Multi-machine calibration method and system for Raman spectrometer and storage medium

Abstract

The invention relates to a multi-machine calibration method and system for a Raman spectrometer, and a storage medium, and the method comprises the steps: outputting an adjustable substrate voltage, and setting an initial signal amplification gain; controlling a CCD sensor to collect first Raman spectrum data of a standard sample with a known standard wave number; establishing a mapping model from a pixel domain to a wave number domain based on a plurality of known standard wave numbers and second pixel point positions of the characteristic peaks corresponding to the known standard wave numbers in the first Raman spectrum data to obtain calibration parameters of the Raman spectrometer; verifying the accuracy of the mapping model by verifying the characteristic peak, and storing calibration parameters after the verification is passed; controlling the plurality of Raman spectrometers to respectively detect the same sample to be detected, acquiring second Raman spectrum data of the sample to be detected, extracting a third pixel point position corresponding to a characteristic peak of the sample to be detected, calling the calibration parameter, and converting the calibration parameter into an actually measured wave number; and feeding back and adjusting the signal amplification gain of each instrument based on a signal intensity measurement value corresponding to the second Raman spectrum data by each Raman spectrometer.

Description

Technical Field

[0001] This invention relates to the field of spectral analysis technology, and in particular to a multi-machine calibration method, system, and storage medium for Raman spectrometers. Background Technology

[0002] As a highly sensitive analytical instrument, the measurement accuracy of a Raman spectrometer directly affects the reliability of the test results. However, due to manufacturing tolerances in optical components, electronic devices, and mechanical structures, performance differences inevitably exist between different batches of Raman spectrometers, and even between the same batch. These differences typically manifest as wavenumber axis drift, inconsistencies in signal intensity, and distortions in spectral shape.

[0003] Currently, most calibration methods focus primarily on the calibration of single instruments, lacking systematic solutions for the collaborative calibration of multiple instruments, making it difficult to ensure the consistency of measurement results across different instruments. Furthermore, existing calibration methods often emphasize wavenumber axis calibration while paying insufficient attention to signal intensity calibration, particularly lacking dynamic feedback adjustment mechanisms based on actual sample measurement results. Moreover, existing calibration methods typically involve complex operational procedures and require specialized knowledge, hindering their widespread adoption in applications such as rapid clinical screening.

[0004] It is evident that ensuring the consistency and comparability of measurement results from multiple Raman spectrometers working simultaneously has become a pressing technical challenge in this field. Summary of the Invention

[0005] To ensure the consistency and reliability of detection results from multiple Raman spectrometers, this invention provides a multi-instrument calibration method for Raman spectrometers, comprising: Control multiple Raman spectrometers to independently execute S10-S40; S10. Control the signal conditioning circuit of the Raman spectrometer to output an adjustable base voltage and set an initial signal amplification gain. S20. Control the CCD sensor to acquire the first Raman spectrum data of a standard sample with a known standard wavenumber; S30. Based on the second pixel position of multiple known standard wavenumbers and their corresponding characteristic peaks in the first Raman spectral data, a mapping model from the pixel domain to the wavenumber domain is established by fitting a nonlinear function, and the calibration parameters of the Raman spectrometer are obtained based on the mapping model. S40. Verify the accuracy of the mapping model by verifying the characteristic peaks, and store the calibration parameters after the verification is passed; Perform coordinated calibration on multiple Raman spectrometers that have already been calibrated, following steps S50-S60. S50. Control multiple Raman spectrometers to detect the same sample to be tested, collect the second Raman spectrum data of the sample to be tested, extract the position of the third pixel point corresponding to the characteristic peak of the sample to be tested from the second Raman spectrum data, and call the calibration parameters stored in each instrument to convert the position of the third pixel point into the measured wavenumber. S60. Based on the signal intensity measurement values ​​corresponding to the second Raman spectrum data of the same sample under test by each Raman spectrometer, the signal amplification gain of each instrument is adjusted accordingly.

[0006] Optionally, S10 includes: S11, A digital-to-analog converter that controls the signal conditioning circuit of the Raman spectrometer to output an adjustable base voltage; S12. An initial signal amplification gain is set by adjusting the resistance value of the digital potentiometer in the feedback loop of the amplifier circuit in the signal conditioning circuit.

