 500 to 699, performance level 3 includes maximum luminance intensity levels from 700 to 1099, performance level 4 includes maximum luminance intensity levels from 1100 to 1999, and performance level 5 includes all maximum luminance intensity levels greater than or equal to 2000.

[0117] In some implementations, the controller 111 generates an intensity linear model based at least on (i) a base level intensity and (ii) a maximum luminance intensity level associated with preliminary sample data at the exposure time, wherein the exposure time is between 1 millisecond and 20 seconds.

[0118] In some implementations, the controller 111 converts the domain of the generated intensity linear model from exposure time to intensity.

[0119] In operation 608, controller 111 determines an intensity-time model that includes multiple intensity levels and multiple exposure times, based at least on one or more deviations from a linear model of sample intensity, which are associated with a determined performance level. For example, in some embodiments, the deviations may be exponential, logarithmic, or first-, second-, or third-order polynomial deviations. These deviations are associated with the performance level determined in operation 606, thereby forming an intensity-time model associated with the determined performance level in operation 606. In some embodiments, the deviations for each category have similar mathematical forms, such as exponential forms, but with different coefficients. In some embodiments, different categories may have deviations with different mathematical forms; for example, one category may have a deviation fitted to an exponential curve, while different categories may have deviations fitted to a second-order polynomial form.

[0120] In some implementations, controller 111 applies one or more deviations to the intensity linear model. Applying one or more deviations to the intensity linear model results in a modified model that deviates from the linearity of the intensity linear model. Examples of deviations are shown below. Figure 6B middle.

[0121] In some implementations, controller 111 converts the domain of the intensity linear model from intensity to exposure time to form an intensity-time model that includes the modified calibration sample.

[0122] In some implementations, controller 111 determines the deviation of the sample from a linear line based on a previously determined curve obtained from measurement data from calibration samples of the same category as the target sample. For example, the target sample is the sample scanned in operation 602, while the calibration samples may be samples that respond similarly to light scattering intensity and thus belong to the same category.

[0123] Figure 6B This explains how to determine the deviation relative to a linear straight line. It also explains how to determine or assign performance levels, such as performance levels 0, 1, 2, 3, 4, and 5 (or performance levels 0, 1, 2, ..., and...). n These values ​​are based on the maximum brightness values ​​described above. Then, calibration samples are selected for each performance level. For example, calibration sample zero belongs to the category defined by the maximum brightness value of performance level 0, calibration sample one belongs to the category defined by the maximum brightness value of performance level 1, calibration sample two belongs to the category defined by the maximum brightness value of performance level 2, calibration sample three belongs to the category defined by the maximum brightness value of performance level 3, calibration sample four belongs to the category defined by the maximum brightness value of performance level 4, and calibration sample five also belongs to the category defined by the maximum brightness value of performance level 4. These samples are... Figure 6B The plots shown are labeled from left to right; categories 2-4 are omitted for simplicity and clarity. The linear model for each calibration sample was determined as described above, i.e., by extrapolating data from a baseline value and a 100ms scan. These are represented in the plot as: line 652 for calibration sample 0 (category 0), line 654 for calibration sample 1 (category 1), and line 656 for calibration sample 5 (category 5). Each calibration sample was then scanned with increasing exposure time. The curves representing these data are schematically represented as: curve 658 for calibration sample 0, curve 660 for calibration sample 1, and curve 662 for calibration sample 5. The differences between the extrapolated lines 652, 654, and 656 and the curves 658, 660, and 662 were calculated, and a graph of the difference versus intensity was plotted for each calibration sample.

[0124] These graphs showing the relationship between differences and intensity are... Figure 6BIn the figure below, the difference versus intensity plots are fitted to a curve. These curves are schematically represented as: curve 664 for calibration sample 0, curve 666 for calibration sample 1, and curve 668 for calibration sample 5. Any fit can be made to match the data trend, and the fit depends on the scattering intensity response of the Raman excitation of the calibration sample. In this embodiment, the best curve fit is an exponential fit, which takes the form of equation (1), as shown below: (1) in It's a difference in strength, A y C y and t y is the coefficient of the y-th performance level (0-5 in this example), and I is the intensity.

[0125] Back Figure 6A The intensity linear model of the sample is generated at least based on a reference intensity value of 0 ms and a maximum brightness intensity value of 100 ms. Then, based at least on a linear plot from the reference value to the maximum brightness intensity value of 100 ms, a linear line is constructed and extrapolated over a range of intensity levels and exposure times. In some embodiments, the intensity linear model is an intensity-time linear line. The domain is converted from intensity level to exposure time so that the intensity-time linear line is described as exposure time versus intensity level. It should be understood that the controller 111 does not necessarily provide a graphical display of the plot or any straight line; although the plot may be displayed on the display 112, what is actually operated by the controller 111 (e.g., an electronic processor) is data representing the plot (e.g., an array containing intensity values ​​and corresponding energy values), which is stored in one or more data storage devices included in the controller 111.

[0126] Controller 111 is used to determine the deviation of the sample from a linear straight line.

[0127] (For example, from exposure time to intensity level), and create an intensity-time model by applying these deviations to a linear line for that category. For example, where the deviations are as referenced. Figure 6B The deviation is defined as the difference between the coefficients of equation (1) and equation (2). The controller 111 subtracts one or more intensity level predictions from the corresponding one or more intensity levels of the linear line, thereby generating a bias model based at least on this subtraction. For example, relative to the intensity linear model, the sample biases by 1300 at intensity level 15,000 (e.g., the determined intensity level is 13,700) and by 1735 at intensity level 24,000 (e.g., the determined intensity level is 22,265). Where the bias is an exponential bias, it is described as ( This generates an exponential deviation relative to the linear model.

[0128] In operation 610, controller 111 determines a first maximum intensity level based at least on a first corresponding exposure time or a first parameter exposure time (based at least on a first corresponding intensity level and an intensity-time model). In some embodiments, the input received by controller 111 includes at least a set of program instructions for identifying the first maximum intensity level based on the first corresponding exposure time. Controller 111 is used to analyze the intensity-time model and identify the first maximum intensity level. In some embodiments, the input received by controller 111 includes at least a set of program instructions for identifying a first parameter exposure time based on the first corresponding intensity level and the intensity-time model. Controller 111 is used to analyze the intensity-time model and identify the first parameter exposure time based on the received input of the first corresponding intensity level.

[0129] In some implementations, controller 111 is also used to determine an intensity-time model. Controller 111 iterates over multiple intensity levels until a first corresponding exposure time is reached on the intensity-time model, wherein the intensity level corresponding to the first corresponding exposure time is the first maximum intensity level.

[0130] In some implementations, controller 111 is also used to determine an intensity-time model. Controller 111 iterates over multiple exposure times until a first corresponding intensity level is reached on the intensity-time model, wherein the exposure time corresponding to the first corresponding intensity level is the first parameter exposure time.

[0131] In some implementations, controller 111 determines a first maximum intensity level. Controller 111 receives first sample data collected from a first sample scan of the sample, wherein the sample is scanned at a first corresponding exposure time.

