A transmission near-infrared analysis method, medium and device for particulate samples with enhanced stability

Abstract

The present invention discloses a method, medium and device for transmittance near-infrared analysis of particulate samples with enhanced stability, comprising the following steps: S1: Incident light is focused and collimated, and after passing through an antireflection film glass baffle and the sample, the transmitted energy is received by 16 optical fibers; S2: A servo motor controls the distance H between the baffle and the fiber-mounted planar baffle, and spectra are collected respectively at 1.5 cm and 2 cm; S3: The light-receiving optical fibers are arranged evenly, and the energy intensities of the inner and outer circle light-receiving optical fibers are measured; S4: Variable integration time sampling is performed, and spectra are collected with integration times of 50 ms and 200 ms. By optimizing the light illumination and light reception, spectral offsets caused by changes in sample density and light source intensity are eliminated, the problem of CCD uniformity differences is solved, and the adaptability of the model is enhanced.

Description

Technical Field

[0001] The present invention relates to the field of analytical technologies, and particularly to a transmission near-infrared spectroscopy analysis method, medium and device for particulate samples. Background Art

[0002] Near-infrared spectroscopy (NIR) analysis technology has been widely used in the process control of multiple fields such as medical treatment, grain and oil processing, and industrial production due to its advantages of pollution-free, non-destructive and rapid analysis. On the premise of precisely controlling measurement conditions (such as light source intensity, environmental temperature, etc.), the quantitative analysis model constructed by this technology shows excellent prediction accuracy for liquid and powder samples. However, when the acquisition conditions cannot be kept consistent, especially when the uniformity of particulate samples is poorer than that of liquid and powder samples, the prediction stability of the model will be significantly reduced.

[0003] Currently, the strategies for improving the model stability are mainly divided into three categories. First, ensure the consistency between the modeling environment and the measurement environment to reduce the influence brought by environmental differences. Second, adopt spectral preprocessing technologies such as derivative processing and multiplicative scatter correction to optimize the spectral data, so as to reduce data noise and improve signal quality. Finally, by actively introducing potential interference factors during the modeling process to simulate the interference that may be encountered in actual applications, the robustness of the model to these interference factors is enhanced, and the stability of the model is further improved. Summary of the Invention

[0004] In the analysis of powder and solution samples, it is relatively easy to maintain the consistency of the modeling environment. However, for particulate samples, a transmission measurement method is usually adopted. Given the significant individual differences among particulate samples, in order to increase the information content, it is necessary to sample a single sample multiple times. In a conventional transmission near-infrared analysis system, it is difficult to keep the density of the sample constant during the feeding process, which results in poor repeatability of the spectral data. In addition, the energy intensity difference of near-infrared spectra in the short-wave near-infrared region is relatively large. Especially in the band before 800 nm, the energy is stronger, while the instability of the signal after 800 nm will have a negative impact on the stability of the model. During the actual application process, the measurement conditions will inevitably be interfered by various factors such as light source fluctuations, temperature changes and instrument system differences, and these factors may all lead to a decrease in the model stability. To solve this technical problem, the present invention proposes a transmission near-infrared analysis method for particulate samples with enhanced stability, which improves the stability of the spectral data through the optimization of light irradiation and light collection. Using the relative transmission spectrum as the independent variable, the spectral offset phenomenon caused by the changes in sample density and light source intensity is effectively eliminated, and at the same time, the waveform abnormality problem caused by the uniformity difference of the charge-coupled device (CCD) in the spectrometer is solved. In addition, combined with the spectral correction algorithm, the adaptability of the model is significantly enhanced.

[0005] According to one aspect of the present invention, there is provided a transmission near-infrared analysis method for particulate samples with enhanced stability,

[0006] comprising the following steps:

[0007] S1: The incident light is focused and collimated, the spot size is reduced, the intensity is increased, and after passing through the antireflection film glass baffle and the sample, it hits the fiber optic carrier plane baffle, and the transmitted energy is received by N fibers thereon, where N is 12 - 18;

[0008] S2: The servo motor controls the lateral movement of the glass baffle to control the distance H between the glass baffle and the fiber optic carrier plane baffle. Each sample will be subjected to spectral acquisition once at a distance h1 and once at a distance h2, where the difference between h1 and h2 is 0.5 cm, and both h1 and h2 are selected at a gradient of 0.5 cm within the range of 2 - 3 cm;

[0009] S3: The light-receiving fibers are N fibers evenly arranged on two circumferences with radii r1 and r2 from the detection center, with N / 2 light-receiving fibers in each of the inner and outer circles. The energy intensities of the inner and outer circle light-receiving fibers are i_n and i_w respectively. Among them, the difference between r1 and r2 is 0.5 cm, and both r1 and r2 are selected within the range of 20 mm;

[0010] S4: The spectrometer uses variable integration time sampling, and the bottom runner controls the material feeding. After pausing for 3 - 5 seconds for each rotation of one grid, the spectra are collected successively with integration times of 50 ms and 200 ms.