[0007] Optionally, S20 specifically controls the CCD sensor to operate in a line-by-line scanning mode to acquire the first Raman spectral data of a standard sample with a known standard wavenumber. S30 includes: S31. Identify the positions of the first pixel points corresponding to the characteristic peaks of multiple known standard wavenumbers from the first Raman spectral data; S32. Collect the pixel positions of the same feature peak multiple times and calculate the corresponding average value as the second pixel position; S33. Based on multiple known standard wavenumbers and the corresponding second pixel positions, a mapping model from the pixel domain to the wavenumber domain is established by fitting a nonlinear function. S34. Solve the system of equations consisting of multiple mapping models using the least squares method to obtain the calibration parameters of the Raman spectrometer.

[0008] Optionally, the nonlinear function fitting adopts a cubic polynomial model, and the polynomial coefficients are solved by the least squares method.

[0009] Optionally, S40 includes: S41. Obtain the position of the fourth pixel corresponding to the verification feature peak; S42. Calculate the predicted wavenumber corresponding to the pixel position using the mapping model; S43. Calculate the first deviation value between the predicted wavenumber and the known standard wavenumber of the verification characteristic peak, and determine whether the first deviation value is less than or equal to the preset error threshold. If so, the verification is passed, and the calibration parameters are stored.

[0010] Optionally, the calibration parameters are stored in an encrypted manner, including: An encryption key is generated based on the identification information of a Raman spectrometer; The calibration parameters are encrypted using the encryption key to generate an encrypted data packet, which is then stored.

[0011] Optional, the S60 includes: S61. Obtain the signal intensity measurement values ​​corresponding to the characteristic peaks of the same sample from each Raman spectrometer. S62. Calculate the average value of all signal strength measurements as the reference strength value; S63. For a single Raman spectrometer, calculate the second deviation between its signal intensity measurement value and the reference intensity value; when the second deviation value is greater than a preset intensity deviation threshold, generate a gain adjustment command; S64. Adjust the signal amplification gain parameter of the corresponding Raman spectrometer according to the gain adjustment command; S65. Repeat S61-S64 until the second deviation value is less than or equal to the preset intensity deviation threshold, and then associate and store the adjusted signal amplification gain parameter with the polynomial coefficients.

[0012] Optionally, the standard sample is acetonitrile.

[0013] Corresponding to the aforementioned multi-machine calibration method for Raman spectrometers, the present invention provides a multi-machine calibration system for Raman spectrometers, comprising: A single calibration module is used to control multiple Raman spectrometers to independently perform the corresponding functions of the signal conditioning unit, standard calibration unit, mapping modeling unit, and model verification unit. The signal conditioning unit is used to control the signal conditioning circuit of the Raman spectrometer to output an adjustable base voltage and set an initial signal amplification gain. The standard calibration unit is used to control the CCD sensor to acquire the first Raman spectral data of a standard sample with a known standard wavenumber. The mapping modeling unit is used to establish a mapping model from the pixel domain to the wavenumber domain by fitting a nonlinear function based on the second pixel position of multiple known standard wavenumbers and their corresponding characteristic peaks in the first Raman spectral data, and to obtain the calibration parameters of the Raman spectrometer based on the mapping model. The model verification unit is used to verify the accuracy of the mapping model by verifying the feature peaks, and to store the calibration parameters after the verification is passed. The multi-machine collaboration module is used to collaboratively calibrate multiple Raman spectrometers that have already been calibrated, and to perform the corresponding functions of the sample detection unit and the feedback adjustment unit. The sample detection unit is used to control multiple Raman spectrometers to detect the same sample to be tested, acquire the second Raman spectrum data of the sample to be tested, extract the position of the third pixel point corresponding to the characteristic peak of the sample to be tested from the second Raman spectrum data, and call the calibration parameters stored in each unit to convert the position of the third pixel point into the measured wavenumber. The feedback adjustment unit is used to adjust the signal amplification gain of each instrument based on the signal intensity measurement value corresponding to the second Raman spectrum data of the same sample from each Raman spectrometer.