[0132] In some implementations, controller 111 determines a first parameter, exposure time. Controller 111 receives second sample data collected from a second sample scan of the sample, wherein the sample is scanned at a first corresponding intensity level.

[0133] In operation 612, controller 111 stores the first maximum intensity level or the first parameter exposure time on a data storage device included in controller 111. In some embodiments, operation 614 may further include controller 111 receiving a set of program instructions to display (i) the first maximum intensity level and (ii) the first parameter exposure time to the user of spectral system 110 on display 112.

[0134] For example, the operator or controller 111 can then scan the sample at a first corresponding scan time using a value of a first maximum intensity level determined based on a first corresponding exposure time, thereby obtaining a maximum intensity level determined to be the same as or approximately the same as the first maximum intensity level. This ensures that the peak intensity at all target wavelengths is sufficiently high, thus providing information such as quantitative or qualitative identification of compounds. In another example, the controller 111 can scan the sample using a value of a first parameter exposure time determined based on the first corresponding maximum intensity level, thereby obtaining a spectrum with the first corresponding maximum intensity level within a time approximately equal to the first parameter exposure time.

[0135] Now for reference Figure 7A This figure is a flowchart of a process 700 for determining additional measurement parameters of a sample compound according to some embodiments. As described above, process 700 can be implemented using a spectroscopic system 110. Process 700 is described herein as being executed via a controller 111. However, it should be understood that process 700 can be executed by multiple software and / or hardware components in various combinations and configurations. Figure 7A As shown, process 700 may include operation 702, operation 704, or operation 706. In some embodiments, process 700 follows... Figure 7A The process is executed in the order shown. In some embodiments, process 700 may be performed in order other than... Figure 7A Any order other than the one shown will be executed.

[0136] In some embodiments, process 700 may be initiated by controller 111 to determine a threshold signal-to-noise ratio (SNR) intensity level based on preliminary sample data collected from a short scan of the sample in operation 604, wherein the preliminary sample data includes bright Raman spectral data and dark Raman spectral data, as described in process 600 above. In some embodiments, the threshold SNR intensity level is calculated by subtracting the maximum brightness intensity value (e.g., determined from operation 604) from the dark Raman spectral value corresponding to the maximum brightness intensity value, thereby providing the difference between the bright Raman spectral data and the dark Raman spectral data.

[0137] In some implementations, controller 111 uses an SNR estimate to determine a threshold SNR intensity level. In some implementations, the SNR estimate is selected from the group consisting of: (i) the original bright / dark spectral data, (ii) a first Savitzky-Golay derivative transform associated with the original bright / dark spectral data, (iii) a second Savitzky-Golay derivative transform associated with the original bright / dark spectral data, or (iv) an SNR estimate known in the art.

[0138] For example, in some implementations, the SNR is calculated using the following formula: SNR=normderivsignalsqrt(derivread noise+thermal noise+dark noise+shot noise)-1 derivX=first derivative of savitzky Gollay filter of X In some implementations, process 700 may include controller 111 generating an SNR linear model based at least on a threshold SNR intensity value. The reference threshold SNR intensity value is associated with at least the initial sample data. Since SNR is zero when there is no intensity (signal), a straight line is drawn through the origin. It should be understood that controller 111 does not necessarily display the plot or any straight line; although the plot may be displayed on display 112, what is actually operated by controller 111 (e.g., an electronic processor) is data representing the plot (e.g., an array containing intensity values ​​and corresponding energy values), which is stored in one or more data storage devices included in controller 111.

[0139] In some implementations, process 700 may include a controller 111 that transforms the domain of the SNR linear model. The domain of the SNR linear model is transformed from intensity to SNR so that the SNR linear model is represented as intensity levels of SNR values. As previously stated, it should be understood that controller 111 does not need to display a plot to the user of the spectral system 110.

[0140] In operation 702, controller 111 determines an SNR-intensity model, which includes multiple SNR values ​​and multiple intensities based at least on one or more deviations relative to a linear SNR model, and one or more deviations associated with the determined performance level. For simplicity, exemplary performance levels have been described in detail above.

[0141] In some implementations, controller 111 applies one or more biases to the SNR linear model, thereby providing a modified calibration sample based on the SNR linear model. One or more biases are described below. Figure 7B This will be discussed in more detail later.

[0142] In some implementations, controller 111 converts the domain of the SNR linear model from SNR values ​​to intensity levels to form an SNR-intensity model that includes the modified calibration sample.

[0143] For reference Figure 7BThe deviation is determined using calibration samples. For clarity, the data for the second, third, and fourth calibration samples are omitted from the figure, but the processing is similar. A linear line is generated using the baseline signal-to-noise ratio and the signal-to-noise ratio corresponding to a 100-millisecond scan for each calibration sample. The baseline signal-to-noise ratio is zero because there is no intensity at zero seconds (e.g., no signal). Calibration samples from calibration sample zero to calibration sample five are provided, schematically shown as follows: linear line 752 is generated for calibration sample zero of category 0, linear line 754 is generated for calibration sample one of category 1, and linear line 756 is generated for calibration sample five of category 5. The signal-to-noise ratios at higher intensities are then plotted, for example, those used to generate... Figure 6B The signal measured using the data cited in the text. The maximum brightness value increases with increasing scan time, and the signal-to-noise ratio (SNR) also increases with increasing scan time; therefore, the SNR increases with increasing maximum brightness value. A curve showing the relationship between SNR and intensity was then fitted to the data. Line 758 corresponds to calibration sample 0, line 760 corresponds to calibration sample 1, and line 762 corresponds to calibration sample 5. Figure 7B In the diagram, straight line 758 corresponds to calibration sample 0, curve 760 corresponds to calibration sample 1, and curve 762 corresponds to calibration sample 5. The differences between the data from the linear extrapolation lines 752, 754, and 756 and the data from curves 758, 760, and 762 are calculated, and a graph showing the relationship between the difference and the intensity is plotted for each calibration sample. These graphs are shown in... Figure 7B In the figure below, the difference versus intensity plots are fitted to a curve. These curves (e.g., for modified calibration samples) are schematically represented as: curve 764 for calibration sample 0, curve 766 for calibration sample 1, and curve 768 for calibration sample 5. Any fitting can be performed to match the data trend, depending on the scattering intensity response of the Raman excitation of the calibration sample. In some implementations, the best curve fit is logarithmic (natural logarithmic fit), which takes the form of equation (2), as follows: (2), in It is the SNR difference for performance level y, A y and C y is the linear fitting coefficient, and SNR is the signal-to-noise ratio.

[0144] Controller 111 is used to determine the deviation of the sample from the linearity of the signal-to-noise ratio (SNR) based on the deviation of the model of this category, thereby creating a signal-to-noise ratio-intensity model. For example, for a reference... Figure 7B The deviation mentioned above is the difference defined by equation (2). ), and uses the coefficients of that category. In some implementations, controller 111 uses deviation rate subtraction to determine the deviation of the sample relative to the SNR linearity. Controller 111 then calculates multiple signal-to-noise ratio (SNR) values ​​(( The logarithmic deviation relative to the linear model is then subtracted from the corresponding multiple SNR values ​​of the linear line (e.g., by deviation rate subtraction). For example, (i) the sample has a linear value of 100 and a deviation value of 75 on the SNR linear model (e.g., a determined SNR value of 25). Another example is the sample having a linear value of 110 and a deviation value of 85 on the SNR linear model (e.g., a determined SNR value of 35). The logarithmic deviation relative to the linear model is then generated based at least on these determined multiple SNR values. The logarithmic deviation relative to the linear model is one implementation of the SNR-intensity model.