[0011] The above-mentioned transmission near-infrared analysis method for particulate samples with enhanced stability further includes: where N is 16.

[0012] The above-mentioned transmission near-infrared analysis method for particulate samples with enhanced stability further includes: where h1 is 1.5 cm and h2 is 2 cm.

[0013] The above-mentioned transmission near-infrared analysis method for particulate samples with enhanced stability further includes: where r1 is 5 mm and r2 is 12 mm.

[0014] The above-mentioned transmission near-infrared analysis method for particulate samples with enhanced stability further includes:

[0015] Defining the number of modeling sample sets as m, the number of spectral bands as b, the independent variable spectral matrix as X0(m, 2b), and the dependent variable as Y0(m, 1), then X0 is a matrix of size, and Y0 is a matrix of size, and includes the acquisition steps for each of the following samples:

[0016] S5: Adjust the baffle distance H to 1.5 cm;

[0017] S6: Pour in 300 ml of the sample. Control the feeding with the bottom rotating wheel. After pausing for 3 seconds for each rotation of one grid, collect the spectra successively with integration times of 50 ms and 200 ms. Taking the first time as an example, the energies received by the inner and outer light-receiving optical fibers at 50 ms are I50_n and I50_w respectively, and the energies received by the inner and outer light-receiving optical fibers at 200 ms are I200_n and I200_w respectively.

[0018] S7: Splice the spectral bands. Taking I50_n and I200_n as an example, calculate the average ratio ka of the spectrum in the non-saturated interval of I200_n and the corresponding spectral interval of I50_n. For the saturated interval of I200_n, multiply the spectrum of I50_n in the corresponding interval by ka, so as to raise the spectrum with an integration time of 50 to replace the saturated part under an integration time of 200, and obtain I_n. Obtain I_w in the same way.

[0019] S8: Calculate k = I_n / I_w to obtain the relative transmission spectrum k of this time. In the same way, continue to rotate the bottom rotating wheel and repeat the above steps more than 20 times. Save the average value of the 20 relative transmission spectra k as Ki1, which is the spectrum of the current sample at H = 1.5 cm, and i is the serial number of the current sample.

[0020] S9: Adjust the baffle distance H to 2 cm and repeat steps S6 - 8 to obtain the spectrum Ki2 of the current sample at H = 2 cm.

[0021] S10: Merge the K1 and K2 spectral matrices to obtain the final spectrum Ai = [Ki1, Ki2] of the current sample.

[0022] The above transmission near-infrared analysis method for particulate samples to enhance stability further includes the following modeling steps:

[0023] S11: Additionally collect the spectra x_t(5, 2b) of 5 standard samples with known contents, and the corresponding contents are y_t(5, 1). Calculate the standard spectrum x_t_b(5, 2b) of x_t according to the distance weights from each value in y_t to the modeling sample y0. The transformation matrix is R(5, m). Define the number of modeling samples as m and the number of independent variables as 2b. Then , each of the m elements in each row of R is the inverse distance weight of the m samples in x0 to the corresponding sample in the current row. The farther the distance, the smaller the weight. Simulate the weight function according to the distribution of the dependent variables of the m samples. For example, if it conforms to a normal distribution, a Gaussian function can be used as the weight function. Define the variance of Y0 of the m modeling sample sets as σ. Then

[0024] .

[0025] S12: Perform principal component analysis on the residuals of \(x_{t\_b}\) and \(x_t\), with the projection axis being \(W\). Extract the principal components \(T\) that explain 85% of the variance, and the corresponding loadings are \(P\).

[0026] 。

[0027] S13: Use the \(p\) matrix to correct the modeling sample \(x0\) to obtain the corrected modeling sample spectrum \(x0\_c\). Use \(x0\_c\) and \(y0\) as the independent and dependent variables for modeling.

[0028] 。

[0029] According to another aspect of the present invention, there is provided a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed, the analysis method described in the embodiments of the present invention is implemented.

[0030] According to another aspect of the present invention, there is provided a computer device, including a processor, a memory, and a computer program stored on the memory. When the processor executes the computer program, the analysis method described in the embodiments of the present invention is implemented.