[0014] Furthermore, to achieve the above objectives, the present invention also provides a computer-readable storage medium storing a multi-machine calibration program for a Raman spectrometer, wherein the multi-machine calibration program for a Raman spectrometer, when executed by a processor, implements the steps of the multi-machine calibration method for a Raman spectrometer as described above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: (1) By constructing a complete closed-loop calibration system that includes wavenumber domain calibration and dynamic feedback adjustment of signal intensity, the problem of poor consistency of measurement results among multiple Raman spectrometers is solved. Specifically, steps S10-S40 establish a pixel-wavenumber domain mapping model to achieve precise correction of wavenumber axis drift and solidification of calibration parameters for each instrument, eliminating the wavenumber scale differences caused by manufacturing tolerances of optical components in different instruments; step S60 innovatively introduces a cross-instrument comparison feedback mechanism based on the measured signal intensity of the same sample, dynamically adjusting the signal amplification gain of each instrument, directly solving the problems of inconsistent signal intensity and spectral shape distortion, and making up for the lack of dynamic intensity calibration in existing technologies. This invention breaks through the limitations of traditional independent calibration of a single instrument. Through standardized procedures and storable and callable calibration parameters, a multi-instrument collaborative calibration framework that can be deployed in batches is constructed, significantly improving the reliability and comparability of measurement results among different instruments. At the same time, the automated process reduces the operational complexity and professional threshold in scenarios such as rapid clinical screening.

[0016] (2) By using a digital-to-analog converter (DAC) and a digital potentiometer to digitally set the base voltage and gain, high precision and repeatability of calibration parameters are achieved, avoiding the randomness of manual adjustment of traditional analog devices, improving the automation level and stability of the calibration process, and providing a reliable hardware guarantee for multi-machine consistency.

[0017] (3) By adopting the line-by-line scanning mode and the multiple acquisition averaging strategy, the influence of random noise and environmental interference of CCD sensor on the localization of feature peak pixels is effectively reduced, significantly improving the extraction accuracy and statistical reliability of the second pixel position, thereby enhancing the accuracy of the mapping model construction; by using a cubic polynomial model combined with the least squares method, the nonlinear characteristics of grating dispersion in Raman spectrometer can be accurately fitted, which greatly improves the accuracy and applicability of pixel-wavenumber mapping compared with linear or low-order models, ensuring high-precision calibration in the full spectrum.

[0018] (4) By introducing an independent verification feature peak to quantify and verify the model prediction deviation, an objective evaluation standard for the validity of calibration parameters was established. Parameters are stored only when the deviation is less than the threshold, which effectively prevents erroneous calibration caused by overfitting or abnormal data and ensures the reliability and traceability of calibration results.

[0019] (5) The calibration parameters are encrypted and stored by generating keys based on the instrument’s unique identification information, which avoids accidental tampering, misuse or cross-device mixing of calibration parameters, and ensures that the calibration parameters of each instrument are strictly bound to its optical characteristics, thereby improving the data security and error prevention capabilities of the multi-machine calibration system.

[0020] (6) By calculating the reference mean of the signal strength of multiple instruments and quantifying the deviation of a single instrument, the abstract "consistency" problem is transformed into an automatically executable closed-loop feedback adjustment logic, realizing the intelligent and unmanned gain adjustment, enabling the intensity response of each instrument to quickly converge to a uniform level and dynamically adapt to the measurement needs of different samples.

[0021] (7) By using acetonitrile as a standard sample, its characteristics of sharp peaks, high stability and good wavenumber distribution coverage are utilized to provide a high signal-to-noise ratio and high reliability benchmark for the establishment of the mapping model, simplifying the management of standard materials and enhancing the practicality and economy of the calibration process. Attached Figure Description

[0022] The accompanying drawings, which are included to provide a further understanding of the invention and form part of this invention, illustrate exemplary embodiments of the invention and are used to explain the invention, but do not constitute an undue limitation of the invention. In the drawings: Figure 1 This is a simplified flowchart of an embodiment of the multi-machine calibration method for Raman spectrometers according to the present invention; Figure 2 This is a framework diagram of an embodiment of the multi-machine calibration system for Raman spectrometers according to the present invention. Detailed Implementation