[0145] In operation 704, controller 111 determines an SNR value based on an intensity level or a second maximum intensity level. The SNR value based on an intensity level is determined at least based on a second corresponding intensity level, while the second maximum intensity level is determined at least based on a first corresponding SNR value.

[0146] In some implementations, the input received by controller 111 includes at least a set of program instructions for identifying an intensity-level-based SNR value based on a second corresponding intensity level. Controller 111 analyzes the SNR-intensity model and identifies the intensity-level-based SNR value based at least on the received second corresponding intensity level.

[0147] In some implementations, controller 111 receives input comprising at least a set of program instructions to identify a second maximum intensity level based at least on a first corresponding SNR value. Controller 111 analyzes the SNR-intensity model and identifies the second maximum intensity level based at least on the first corresponding SNR value. As previously described, there exists a direct mapping between scan time, corresponding intensity, and corresponding SNR, which can be determined using an intensity-time model and an SNR-intensity model.

[0148] In some implementations, controller 111 determines an intensity-level-based SNR value based at least on a second corresponding intensity level. Controller 111 receives third sample data collected from a third scan of the sample, wherein the sample is scanned at the second corresponding intensity level.

[0149] In some implementations, controller 111 determines a second maximum intensity level. Controller 111 receives fourth sample data collected from a fourth scan of the sample, wherein the sample is scanned at a first corresponding SNR value.

[0150] In operation 706, controller 111 stores the SNR value based on the intensity level or the second maximum intensity level in one or more data storage devices included in controller 111. In some embodiments, operation 714 may further include controller 111 receiving a set of program instructions for displaying (i) the SNR value based on the intensity level and (ii) the second maximum intensity level.

[0151] For example, controller 111 can scan the sample using an intensity-level-based SNR value determined based on a second corresponding intensity level, thereby obtaining a spectrum with an SNR amplitude approximately equal to the intensity-level-based SNR value and a second parameter intensity level. As another example, controller 111 can scan the sample using a second maximum intensity level based on a first corresponding SNR value, thereby obtaining a spectrum with a maximum intensity level approximately equal to the amplitude of the second maximum intensity level and the first corresponding SNR value. Through this process, spectra with defined SNR values ​​or intensities can be collected, and these spectra can be used for qualitative and / or quantitative identification of the sample.

[0152] Now for reference Figure 8 This figure is a flowchart of a process 800 for determining additional measurement parameters of a sample compound according to some embodiments. As described above, process 800 can be implemented using a spectroscopic system 110. Process 800 is described herein as being executed via a controller 111. However, it should be understood that process 800 can be executed by multiple software and / or hardware components in various combinations and configurations. Figure 8 As shown, process 800 may include operation 802, operation 804, or operation 806. In some embodiments, process 800 follows... Figure 8 The process is executed in the order shown. In some implementations, process 800 may be performed in order other than... Figure 8 Perform in any order other than the one shown.

[0153] Unless otherwise stated, process 800 may include operations 602 to 612, and operations 802 to 806.

[0154] In some embodiments, process 800 may begin with operation 802, where controller 111 determines a threshold SNR intensity value based on a first maximum intensity level. As described above, the first maximum intensity level determined in operation 610 can be used to determine the threshold SNR intensity level. In some embodiments, the threshold SNR intensity level is calculated by subtracting the first maximum intensity value (e.g., determined from operation 610) from the dark Raman spectral value corresponding to the first maximum intensity value, thereby providing the difference between the bright Raman spectral data and the dark Raman spectral data.

[0155] In various implementations, the controller 111 determines the σ reading and bias (e.g., CCD bias) based at least on dark Raman spectral data.

[0156] The controller 111 determines the threshold SNR intensity level by using a signal-to-noise ratio (SNR) estimate. In some embodiments, the SNR estimate is selected from the group consisting of: (i) the original bright / dark spectral data, (2) a first Savitzky-Golay derivative transform associated with the original bright / dark spectral data, (3) a second Savitzky-Golay derivative transform associated with the original bright / dark spectral data, or (4) an SNR estimate known in the art.

[0157] In some implementations, controller 111 generates a linear SNR model containing the calibration sample, based at least on a threshold SNR intensity value. This threshold SNR value is associated with at least the initial sample data. The straight line is drawn through the origin because the SNR is zero when there is no intensity (signal). It should be understood that controller 111 does not necessarily display a plot or any straight line; although the plot may be displayed on display 112, what is actually operated by controller 111 (e.g., an electronic processor) is data representing the plot (e.g., an array containing intensity values ​​and corresponding energy values), which is stored in one or more data storage devices included in controller 111.

[0158] In some implementations, controller 111 transforms the domain of the SNR linear model. The domain of the SNR linear model is transformed from SNR to intensity, so that the SNR linear model can be represented as the SNR value corresponding to the intensity level. As previously mentioned, it should be understood that controller 111 does not need to display or demonstrate plots to the user of the spectral system 110.

[0159] In operation 802, controller 111 determines an SNR-intensity model. This SNR-intensity model includes multiple SNR values ​​and multiple intensities, which are based at least on one or more logarithmic deviations generated from a linear SNR model. The one or more logarithmic deviations are associated with a determined performance level.

[0160] In some implementations, controller 111 applies one or more deviations to the SNR linear model, thereby providing a modified calibration sample based on the SNR linear model. One or more deviations in Figure 7B This will be discussed in more detail later.

[0161] In some implementations, controller 111 converts the domain of the SNR linear model from SNR values ​​to intensity levels to form an SNR-intensity model that includes the modified calibration sample.

[0162] In some implementations, the controller 111 is at least based on a reference Figure 7B The calibration sample is used to determine the deviation of the sample from a linear straight line. In some embodiments, this deviation may take the form of equation (2) shown above.

[0163] The controller 111 determines the deviation of the sample from the SNR linear line based on at least a plurality of SNR values ​​and a plurality of intensities, as previously described. In some embodiments, the controller 111 uses deviation rate subtraction to determine the deviation of the sample from the SNR linear line, as discussed in process 700 above. Thus, an SNR-intensity model is generated based at least on these determined plurality of SNR values.

[0164] In operation 804, controller 111 determines an SNR value based on exposure time or a second parameter exposure time. The SNR value based on exposure time is determined at least based on a second corresponding exposure time level, and the second parameter exposure time is determined at least based on a second corresponding SNR value.

[0165] In some implementations, the input received by controller 111 includes at least a set of program instructions for identifying an exposure time-based SNR value, at least based on a second corresponding exposure time. Controller 111 is used to analyze the SNR-intensity model and the intensity-time model and identify the exposure time-based SNR value.