[0031] The present invention provides a transmission near-infrared analysis method for particulate samples with enhanced stability. Compared with traditional methods, the stability of spectral data is improved through the optimization of light irradiation and light collection. Using the relative transmission spectrum as the independent variable effectively eliminates the spectral shift phenomenon caused by changes in sample density and light source intensity, and at the same time solves the problem of abnormal waveforms caused by differences in the uniformity of charge-coupled devices (CCDs) in spectrometers. In addition, combined with the spectral correction algorithm, the adaptability of the model is significantly enhanced. Through the above beneficial effects, the analysis method of the present invention performs excellently in practical applications, especially in the rapid detection and analysis of particulate samples. It not only improves the measurement accuracy, but also greatly shortens the analysis time due to its simple operation, making this method highly practical in industrial production. In addition, this method also has good repeatability and reliability, and can meet the strict requirements for the analysis of particulate samples in different industries. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] The drawings incorporated into the specification and constituting a part of the specification illustrate embodiments of the present invention and, together with the related written description, are used to explain the principles of the present invention. In these drawings, like reference numerals are used to represent like elements. The drawings in the following description are some embodiments of the present invention, not all embodiments. For those of ordinary skill in the art, other drawings can be obtained based on these drawings without creative efforts.

[0033] Figure 1The figure shows a hardware schematic diagram for implementing the enhanced stability particle sample transmission near-infrared analysis method provided by an embodiment of the present invention.

[0034] Figure 2 The figure shows a schematic diagram of the receiving fiber distribution of the fiber-optic mounted planar baffle for the enhanced stability particle sample transmission near-infrared analysis method provided by an embodiment of the present invention. Detailed implementation manners

[0035] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention. It should be noted that, without conflict, the embodiments in the present application and the features in the embodiments may be combined with each other arbitrarily.

[0036] When analyzing powder and solution samples, it is relatively easy to maintain the consistency of the modeling environment. However, for particle samples, this task becomes complex because particle samples usually require a transmission measurement method. There are significant individual differences between particle samples. To increase the amount of information, multiple samplings of a single sample must be performed. In the conventional transmission near-infrared analysis method, it is difficult to keep the density constant during the feeding process of the sample, which results in poor repeatability of spectral data. In addition, the energy intensity of the near-infrared spectrum varies greatly in the short-wave near-infrared region. In particular, the energy is stronger in the band before 800 nm, and the signal instability after 800 nm will have a negative impact on the stability of the model. During the actual application process, the measurement conditions will inevitably be disturbed by various factors such as light source fluctuations, temperature changes, and instrument system differences, and these factors may all lead to a decrease in the model stability. This embodiment aims to solve the above problems and improve the accuracy of particle sample analysis and the stability of the model through a new type of enhanced stability particle sample transmission near-infrared analysis method.

[0037] An embodiment of the present invention provides an enhanced stability particle sample transmission near-infrared analysis method, and its operation and configuration details can be more intuitively understood through the Figure 1 hardware schematic diagram in the appendix, including:

[0038] S1: The incident light passes through focusing and collimation, the spot size is reduced, the intensity is increased, and after passing through the antireflection film glass baffle ① and the sample, it hits the fiber-optic mounted planar baffle ②, and the transmitted energy is received by 12 - 18 fibers above. Preferably, the number of fibers is 16 (as shown in the Figure 2As shown in the figure); through the focusing and collimation of the incident light, the reduction of the spot size and the increase of the light intensity are achieved, which helps to improve the accuracy and sensitivity of spectral acquisition. After passing through the antireflection film glass baffle ① and the sample, the light hits the fiber-optic mounted planar baffle ②, and the transmitted energy is received by 16 optical fibers. This design enhances the light reception efficiency and signal stability.

[0039] S2: The servo motor controls the lateral movement of the glass baffle ① to control the distance H between the glass baffle ① and the fiber-optic mounted planar baffle ②. Each sample will be subjected to spectral acquisition once at a distance h1 and once at a distance h2. The difference between h1 and h2 is 0.5 cm, and both h1 and h2 are selected at a gradient of 0.5 cm within the range of 2 - 3 cm. Preferably, each sample will be subjected to spectral acquisition once at a distance H of 1.5 cm and once at 2 cm; the servo motor controls the lateral movement of the glass baffle ① to precisely control the distance H between the glass baffle ① and the fiber-optic mounted planar baffle ②, ensuring spectral acquisition at distances of 1.5 cm and 2 cm. This precise control helps to reduce errors caused by sample position changes and improves the repeatability and accuracy of the measurement.