[0023] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, not all embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0024] like Figure 1 As shown, a multi-machine calibration method for a Raman spectrometer according to the present invention includes: Control multiple Raman spectrometers to independently execute S10-S40; S10. Control the signal conditioning circuit of the Raman spectrometer to output an adjustable base voltage and set an initial signal amplification gain. S20. Control the CCD sensor to acquire the first Raman spectrum data of a standard sample with a known standard wavenumber; S30. Based on multiple known standard wavenumbers and their corresponding characteristic peaks, the second pixel position in the first Raman spectral data is used to establish a mapping model from the pixel domain to the wavenumber domain through nonlinear function fitting. The calibration parameters of the Raman spectrometer are obtained based on the mapping model. S40. Verify the accuracy of the mapping model by verifying the characteristic peaks, and store the calibration parameters after the verification is passed; Perform coordinated calibration on multiple Raman spectrometers that have already been calibrated, following steps S50-S60. S50 controls multiple Raman spectrometers to detect the same sample, acquires the second Raman spectrum data of the sample, extracts the position of the third pixel corresponding to the characteristic peak of the sample from the second Raman spectrum data, and calls the calibration parameters stored in each instrument to convert the position of the third pixel into the measured wavenumber. S60. Based on the signal intensity measurement values ​​corresponding to the second Raman spectrum data of the same sample under test by each Raman spectrometer, the signal amplification gain of each instrument is adjusted accordingly.

[0025] It should be noted that signal conditioning circuits can be implemented using existing technologies.

[0026] In this embodiment, the standard sample is acetonitrile.

[0027] This invention uses acetonitrile as a standard sample, taking advantage of its sharp characteristic peaks, high stability, and good wavenumber distribution coverage. This provides a high signal-to-noise ratio and high reliability benchmark for establishing mapping models, simplifies the management of standard materials, and enhances the practicality and economy of the calibration process.

[0028] It should be noted that both wavenumber and pixel position are "position coordinates," differing only in their dimension of representation (wavenumber reflects molecular properties, while pixel position reflects the detection position). A cubic polynomial is generated by fitting the known "wavenumber-pixel position" correspondence of acetonitrile standards. Where x is the pixel position and y is the wavenumber, a dedicated position mapping model for a single device is established to offset the nonlinear tolerance of the CCD sensor's spectral response.

[0029] In this embodiment, S30 constructs a model from the pixel domain to the wavenumber domain based on four sets of "standard wavenumber-second pixel position" sample pairs; preferably, the known standard wavenumber (confirmed by an authoritative spectral database, used only as a position reference, and unrelated to signal strength) is: =378 , =918 , =1374 , =2252 All of them exhibit no fluorescence interference and have sharp peak shapes, making them suitable as reference points for position mapping.

[0030] This invention solves the problem of poor consistency in measurement results among multiple Raman spectrometers by constructing a complete closed-loop calibration system that includes wavenumber domain calibration and dynamic signal intensity feedback adjustment. Specifically, steps S10-S40 establish a pixel-domain-wavenumber domain mapping model to achieve precise correction of wavenumber axis drift and solidification of calibration parameters for each instrument, eliminating wavenumber scale differences caused by manufacturing tolerances of optical components in different instruments. Step S60 innovatively introduces a cross-instrument comparison feedback mechanism based on the measured signal intensity of the same sample, dynamically adjusting the signal amplification gain of each instrument, directly solving the problems of inconsistent signal intensity and spectral shape distortion, and compensating for the lack of dynamic intensity calibration in existing technologies. This invention breaks through the limitations of traditional independent calibration of a single instrument. Through standardized procedures and storable and recallable calibration parameters, it constructs a multi-instrument collaborative calibration framework that can be deployed in batches, significantly improving the reliability and comparability of measurement results among different instruments. At the same time, the automated process reduces the operational complexity and professional threshold in scenarios such as rapid clinical screening.

[0031] In this embodiment, S10 includes: S11, A digital-to-analog converter that controls the signal conditioning circuit of the Raman spectrometer to output an adjustable base voltage; S12. An initial signal amplification gain is set by adjusting the resistance value of the digital potentiometer in the feedback loop of the amplifier circuit in the signal conditioning circuit.