[0166] In some implementations, the input received by controller 111 includes at least a set of program instructions for identifying the second parameter exposure time based at least on a second corresponding intensity level. Controller 111 is used to analyze the SNR-intensity model and the intensity-time model and identify the first parameter exposure time.

[0167] In some implementations, controller 111 determines an SNR value based on exposure time. Controller 111 receives fifth sample data collected from a fifth sample scan of the sample, wherein the sample is scanned with a second corresponding exposure time.

[0168] In some implementations, controller 111 determines the second parameter, exposure time. Controller 111 receives sixth sample data collected from the sixth sample scan of the sample, wherein the sample is scanned at a second corresponding SNR value.

[0169] In operation 806, controller 111 stores an SNR value based on exposure time or a second parameter, exposure time. In some embodiments, controller 111 receives a set of program instructions for displaying the SNR value based on exposure time or the second parameter, exposure time, to a user on display 112. In some embodiments, operation 808 may further include controller 111 receiving a set of program instructions for displaying (i) the SNR value based on exposure time and (ii) the second parameter, exposure time.

[0170] For example, controller 111 can then scan the sample using an exposure time-based SNR value determined based on a second corresponding exposure time, thereby obtaining a spectrum with an SNR amplitude approximately equal to that exposure time-based SNR value within the second corresponding exposure time. As another example, controller 111 can scan the sample using a second parameter exposure time based on the second corresponding SNR value, thereby obtaining a sample with an exposure time-based SNR value within a time approximately equal to the second parameter exposure time. This process can collect a spectrum with a determined SNR value within a known time period, or the time required to collect the spectrum to achieve a target SNR value can be determined.

[0171] III. Experimental Data

[0172] A. Exemplary performance level

[0173] In some implementations, the experimental data described below in this section includes at least Figures 6 to 6 above. Figure 8 Any of the operating methods described herein.

[0174] Figure 9 This includes a table 900 showing an exemplary performance level hierarchy, along with associated lower and upper limits for maximum luminance intensity values. For example, as shown, there are six possible performance levels: Performance Level 0, Performance Level 1, Performance Level 2, Performance Level 3, Performance Level 4, and Performance Level 5. Furthermore, a range of maximum luminance intensity values ​​is assigned to each individual performance level. Performance Level 0 includes maximum luminance intensity values ​​from 0 to 349. Performance Level 1 includes maximum luminance intensity values ​​from 350 to 499. Performance Level 2 includes maximum luminance intensity values ​​from 500 to 699. Performance Level 3 includes maximum luminance intensity values ​​from 700 to 1099. Performance Level 4 includes maximum luminance intensity values ​​from 1100 to 1999. Performance Level 5 includes maximum luminance intensity values ​​from 2000 up to when the detector of the analytical instrument support device is fully saturated.

[0175] The exemplary systems and methods are not limited to Figure 9The exemplary systems and methods may include or define any number of performance levels (e.g., one or more performance levels and / or multiple performance levels). Furthermore, the exemplary systems and methods are not limited to any particular range of maximum luminance intensity values ​​associated with each individual performance level. For example, there may be a performance level whose maximum luminance intensity values ​​range from 0 to complete saturation of the detector 117 of the analytical instrument support device. In another non-limiting example, there may be two performance levels, where performance level 0 includes maximum luminance intensity values ​​from 0 to 599, and performance level 1 includes maximum luminance intensity values ​​from 600 to complete saturation of the detector of the analytical instrument support device. In yet another non-limiting example, there may be 100 performance levels, where the maximum luminance intensity values ​​of each individual performance level increase by 10 (e.g., performance level 0 includes maximum luminance intensity values ​​from 0 to 10, performance level 1 includes maximum luminance intensity values ​​from 11 to 20, performance level 2 includes maximum luminance intensity values ​​from 21 to 30, and so on).

[0176] B. Exemplary Intensity-Time Model and Signal-to-Noise Ratio (SNR)-Intensity Model

[0177] In some embodiments, the various sample compounds provided in Table 1 below can be used as samples in one or more of the operating methods described in Figures 6-8 above.

[0178] Exemplary systems and methods tested a variety of sample compounds and determined the maximum luminance intensity level based at least on the scanned sample data and performance class, as shown in Table 1.

[0179] Table 1

[0180] Figure 10A An exemplary strength-time model for an exemplary sample compound: L-histidine hydrochloride monohydrate is shown. The performance level of the exemplary sample compound: L-histidine hydrochloride monohydrate is determined to be level 0. Figure 10A The linear curve for scanning the sample compound, L-histidine hydrochloride monohydrate (e.g., the calibration sample), is further illustrated, along with a modified calibration sample (e.g., an intensity-time model) incorporating multiple intensity levels and exposure times based on an exponential deviation relative to the linear curve. The coefficients for the exponential deviation are A1 = 0.000473, t1 = 0.000244, and c1 = -0.02757, obtained as previously described using the calibration sample, which selected calcium carbonate as the compound being scanned.

[0181] Figure 10BAn exemplary SNR-intensity model for an exemplary sample compound: L-histidine hydrochloride monohydrate is shown. The performance level of the exemplary sample compound: L-histidine hydrochloride monohydrate is determined to be level 0. Figure 10B The linear SNR of the scanned sample compound, L-histidine hydrochloride monohydrate (e.g., a calibration sample), is further illustrated, along with a modified calibration sample (e.g., an SNR-intensity model) comprising multiple SNR values ​​and multiple intensities based on the logarithmic deviation relative to the linear SNR. The coefficients for the logarithmic deviation are A1 = 0.72864 and c1 = -0.1981.

[0182] Figure 11A An exemplary strength-time model of an exemplary sample compound, lactose monohydrate, is shown. The performance level of the exemplary sample compound, lactose monohydrate, is determined to be level 1. Figure 11A The linear curve of the scanned sample compound, lactose monohydrate (e.g., a calibration sample), is further illustrated, along with a modified calibration sample (e.g., an intensity-time model) that includes multiple intensity levels and multiple exposure times based on exponential deviations from the linear curve.

[0183] Figure 11B An exemplary SNR-intensity model for an exemplary sample compound: lactose monohydrate is shown. The performance level of the exemplary sample compound: lactose monohydrate is determined to be level 1. Figure 11B Further illustrations show the scanned sample: a linear SNR of lactose monohydrate (e.g., a calibration sample), and a modified calibration sample (e.g., an SNR-intensity model) comprising multiple SNR values ​​and multiple intensities based on the logarithmic deviation relative to the linear SNR.

[0184] The exemplary systems and methods specify that when the determined performance level is performance level 2 or higher (e.g., maximum luminance intensity level 500 or higher), the predictive model improves the accuracy of determining the measurement parameters of the scanned sample. Reference Figures 12A to 14B The exemplary systems and methods determine the measurement parameters of the scanned sample with higher accuracy.

[0185] Figure 12A An exemplary strength-time model of an exemplary sample compound, paracetamol, is shown. The performance level of the exemplary sample compound, paracetamol, is determined to be level 2. Figure 12A Further illustrations show a scanned sample: a linear line of acetaminophen (e.g., a calibration sample), and a modified calibration sample (e.g., an intensity-time model) that includes multiple intensity levels and multiple exposure times based on exponential deviations relative to the linear line.