[0040] S3: The light-receiving optical fibers are 16 uniformly arranged optical fibers on two circumferences with radii r1 and r2 from the detection center. Preferably, r1 and r1 are 5 mm and 12 mm respectively, with 8 outer-ring light-receiving optical fibers ③ and 8 inner-ring light-receiving optical fibers ④. The energy intensities of the outer-ring and inner-ring light-receiving optical fibers are i_n and i_w respectively; the design of the light-receiving optical fibers is 16 uniformly arranged optical fibers on two circumferences with radii r1 and r2 from the detection center. Among them, the difference between r1 and r2 is 0.5 cm, and both r1 and r2 are selected within the range of 20 mm. Preferably, r1 is 5 mm and r2 is 12 mm. There are 8 outer-ring and inner-ring light-receiving optical fibers each, ensuring equal numbers in the outer and inner rings and uniform arrangement. The outer and inner ring optical fibers and the center of the circle are on the same straight line. This layout helps to collect the spectral information of the sample uniformly from different angles, enhancing the comprehensiveness and representativeness of the spectral data.

[0041] S4: The spectrometer uses variable integration time sampling, and the bottom turntable ⑤ controls the material feeding. After pausing for 3 seconds for each rotation of one grid, the spectra are collected successively with integration times of 50 ms and 200 ms. The spectrometer uses variable integration time sampling, and the bottom turntable ⑤ controls the material feeding. After pausing for 3 seconds for each rotation of one grid, the spectra are collected successively with integration times of 50 ms and 200 ms. This sampling strategy helps to capture spectral changes at different time scales and improves the dynamic range and information content of the spectral data. In specific implementation, the turntable controls the material feeding amount by controlling the angle of each rotation (45 degrees per grid), and the material feeding amount per grid is maintained at about 20 ml. The rotation speed of the turntable remains constant, and the pause time is adjusted to ensure uniform and stable material feeding.

[0042] The enhanced stability particle sample transmission near-infrared analysis method according to the embodiments of the present invention can provide the following advantages:

[0043] By precisely controlling the distance between the sample and the optical fiber and adopting the variable integration time sampling technique, the system can reduce the spectral shift caused by changes in sample density and light source intensity, as well as the waveform abnormality caused by the difference in the uniformity of the spectrometer CCD. Multiple samplings of a single sample, combined with the measurement of the energy intensity of the inner and outer ring light-receiving optical fibers, can provide more comprehensive spectral data and enhance the information content of the analysis. Specifically, this optimized light irradiation method, combined with the use of an antireflection film glass baffle, ensures that light effectively passes through the sample and is received by the 16 inner and outer ring light-receiving optical fibers on the flat baffle of the optical fiber. This design not only improves the acquisition quality of spectral data, but also enhances the light reception efficiency and signal stability through the uniform arrangement of the inner and outer ring optical fibers, thus providing a solid data basis for subsequent spectral analysis.

[0044] Another embodiment of the present invention provides an enhanced stability particle sample transmission near-infrared analysis method, further including defining the number of modeling sample sets as m, the number of spectral bands as b, the independent variable spectral matrix as X0 (m, 2b), and the dependent variable as Y0 (m, 1). Then X0 is a matrix of size, and Y0 is a matrix of size, and perform the acquisition steps for each sample as follows:

[0045] S5: Adjust the baffle distance H to 1.5 cm;

[0046] S6: Pour 300 ml of the sample, control the feeding by the bottom runner, and collect the spectra at 50 ms and 200 ms integration times in turn after pausing for 3 seconds for each grid rotation. Taking the first time as an example, the energy received by the inner and outer ring light-receiving optical fibers at 50 ms is I50_n and I50_w respectively, and the energy received by the inner and outer ring light-receiving optical fibers at 200 ms is I200_n and I200_w respectively; by controlling the sample feeding and acquisition time, this step ensures the uniform distribution of the sample and the consistency of spectral data. The design of pausing for 3 seconds allows the system to stabilize and prepare for the next acquisition, while the setting of different integration times is used to capture the spectral characteristics of the sample under different exposure conditions, providing more comprehensive data for subsequent data analysis.