[0032] Preferably, an STM32F407IGT6 microcontroller is selected as the core control unit. Its built-in 12-bit DAC module outputs an adjustable base voltage of 0-3.3V, which is buffered by an LMV321IDBVR operational amplifier forming a voltage follower to provide a stable base voltage for the subsequent OPA2350 amplifier circuit. At the same time, the AD5272 digital potentiometer (1024-level resolution) is controlled through the interface to adjust the resistance value of the feedback loop of the amplifier circuit, achieving 1-2 times adjustable gain. The adjustment step can reach 0.81mV (3.3V / 4096). The measured dark current noise voltage is reduced from greater than or equal to 20μV in the traditional circuit to 8μV, ensuring clear acquisition of weak signals at the corresponding pixels of acetonitrile.

[0033] This invention achieves high precision and repeatable adjustment of calibration parameters by using a digital-to-analog converter (DAC) and a digital potentiometer to digitally set the base voltage and gain. This avoids the arbitrariness of manual adjustment of traditional analog devices, improves the automation level and stability of the calibration process, and provides a reliable hardware-level guarantee for multi-machine consistency.

[0034] In this embodiment, S20 specifically controls the CCD sensor to operate in line-by-line scanning mode to acquire the first Raman spectral data of a standard sample with a known standard wavenumber.

[0035] Preferably, the CCD sensor (such as S11639) is controlled to operate in progressive scan mode via the GPIO pins (PB0-PB3) of the STM32F407IGT6, with a driving clock frequency of 10MHz (matching the I / O port rate of the STM32F407IGT6); the Raman spectrum of acetonitrile standard is acquired, and the STM32F407IGT6 reads all Raman spectral data output by the CCD sensor through the SPI2 interface, extracting the four characteristic peaks of the acetonitrile standard (i.e., =378 , =918 , =1374 , =2252 The position of the first pixel corresponding to the vertex (denoted as ) , , , Each pixel is sampled 5 times, and the average value is calculated by STM32F407IGT6 as the position of the second pixel. This reduces the position shift caused by random noise and ensures the reliability of the "standard wavenumber-second pixel position" sample pair.

[0036] This invention effectively reduces the impact of random noise and environmental interference from the CCD sensor on the localization of feature peak pixels by adopting a line-by-line scanning mode and a multiple acquisition averaging strategy, significantly improving the extraction accuracy and statistical reliability of the second pixel position, thereby enhancing the accuracy of the mapping model construction.

[0037] In this embodiment, S30 includes: S31. Identify the positions of the first pixel points corresponding to the characteristic peaks of multiple known standard wavenumbers from the first Raman spectral data; S32. Collect the pixel positions of the same feature peak multiple times and calculate the corresponding average value as the second pixel position; S33. Based on multiple known standard wavenumbers and the corresponding second pixel positions, a mapping model from the pixel domain to the wavenumber domain is established by fitting a nonlinear function. S34. Solve the system of equations consisting of multiple mapping models using the least squares method to obtain the calibration parameters of the Raman spectrometer.

[0038] In this embodiment, a cubic polynomial model is used for nonlinear function fitting, and the polynomial coefficients are solved by the least squares method to obtain the polynomial coefficients of a single device. Preferably, the STM32F407IGT6 uses "standard wavenumber - second pixel position" as 4 sample pairs [( , ), ( , ), ( , ), ( , Substituting into the cubic polynomial model The system of linear equations is constructed as follows: ; ; ; ; It should be noted that the above equations only reflect the position mapping relationship and are independent of the signal strength at each position. Furthermore, during the fitting process, the STM32F407IGT6 also reduces the residual through iterative optimization, ensuring a goodness of fit R² ≥ 0.999 and a pixel-to-wavenumber position error ≤ 0.3. .

[0039] This invention uses a cubic polynomial model combined with the least squares method to accurately fit the nonlinear characteristics of grating dispersion in a Raman spectrometer. Compared with linear or low-order models, it significantly improves the accuracy and applicability of pixel-wavenumber mapping, ensuring high-precision calibration across the entire spectrum.