[0186] Figure 12B An exemplary SNR-intensity model for an exemplary sample compound, paracetamol, is shown. The performance level of the exemplary sample compound, paracetamol, is determined to be level 2. Figure 12B Further illustrations show a scanned sample: a linear SNR curve for acetaminophen (e.g., a calibration sample), and a modified calibration sample (e.g., an SNR-intensity model) comprising multiple SNR values ​​and multiple intensities based on the logarithmic deviation relative to the linear SNR curve.

[0187] Figure 13A An exemplary strength-time model of an exemplary sample compound, methanol, is shown. The performance level of the exemplary sample compound, methanol, is determined to be level 3. Figure 13A Further illustrations show a linear curve of methanol (e.g., a calibration sample) and a modified calibration sample (e.g., an intensity-time model) that includes multiple intensity levels and multiple exposure times based on exponential deviations from the linear curve.

[0188] Figure 13B An exemplary SNR-intensity model for an exemplary sample compound, methanol, is shown. The performance level of the exemplary sample compound, methanol, is determined to be level 3. Figure 13B Further illustrations show the SNR linearity of a scanned sample: methanol (e.g., a calibration sample), and a modified calibration sample (e.g., an SNR-intensity model) including multiple SNR values ​​and multiple intensities based on exponential deviations relative to the SNR linearity.

[0189] Figure 14A An exemplary intensity-time model of an exemplary sample compound, cyclohexane, is shown. The performance level of the exemplary sample compound, cyclohexane, is determined to be level 5. Figure 14A Further illustrations show a linear curve for scanning samples of cyclohexane (e.g., a calibration sample), and a modified calibration sample (e.g., an intensity-time model) including multiple intensity levels and multiple exposure times based on exponential deviations relative to the linear curve. The exponential deviation coefficient for level 5 is based on A5 = 0.027061 and t5 = 8.23 ​​x 10⁻⁶. -5 And a calibration sample with c5 = -0.03037. In this example, both the calibration sample and the test sample are cyclohexane.

[0190] Figure 14B An exemplary SNR-intensity model for an exemplary sample compound: cyclohexane is shown. The performance level of the exemplary sample compound: cyclohexane is determined to be level 5. Figure 14BFurther illustrations show the scanned sample: a linear SNR curve for cyclohexane (e.g., a calibration sample), and a modified calibration sample (e.g., an SNR-intensity model) including multiple SNR values ​​and multiple intensities based on exponential deviations relative to the linear SNR curve. The logarithmic deviation coefficients for cyclohexane are A5 = 0.335944 and c5 = -1.37313.

[0191] In some implementations, controller 111 receives a set of program instructions for displaying communication content on, for example, a display screen, informing the user of determined values, including at least a first maximum intensity level of the scanned sample, a first parameter exposure time, a first SNR value, a second maximum intensity level, a second SNR value, or a second parameter exposure time. Alternatively, controller 111 may also output the determined values ​​to one or more external devices, networks, or data storage devices. This data may be provided in its original form, as part of a report, or a combination of both. One or more alarms may also be generated based on the determined values, for example, to warn the user of errors, sample out-of-range conditions, or other similar situations.

[0192] Figure 15A This is a block diagram illustrating the steps a device operator (e.g., an operator of a handheld Raman spectrometer) can take to exemplify the advantages of the methods described herein. In operation 1502, controller 111 receives a set of program instructions for a specific desired SNR level. These instructions may be provided by controller 111, for example, from data in the storage components of one or more data storage devices included with controller 111, or as input from a device user. The controller initiates a short scan 1504 (e.g., similar to operation 602). This initial short scan 1504 is used to create an intensity-time and SNR-intensity model, as described herein, and then uses this model to determine the scan time to obtain the desired SNR. Controller 111 executes this determined time in a longer scan operation 1506. In operation 1508, a spectrum with the target SNR is provided. Alternatively, the intensity-time model and the SNR-intensity model can be used to determine the maximum peak intensity required to achieve the desired SNR.

[0193] Figure 15BThis is a block diagram, which also exemplarily illustrates the improved effect of the method described herein. In operation 1512, controller 111 receives a set of program instructions for a specific desired maximum intensity level. These instructions may be provided by the controller, for example, from data in the storage components of one or more data storage devices included in controller 111, or from data from a device user. The controller initiates a short scan 1504. This initial short scan 1504 is used to create an intensity-time and SNR-intensity model, as described herein, and then uses this model to predict the scan time to obtain the desired maximum intensity. Controller 111 executes this determined time in a longer scan operation 1516. In operation 1508, a spectrum with the target SNR is provided.

[0194] The methods described herein (see reference) Figure 15A and 15B When implemented, for example in system 100 ( Figure 1 On a Raman spectroscopy system, the operator may only need to perform two actions: point the Raman laser at the sample and press the acquisition button, subsequently obtaining a spectrum with the desired SNR or maximum intensity. Additional steps may involve inputting the target SNR or target maximum intensity, but typically these specific SNR values ​​are pre-programmed, and the operator does not need to know the specific numerical values ​​providing the spectrum. In other words, the methods and systems described herein offer an improved and efficient way to acquire data, such as Raman spectra.

[0195] Therefore, the embodiments described herein provide systems, methods, computing and storage devices, and computer-readable media for sample analysis, such as determining the SNR value of a sample. As mentioned above, the embodiments described herein can achieve performance improvements over existing prediction-based Raman spectroscopy techniques while being efficient and cost-effective (e.g., without requiring complex and expensive analytical instruments). For example, by performing a short-time scan of the sample (e.g., 100 milliseconds), exemplary systems and methods can predict the shortest exposure time required to achieve an SNR value of 100, thereby generating more accurate predictions, improving customer satisfaction, and building customer trust through exemplary systems and methods.

[0196] As described above, and other potentially related prior art problems, can be effectively solved by the various examples, aspects, features, and implementations of the exemplary systems and methods for prediction-based Raman spectroscopy disclosed herein. Therefore, integrating prediction-based Raman spectroscopy into systems and methods for predicting intensity levels and SNR from an initial scan (e.g., 100 ms) may present technical problems, which the embodiments described herein address through specific computing systems and devices and computer-based prediction models. Thus, the embodiments disclosed herein provide improvements to prediction-based Raman spectroscopy.

[0197] As described in the detailed description above, reference has been made to the accompanying drawings, which form part of this specification, in which the same numbers always denote the same components, and the drawings illustrate possible implementations by way of example. It should be understood that other implementations may be adopted, and structural or logical changes may be made without departing from the scope of this disclosure. Therefore, the detailed description above should not be considered as a binding interpretation.

[0198] The various operations can be described sequentially as multiple discrete actions or operations in a manner most conducive to understanding the subject matter disclosed herein. However, the described order should not be construed as implying that these operations must depend on a specific order. In particular, these operations may not be performed in the order presented. The described operations may be performed in a different order than the specific implementation described. Various other operations may be performed, and / or the described operations may be omitted in other specific implementations.