[0040] In this embodiment, S40 includes: S41. Obtain the position of the fourth pixel corresponding to the verification feature peak; preferably, the verification feature peak... =1450 The position of the fourth pixel is denoted as ; S42. Calculate the predicted wavenumber corresponding to the pixel position using the mapping model; S43. Calculate the first deviation between the predicted wavenumber and the known standard wavenumber of the verification characteristic peak, and determine whether the first deviation is less than or equal to a preset error threshold. If so, the verification passes, and the calibration parameters are stored. In this embodiment, the preset error threshold is 0.3. .

[0041] This invention introduces an independent verification characteristic peak to quantify and verify the model prediction bias, establishing an objective evaluation standard for the validity of calibration parameters. Parameters are only stored when the deviation is less than a threshold, effectively preventing erroneous calibration caused by overfitting or abnormal data, and ensuring the reliability and traceability of calibration results.

[0042] In this embodiment, the calibration parameters are stored in an encrypted manner, including: An encryption key is generated based on the identification information of a Raman spectrometer; The calibration parameters are encrypted using an encryption key, and an encrypted data packet is generated and stored.

[0043] This invention generates a key based on the instrument's unique identification information for encrypted storage, which avoids the calibration parameters from being accidentally tampered with, misused, or mixed across devices. It ensures that the calibration parameters of each instrument are strictly bound to its optical characteristics, thereby improving the data security and error prevention capabilities of the multi-machine calibration system.

[0044] In this embodiment, S60 includes: S61. Obtain the signal intensity measurements of the characteristic peaks corresponding to the same sample from each Raman spectrometer; preferably, the sample to be tested is a simulated sample of myocardial infarction markers (containing known concentrations of troponin I and cTnI characteristic wavenumbers). =1245 (used only as a location reference). S62. Calculate the average value of all signal strength measurements as the reference strength value; S63. For a single Raman spectrometer, calculate the second deviation between its measured signal intensity value and the reference intensity value; when the second deviation value is greater than the preset intensity deviation threshold, generate a gain adjustment command; S64. According to the gain adjustment command, adjust the signal amplification gain parameter of the corresponding Raman spectrometer (regardless of wavenumber position, only adjust the signal intensity). S65. Repeat S61-S64 until the second deviation value is less than or equal to the preset intensity deviation threshold, and store the adjusted signal amplification gain parameter and polynomial coefficients in association; preferably, the preset intensity deviation threshold is 2%.

[0045] This invention transforms the abstract "consistency" problem into an automatically executable closed-loop feedback adjustment logic by calculating the reference mean of the signal strength of multiple instruments and quantifying the deviation of a single instrument. This achieves intelligent and unmanned gain adjustment, enabling the intensity response of each instrument to quickly converge to a uniform level and dynamically adapt to the measurement needs of different samples.

[0046] like Figure 2 As shown, the present invention also provides a multi-machine calibration system for a Raman spectrometer, comprising: A single calibration module 10 is used to control multiple Raman spectrometers to independently perform the corresponding functions of the signal conditioning unit 11, the standard calibration unit 12, the mapping modeling unit 13, and the model verification unit 14. The signal conditioning unit 11 is used to control the signal conditioning circuit of the Raman spectrometer to output an adjustable base voltage and set an initial signal amplification gain. The standard calibration unit 12 is used to control the CCD sensor to acquire the first Raman spectrum data of a standard sample with a known standard wavenumber. The mapping modeling unit 13 is used to establish a mapping model from the pixel domain to the wavenumber domain based on the second pixel position of multiple known standard wavenumbers and their corresponding characteristic peaks in the first Raman spectral data through nonlinear function fitting, and to obtain the calibration parameters of the Raman spectrometer based on the mapping model. The model verification unit 14 is used to verify the accuracy of the mapping model by verifying the feature peaks, and to store the calibration parameters after the verification is passed. The multi-machine collaborative calibration module 20 is used to perform collaborative calibration on multiple Raman spectrometers that have been calibrated, and executes the corresponding functions of the sample detection unit 21 and the feedback adjustment unit 22. The sample detection unit 21 is used to control multiple Raman spectrometers to detect the same sample to be tested, acquire the second Raman spectrum data of the sample to be tested, extract the position of the third pixel point corresponding to the characteristic peak of the sample to be tested from the second Raman spectrum data, and call the calibration parameters stored in each unit to convert the position of the third pixel point into the measured wavenumber. The feedback adjustment unit 22 is used to adjust the signal amplification gain of each instrument based on the signal intensity measurement value corresponding to the second Raman spectrum data of the same sample under test by each Raman spectrometer.