[0199] Terms and Conditions

[0200] Implementation of this disclosure is disclosed in the following terms: Clause 1 A computer-implemented method in an analytical instrument support device, the method comprising: Preliminary sample data collected via a short sample scan is received by one or more processors; The maximum luminance intensity level is determined by one or more processors based at least on preliminary sample data; The performance level is determined by one or more processors based at least on the maximum luminance intensity level; An intensity-time model is determined by one or more processors, the intensity-time model comprising multiple intensity levels and multiple exposure times based on at least one or more deviations of an intensity linear model, the one or more deviations being associated with a determined performance level.

[0201] Determined by one or more processors The first prediction represents, at least based on the first corresponding exposure time and the first maximum intensity level of the intensity-time model, or The second prediction is based at least on the first corresponding intensity level and the first parameter exposure time of the intensity-time model; and The first maximum intensity level or the first parameter exposure time is stored on one or more computer-readable storage devices.

[0202] Clause 2 The computer-implemented method according to Clause 1, wherein determining the first prediction includes receiving first sample data collected from a first scan of the sample by one or more processors, wherein the sample is scanned at a first corresponding exposure time.

[0203] Clause 3. A computer-implemented method according to Clause 1 or Clause 2, wherein determining the second prediction includes receiving second sample data collected from a second scan of the sample by one or more processors, wherein the sample is scanned to a first corresponding intensity level.

[0204] Clause 4. The computer-implemented method according to any one of Clauses 1-3, wherein the analytical instrument support device is a Raman spectrometer.

[0205] Clause 5 The computer-implemented method according to any one of Clauses 1-4, the method further comprising: An intensity linear model is generated by one or more processors, based at least on the maximum luminance intensity level associated with the preliminary sample data; One or more processors convert the domain of the generated intensity linear model from exposure time to intensity; One or more biases are applied to the intensity linear model by one or more processors; and One or more processors convert the domain of the intensity-linear model from intensity to exposure time to form an intensity-time model; Clause 6. A computer-implemented method according to any one of Clauses 1-5, wherein one or more deviations from the intensity linear model include exponential deviations.

[0206] Clause 7. A computer-implemented method according to any one of Clauses 1-6, wherein determining the intensity-time model comprises: Iterate through multiple intensity levels until a first corresponding exposure time is reached in the intensity-time model, where the intensity level corresponding to the first corresponding exposure time is the first maximum intensity level, or Iterate through multiple exposure times until the first corresponding intensity level is reached on the intensity-time model, where the exposure time corresponding to the first corresponding intensity level is the first parameter exposure time.

[0207] Clause 8 The computer-implemented method according to any one of Clauses 1-7, the method further comprising: One or more processors retrieve one or more device characteristics of the analytical instrument supporting the device, wherein the device characteristics of the analytical instrument include bias, gain, and σ readings; and The baseline intensity level is determined by one or more processors based on one or more analytical instrument features.

[0208] Clause 9 The computer-implemented method according to any one of Clauses 1-8, the method further comprising: An intensity linear model is generated by one or more processors, based at least on (i) a baseline intensity level and (ii) a maximum luminance intensity level at a given exposure time, which is between 1 millisecond and 20 seconds.

[0209] Clause 10 A computer-implemented method according to any one of Clauses 1-9, wherein determining the performance category of a short scan of a sample includes determining the performance category based on a result selected from a plurality of performance categories. These multiple performance categories are based at least on the maximum brightness intensity level of the sample during short scans with exposure times ranging from 1 millisecond to 20 seconds, and the intensity count between 0 and the detector saturation value of the analytical instrument support device.

[0210] Clause 11 The computer-implemented method according to any one of Clauses 1-10, the method further comprising: An SNR-intensity model is determined by one or more processors, the SNR-intensity model including multiple SNR values ​​and multiple intensity values, based at least on one or more deviations from a linear SNR model, the one or more deviations being associated with a determined performance level; Determined by one or more processors The third prediction represents at least the intensity-level SNR value based on the second corresponding intensity level, or The fourth prediction represents the second maximum intensity level based at least on the first corresponding SNR value; and The SNR value based on the intensity level or the second maximum intensity level is stored on one or more computer-readable storage devices.

[0211] Clause 12 The computer-implemented method according to Clause 11, wherein determining the third prediction includes receiving third sample data collected from a third scan of the sample by one or more processors, wherein the sample is scanned to a second corresponding intensity level.

[0212] Clause 13 The computer-implemented method according to Clause 11, wherein determining the fourth prediction includes receiving fourth sample data collected from a fourth scan of the sample by one or more processors, wherein the sample is scanned to a first corresponding SNR value.

[0213] Clause 14 The computer-implemented method according to any one of Clauses 1-13, the method further comprising: An SNR-intensity model is determined by one or more processors. This model includes multiple SNR values ​​and multiple intensity values, based at least on one or more logarithmic deviations from a linear SNR model, which are associated with the determined performance level. The fifth prediction represents the exposure-time-based SNR value, based at least on the second corresponding exposure time, the SNR-intensity model, and the intensity-time model, or The sixth prediction represents the exposure time as a second parameter, based at least on the second corresponding SNR value, the SNR-intensity model, and the intensity-time model; and Store the SNR value based on exposure time or the second parameter exposure time on one or more computer-readable storage devices.

[0214] Clause 15 The computer-implemented method according to Clause 14, wherein determining the fifth prediction includes receiving fifth sample data collected from a fifth scan of the sample by one or more processors, wherein the sample is scanned at a second corresponding exposure time.

[0215] Clause 16 The computer-implemented method according to Clause 14, wherein determining the sixth prediction includes receiving sixth sample data collected from a sixth scan of the sample by one or more processors, wherein the sample is scanned to a second corresponding SNR value.

[0216] Clause 17 The computer-implemented method according to Clause 14 further includes: One or more processors determine a threshold signal-to-noise ratio (SNR) intensity value based on preliminary sample data, wherein the preliminary sample data includes dark Raman spectral data; A linear SNR model is generated by one or more processors, based at least on the threshold SNR intensity value; One or more processors convert the domain of the generated SNR linear model from intensity level to SNR; Apply the one or more biases to the SNR linear model; and One or more processors convert the domain of the SNR linear model from SNR to intensity level to form an SNR-intensity model.

[0217] Clause 18 One or more non-transitory computer-readable media having instructions stored thereon that, when executed by one or more processing devices of an analytical instrument support apparatus, cause the analytical instrument support apparatus to perform the method described in Clause 1.

[0218] Clause 19 An analytical instrument support system, comprising: One or more processors, One or more non-transitory computer-readable storage media; and Program instructions stored on one or more non-transitory computer-readable storage media, for execution by one or more processors, the program instructions comprising: Program instructions to receive preliminary sample data collected from a short scan of the sample; Program instructions to determine the maximum luminance intensity level based at least on the preliminary sample data; Program instructions that determine the performance level based at least on the maximum luminance intensity level; The program instructions for determining an intensity-time model, which includes multiple intensity levels and multiple exposure times, based at least on one or more deviations from an intensity linear model, said one or more deviations being associated with a determined performance level; Determine the following program instructions: The first prediction represents, at least based on the first corresponding exposure time and the first maximum intensity level of the intensity-time model, or The second prediction is based at least on the first corresponding intensity level and the first parameter exposure time of the intensity-time model; and The program instructions store the first maximum intensity level or the first parameter exposure time in one or more computer-readable storage devices.

[0219] Clause 20 The analytical instrument support system as described in Clause 19, wherein the program instructions are executed on a general-purpose computing device, which includes at least one or more processors.

[0220] Clause 21 Analytical instrument support system as described in Clause 19 or Clause 20, wherein the program instructions are executed on a computing device remote from the analytical instrument, the computing device including at least one or more processors.

[0221] Clause 22 Analytical instrument support system according to any one of Clauses 19-21, wherein the program instructions are executed on a user computing device, the user computing device including at least one or more processors.

[0222] Clause 23 The analytical instrument support system as described in Clause 20, wherein the program instructions are executed on the analytical instrument, which includes one or more processors.

[0223] Clause 24 An analytical instrument, said analytical instrument comprising: A light source configured to illuminate the surface of a sample; A spectrometer is used to obtain the Raman spectrum of a sample surface in response to a light source illuminating the sample surface. One or more processors; One or more non-transitory computer-readable storage media; and Program instructions stored on one or more non-transitory computer-readable storage media, for execution by at least one of one or more processors, wherein when at least one of the one or more processors executes the program instructions, it causes the analytical instrument to perform a set of operations, including: Analyze the Raman spectral data from the collected Raman spectra related to the sample surface. The maximum brightness intensity level should be determined at least based on the acquired Raman spectra. The performance level is determined based on the maximum brightness intensity level associated with the acquired Raman spectrum. A strength-time model is determined, which includes multiple strength levels and multiple exposure times based on at least one or more deviations from a strength linear model, the one or more deviations being associated with a determined performance level.

[0224] The first maximum intensity level is determined at least based on the first corresponding exposure time and the intensity-time model, or the first parameter exposure time is determined at least based on the first corresponding intensity level and the intensity-time model.

[0225] The first maximum intensity level or the first parameter exposure time is stored in at least one of the one or more non-transitory computer-readable storage media.

[0226] Clause 25 The analytical instrument as described in Clause 24, wherein the set of operations further includes: Determine an SNR-intensity model, which includes multiple SNR values ​​and multiple intensity values, based at least on one or more deviations from an SNR linear model, the one or more deviations being associated with the determined performance level; At least based on The second corresponding intensity level determines the SNR value based on the intensity level, or The second maximum intensity level is determined at least based on the first corresponding SNR value; and The SNR value based on the intensity level or the second maximum intensity level is stored on one or more computer-readable storage devices.

[0227] Clause 26 The analytical instrument as described in Clause 25, wherein the set of operations further includes: Determine an SNR-intensity model, which includes multiple SNR values ​​and multiple intensity values, based at least on one or more logarithmic deviations from a linear SNR model, the one or more logarithmic deviations being associated with the determined performance level; At least based on The second method, using exposure time, the SNR-intensity model, and the intensity-time model, determines the SNR value based on exposure time. The second parameter, exposure time, is determined at least based on the second corresponding SNR value, the SNR-intensity model, and the intensity-time model; and Store the SNR value based on exposure time or the second parameter exposure time on one or more computer-readable storage devices.

[0228] Clause 27 The analytical instrument as described in Clause 26, wherein the set of operations further includes: The threshold signal-to-noise ratio (SNR) intensity value is determined based on preliminary sample data, which includes dark Raman spectral data. Generate an SNR linear model based at least on the SNR linear model and at least on the SNR intensity threshold.

[0229] The domain of the generated SNR linear model is converted from intensity level to SNR; Apply the one or more biases to the SNR linear model; and The domain of the bias provided from the SNR linear model is converted from SNR to intensity level to form an SNR-intensity model.

Claims

1. A computer-implemented method in an analytical instrument support device, the method comprising: Preliminary sample data collected via a short sample scan is received by one or more processors; The maximum luminance intensity level is determined by one or more processors based at least on preliminary sample data; The performance level is determined by one or more processors based at least on the maximum luminance intensity level; An intensity-time model is determined by one or more processors, the intensity-time model comprising multiple intensity levels and multiple exposure times based on at least one or more deviations of an intensity linear model, the one or more deviations being associated with a determined performance level; Determined by one or more processors The first prediction represents, at least based on the first corresponding exposure time and the first maximum intensity level of the intensity-time model, or The second prediction is based at least on the first corresponding intensity level and the first parameter exposure time of the intensity-time model; as well as The first maximum intensity level or the first parameter exposure time is stored on one or more computer-readable storage devices.

2. The computer-implemented method of claim 1, wherein determining the first prediction includes receiving first sample data collected from a first scan of the sample by the one or more processors, wherein the sample was scanned for a first corresponding exposure time.

3. The computer-implemented method of claim 1, wherein determining the second prediction comprises receiving second sample data collected from a second scan of the sample by the one or more processors, wherein the sample is scanned to a first corresponding intensity level.

4. The computer-implemented method according to claim 1, wherein the analytical instrument support device is a Raman spectrometer.

5. The computer-implemented method according to claim 1, wherein the method further comprises: An intensity linear model is generated by one or more processors, based at least on the maximum luminance intensity level associated with the preliminary sample data; One or more processors convert the domain of the generated intensity linear model from exposure time to intensity; One or more biases are applied to the intensity linear model by one or more processors; as well as One or more processors convert the domain of the intensity linear model from intensity to exposure time to form an intensity-time model.

6. The computer-implemented method of claim 5, wherein one or more deviations relative to the intensity linear model include exponential deviations.

7. The computer-implemented method according to claim 1, wherein determining the intensity-time model comprises: Iterate through multiple intensity levels until a first corresponding exposure time is reached in the intensity-time model, where the intensity level corresponding to the first corresponding exposure time is the first maximum intensity level, or Iterate through multiple exposure times until the first corresponding intensity level is reached on the intensity-time model, where the exposure time corresponding to the first corresponding intensity level is the first parameter exposure time.

8. The computer-implemented method according to claim 5, wherein the method further comprises: One or more processors retrieve one or more device characteristics of the analytical instrument supporting the device, wherein the device characteristics of the analytical instrument include bias, gain, and σ readings; and The baseline intensity level is determined by one or more processors based on one or more analytical instrument features.

9. The computer-implemented method of claim 8, the method further comprising generating an intensity linear model by one or more processors, which is at least based on (i) a baseline intensity level and (ii) a maximum luminance intensity level at a certain exposure time, wherein the exposure time is between 1 millisecond and 20 seconds.

10. The computer-implemented method of claim 1, wherein the determination of the performance level of the short scan of the sample is based on selection from a plurality of performance levels, and The aforementioned multiple performance levels are based at least on the maximum brightness intensity level of the sample during short scans with exposure times ranging from 1 millisecond to 20 seconds, and the intensity count between 0 and the detector saturation value of the analytical instrument support device.