[0047] This invention also provides a computer-readable storage medium, which may be a computer-readable storage medium included in the memory described in the above embodiments; or it may be a standalone computer-readable storage medium not assembled into a device. The computer-readable storage medium stores at least one instruction, which is loaded and executed by a processor to implement... Figure 1 The diagram illustrates a multi-machine calibration method for a Raman spectrometer. The computer-readable storage medium may be a read-only memory, a hard disk, or an optical disk, etc.

[0048] It should be noted that the various embodiments in this specification are described in a progressive manner, with each embodiment focusing on the differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For system embodiments and storage medium embodiments, since they are basically similar to method embodiments, the descriptions are relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0049] Furthermore, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0050] The foregoing description illustrates and describes preferred embodiments of the present invention. It should be understood that the present invention is not limited to the forms disclosed herein and should not be construed as excluding other embodiments. It can be used in various other combinations, modifications, and environments, and can be altered within the scope of the inventive concept by means of the foregoing teachings or techniques or knowledge in related fields. Any modifications and variations made by those skilled in the art that do not depart from the spirit and scope of the present invention should be within the protection scope of the appended claims.

Claims

1. A multi-machine calibration method for a Raman spectrometer, the method comprising: The method comprises the following steps: controlling multiple Raman spectrometers to independently execute S10-S40; S10, controlling the signal conditioning circuit of the Raman spectrometer to output an adjustable base voltage and set an initial signal amplification gain; S20, controlling the CCD sensor to collect first Raman spectrum data of a standard sample with known standard wave numbers; S30, based on multiple known standard wave numbers and the second pixel point positions of the characteristic peaks corresponding thereto in the first Raman spectrum data, a mapping model from the pixel domain to the wave number domain is established through nonlinear function fitting, and the calibration parameters of the Raman spectrometer are obtained based on the mapping model; the nonlinear function fitting adopts a cubic polynomial model, and the polynomial coefficients are solved by the least square method; S40, the accuracy of the mapping model is verified by verifying the characteristic peaks, and the calibration parameters are stored after the verification is passed; cooperative calibration is performed on the multiple Raman spectrometers that have completed calibration, and S50-S60 are executed; S50, controlling multiple Raman spectrometers to detect the same sample to be measured respectively, collecting second Raman spectrum data of the sample to be measured, extracting third pixel point positions corresponding to the characteristic peaks of the sample to be measured from the second Raman spectrum data, and calling the stored calibration parameters to convert the third pixel point positions into measured wave numbers; S60, based on the signal intensity measurement values corresponding to the second Raman spectrum data of the same sample to be measured by each Raman spectrometer, the signal amplification gain of each instrument is feedback adjusted; S60 comprises: S61, obtaining the signal intensity measurement values corresponding to the characteristic peaks of the same sample to be measured by each Raman spectrometer; S62, calculating the average value of all signal intensity measurement values as a reference intensity value; S63, for a single Raman spectrometer, calculating a second deviation value of the signal intensity measurement value and the reference intensity value; when the second deviation value is greater than a preset intensity deviation threshold, a gain adjustment instruction is generated; S64, adjusting the signal amplification gain parameter of the corresponding Raman spectrometer according to the gain adjustment instruction; S65, repeating S61-S64 until the second deviation value is less than or equal to the preset intensity deviation threshold, and the adjusted signal amplification gain parameter is associated and stored with the polynomial coefficients.

2. The multi-machine calibration method for a Raman spectrometer of claim 1, wherein, S10 comprises: S11, controlling the digital-to-analog converter of the signal conditioning circuit of the Raman spectrometer to output an adjustable base voltage; S12, adjusting the resistance value of the digital potentiometer in the feedback loop of the amplification circuit in the signal conditioning circuit to set an initial signal amplification gain.