11. The computer-implemented method according to claim 1, wherein the method further comprises: An SNR-intensity model is determined by one or more processors, the SNR-intensity model including multiple SNR values ​​and multiple intensity values, based at least on one or more deviations from a linear SNR model, the one or more deviations being associated with a determined performance level; Determined by one or more processors The third prediction represents at least the intensity-level SNR value based on the second corresponding intensity level, or The fourth prediction represents the second maximum intensity level based at least on the first corresponding SNR value; and The SNR value based on the intensity level or the second maximum intensity level is stored on one or more computer-readable storage devices.

12. The computer-implemented method of claim 11, wherein determining the third prediction comprises receiving third sample data collected from a third scan of the sample by one or more processors, wherein the sample is scanned to a second corresponding intensity level.

13. The computer-implemented method of claim 11, wherein determining the fourth prediction comprises receiving fourth sample data collected from a fourth scan of the sample by one or more processors, wherein the sample is scanned to a first corresponding SNR value.

14. The computer-implemented method according to claim 1, the method further comprising: An SNR-intensity model is determined by one or more processors, the SNR-intensity model comprising multiple SNR values ​​and multiple intensity values, based at least on one or more log-deviations from a linear SNR model, the one or more log-deviations being associated with the determined performance level; Determined by one or more processors The fifth prediction represents the exposure-time-based SNR value, based at least on the second corresponding exposure time, the SNR-intensity model, and the intensity-time model, or The sixth prediction represents the exposure time as a second parameter, based at least on the second corresponding SNR value, the SNR-intensity model, and the intensity-time model. as well as Store the SNR value based on exposure time or the second parameter exposure time on one or more computer-readable storage devices.

15. The computer-implemented method of claim 14, wherein determining the fifth prediction comprises receiving fifth sample data collected from a fifth scan of the sample by one or more processors, wherein the sample is scanned at a second corresponding exposure time.

16. The computer-implemented method of claim 14, wherein determining the sixth prediction comprises receiving sixth sample data collected from a sixth scan of the sample by one or more processors, wherein the sample is scanned at a second corresponding SNR value.

17. The computer-implemented method according to claim 14, the method further comprising: One or more processors determine a threshold signal-to-noise ratio (SNR) intensity value based on preliminary sample data, wherein the preliminary sample data includes dark Raman spectral data; A linear SNR model is generated by one or more processors, based at least on the threshold SNR intensity value; One or more processors convert the domain of the generated SNR linear model from intensity level to SNR; Apply the one or more deviations to the SNR linear model; as well as One or more processors convert the domain of the SNR linear model from SNR to intensity level to form an SNR-intensity model.

18. One or more non-transitory computer-readable media having instructions stored thereon that, when executed by one or more processing devices of an analytical instrument support apparatus, cause the analytical instrument support apparatus to perform the method of claim 1.

19. An analytical instrument support system, comprising: One or more processors, One or more non-transitory computer-readable storage media; as well as Program instructions stored in one or more non-transitory computer-readable storage media, for execution by at least one of one or more processors, the program instructions comprising: Program instructions to receive preliminary sample data collected from a short scan of the sample; Program instructions to determine the maximum luminance intensity level based at least on the preliminary sample data; Program instructions that determine the performance level based at least on the maximum luminance intensity level; The program instructions for determining an intensity-time model, which includes multiple intensity levels and multiple exposure times, based at least on one or more deviations from an intensity linear model, said one or more deviations being associated with a determined performance level; Determine the following program instructions: The first prediction represents, at least based on the first corresponding exposure time and the first maximum intensity level of the intensity-time model, or The second prediction is based at least on the first corresponding intensity level and the first parameter exposure time of the intensity-time model; and The program instructions store the first maximum intensity level or the first parameter exposure time in one or more computer-readable storage devices.

20. The analytical instrument support system of claim 19, wherein the program instructions are executed on a general-purpose computing device including at least one of the one or more processors.

21. The analytical instrument support system according to claim 19, wherein, The program instructions are executed on a computing device that includes at least one of the one or more processors, and the computing device is located at a remote location of the analytical instrument associated with the analytical instrument support system.

22. The analytical instrument support system of claim 19, wherein the program instructions are executed on a user computing device including at least one of the one or more processors.

23. The analytical instrument support system according to claim 19, wherein, At least one of the one or more processors is included in an analytical instrument associated with the analytical instrument support system, and the program instructions are executed on at least one of the one or more processors.

24. An analytical instrument, the analytical instrument comprising: A light source configured to illuminate the surface of a sample; A spectrometer is used to obtain the Raman spectrum of a sample surface in response to a light source illuminating the sample surface. One or more processors; One or more non-transitory computer-readable storage media; as well as Program instructions stored on one or more non-transitory computer-readable storage media, for execution by at least one of one or more processors, wherein when at least one of the one or more processors executes the program instructions, it causes the analytical instrument to perform a set of operations, including: Analyze the Raman spectral data from the collected Raman spectra related to the sample surface. The maximum brightness intensity level should be determined at least based on the acquired Raman spectra. The performance level is determined based on the maximum brightness intensity level associated with the acquired Raman spectrum. A intensity-time model is determined, comprising multiple intensity levels and multiple exposure times based on at least one or more deviations from an intensity linear model, wherein the one or more deviations are associated with a determined performance level. A first maximum intensity level is determined at least based on a first corresponding exposure time and the intensity-time model, or a first parameter exposure time is determined at least based on a first corresponding intensity level and the intensity-time model. The first maximum intensity level or the first parameter exposure time is stored in at least one of the one or more non-transitory computer-readable storage media.

25. The analytical instrument according to claim 24, wherein the set of actions further comprises: Determine an SNR-intensity model, which includes multiple SNR values ​​and multiple intensity values, based at least on one or more deviations from an SNR linear model, the one or more deviations being associated with the determined performance level; At least based on The second corresponding intensity level determines the SNR value based on the intensity level, or The second maximum intensity level is determined at least based on the first corresponding SNR value; as well as The intensity level-based SNR value or the second maximum intensity level is stored on at least one of the one or more non-transitory computer-readable storage media.

26. The analytical instrument according to claim 24, wherein the set of actions further comprises: Determine an SNR-intensity model, which includes multiple SNR values ​​and multiple intensity values, based at least on one or more logarithmic deviations from a linear SNR model, the one or more logarithmic deviations being associated with the determined performance level; At least based on The second corresponding exposure time, the SNR-intensity model, and the intensity-time model determine the SNR value based on the exposure time; or The second parameter, exposure time, is determined at least based on the second corresponding SNR value, the SNR-intensity model, and the intensity-time model. as well as The exposure time-based SNR value or the second parameter exposure time is stored on at least one of one or more non-transitory computer-readable storage media.

27. The analytical instrument according to claim 26, wherein the set of actions further comprises: The threshold signal-to-noise ratio (SNR) intensity value is determined based on preliminary sample data, wherein the preliminary sample data includes dark Raman spectral data; The SNR linear model is generated based at least on the SNR intensity threshold; The domain of the generated SNR linear model is converted from intensity level to SNR; Apply the one or more biases to the SNR linear model; and The domain of the SNR linear model is converted from SNR to intensity level to form the SNR-intensity model.

Images