3. The multi-machine calibration method for a Raman spectrometer of claim 1, wherein, S20 specifically controls the CCD sensor to work in a line-by-line scanning mode to collect first Raman spectrum data of a standard sample with known standard wave numbers; S30 comprises: S31, identifying the first pixel point positions corresponding to the characteristic peaks of the multiple known standard wave numbers from the first Raman spectrum data; S32, collecting the pixel point positions of the same characteristic peak multiple times and calculating the corresponding average value as the second pixel point position; S33, based on multiple known standard wave numbers and corresponding second pixel point positions, a mapping model from the pixel domain to the wave number domain is established through nonlinear function fitting; S34, solve the equation group composed of multiple mapping models based on least square method to obtain the calibration parameter of the Raman spectrometer.

4. The multi-machine calibration method for a Raman spectrometer of claim 1, wherein, S40 includes: S41, obtain the fourth pixel point position corresponding to the verification characteristic peak; S42, calculate the predicted wave number corresponding to the pixel point position through the mapping model; S43, calculate the first deviation value of the predicted wave number and the known standard wave number of the verification characteristic peak, and judge whether the first deviation value is less than or equal to the preset error threshold value, if yes, the verification is passed, and the calibration parameter is stored.

5. The multi-machine calibration method for a Raman spectrometer of claim 4, wherein, When storing the calibration parameter, the storage is performed in an encrypted storage manner, including: generating an encryption key based on the identification information of a Raman spectrometer; encrypting the calibration parameter using the encryption key, generating an encrypted data packet and storing.

6. The multi-machine calibration method for a Raman spectrometer of claim 1, wherein, The standard sample is acetonitrile.

7. A multi-machine calibration system for a Raman spectrometer, comprising: Including: A single calibration module is used to control multiple Raman spectrometers to independently execute the corresponding functions of the signal conditioning unit, the standard calibration unit, the mapping modeling unit and the model verification unit; The signal conditioning unit is used to control the signal conditioning circuit of the Raman spectrometer to output an adjustable base voltage and set an initial signal amplification gain; The standard calibration unit is used to control the CCD sensor to collect the first Raman spectrum data of the standard sample with known standard wave number; The mapping modeling unit is used to establish a mapping model from the pixel domain to the wave number domain based on multiple known standard wave numbers and the second pixel point positions of the characteristic peaks corresponding to the known standard wave numbers in the first Raman spectrum data through nonlinear function fitting, and obtain the calibration parameter of the Raman spectrometer based on the mapping model; The nonlinear function fitting adopts a cubic polynomial model, and the polynomial coefficients are solved by least square method; The model verification unit is used to verify the accuracy of the mapping model through the verification characteristic peak, and store the calibration parameter after the verification is passed; The multi-machine cooperative calibration module is used to cooperatively calibrate multiple Raman spectrometers that have completed calibration, and execute the corresponding functions of the sample detection unit and the feedback adjustment unit; The sample detection unit is used to control multiple Raman spectrometers to detect the same sample to be tested respectively, collect the second Raman spectrum data of the sample to be tested, extract the third pixel point position corresponding to the characteristic peak of the sample to be tested from the second Raman spectrum data, and call the stored calibration parameter to convert the third pixel point position into the measured wave number; The feedback adjusting unit is configured to feedback adjust the signal amplification gain of each Raman spectrometer based on the signal intensity measurement values corresponding to the second Raman spectrum data of the same sample, and comprises: S61, obtaining the signal intensity measurement values corresponding to the characteristic peaks of the same sample by each Raman spectrometer; S62, calculating the average value of all the signal intensity measurement values as a reference intensity value; S63, for a single Raman spectrometer, calculating a second deviation value of the signal intensity measurement value and the reference intensity value; when the second deviation value is greater than a preset intensity deviation threshold, generating a gain adjustment instruction; S64, adjusting the signal amplification gain parameter of the corresponding Raman spectrometer according to the gain adjustment instruction; S65, repeating S61-S64 until the second deviation value is less than or equal to the preset intensity deviation threshold, and storing the adjusted signal amplification gain parameter and the polynomial coefficient in association.

8. A computer-readable storage medium, characterized in that, The computer readable storage medium stores a multi-machine calibration program for Raman spectrometers, and the multi-machine calibration program for Raman spectrometers, when executed by a processor, implements the steps of the multi-machine calibration method for Raman spectrometers according to any one of claims 1 to 6.

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