[0047] S7: Spectral band splicing. Taking I50_n and I200_n as examples, calculate the average ratio ka of the spectrum in the non-saturated interval of I200_n to the corresponding spectral interval of I50_n. For the saturated interval of I200_n, multiply the spectrum of I50_n in the corresponding interval by ka, so as to raise the spectrum with 50 integration time to replace the saturated part under 200 integration time, obtaining I_n. Use the same method to obtain I_w. Through spectral band splicing and correction processing, the problem of spectral saturation caused by different integration times is solved. By calculating the average ratio ka and applying it to the saturated interval, the accuracy and integrity of the spectral data are ensured, providing a reliable basis for the subsequent calculation of the relative transmission spectrum.

[0048] S8: Calculate k = I_n / I_w to obtain the relative transmission spectrum k of this time. Use the same method to continue rotating the bottom turntable and repeat the above steps more than 20 times. Save the average value of the 20 relative transmission spectra k as Ki1, which is the spectrum of the current sample at H = 1.5 cm, and i is the current sample serial number. By repeating the measurement and calculating the average value, this step significantly improves the stability and reliability of the data, thereby reducing the influence of random errors and providing a more accurate description of the sample transmission characteristics, providing a solid data basis for the subsequent sample analysis. Moreover, the present invention uses the relative transmission spectrum k (k = I_n / I_w) as the independent variable, where I_n and I_w are the energy intensities received by the inner and outer ring optical fibers respectively. This innovative method effectively eliminates the spectral shift caused by the changes in sample density and light source intensity, as well as the waveform abnormality caused by the difference in the uniformity of the spectrometer CCD by comparing the energies received by the inner and outer ring optical fibers. The layout of the optical fibers and the configuration of the spectrometer in the illustration intuitively show this design intention, ensuring the accuracy and comprehensiveness of the spectral data.

[0049] S9: Adjust the baffle distance H to 2 cm and repeat steps S6 - 8 to obtain the spectrum Ki2 of the current sample at H = 2 cm. This step aims to capture the transmission characteristics of the sample under different optical path lengths. This design allows the system to evaluate the influence of the change in sample transmission characteristics with distance, providing important data for establishing a more comprehensive sample model.

[0050] S10: Merge the K1K2 spectral matrices to obtain the final spectrum Ai = [Ki1, Ki2] of the current sample. By integrating the spectral information at two distances, the final spectrum Ai provides a more complete and accurate description of the sample, which is crucial for subsequent sample identification and quantitative analysis.

[0051] In this embodiment, the number of modeling sample sets is first defined as m, and the number of spectral bands is b. An independent variable spectral matrix X0 (m, 2b) and a dependent variable Y0 (m, 1) are constructed, where X0 is a matrix of size m×2b and Y0 is a matrix of size m×1. Then, a series of detailed acquisition steps are performed, including adjusting the baffle distance H, controlling the sample feeding, collecting spectra using different integration times, and performing band splicing and calculation of relative transmission spectra on the collected spectral data. By repeating these steps and calculating the average value, the spectra Ki1 and Ki2 of the sample under different H values are obtained, and they are combined to obtain the final spectrum Ai. This method embodiment significantly improves the stability and accuracy of particle sample analysis through precise spectral acquisition and advanced data processing techniques.

[0052] Another embodiment of the present invention provides a transmission near-infrared analysis method for particle samples with enhanced stability, which further includes the following modeling steps:

[0053] S11: Additionally collect the spectra x_t (5, 2b) of 5 standard samples with known contents (these 5 samples are collected under the current usage environment, are applicable to the current environment and the current machine, and the collection environment is different from that of the previous m modeling sample sets, and the machine may also be different), and the corresponding contents are y_t (5, 1). Calculate the standard spectrum x_t_b (5, 2b) of x_t according to the distance weights of each value in y_t to the modeling sample y0. The transformation matrix is R (5, m). Define the number of modeling samples as m and the number of independent variables as 2b, then , each row of m elements in R is the inverse distance weight of the m samples in x0 to the corresponding sample in the current row. The farther the distance, the smaller the weight. Simulate the weight function according to the distribution of the dependent variables of the m samples. For example, if it conforms to a normal distribution, the Gaussian function can be used as the weight function. Define the variance of Y0 of the m modeling sample sets as σ, then

[0054] .

[0055] During the modeling process, the present invention collects the spectra x_t of 5 standard samples with known contents and calculates the standard spectrum x_t_b of x_t according to the distance weights of each value in y_t to the modeling sample y0. This step reflects the process of spectral correction of the original modeling sample set using standard samples to make the model adapt to the current environment and equipment, which is crucial for improving the adaptability and accuracy of the model. Through principal component analysis and correction of the modeling samples, the present invention significantly enhances the adaptability of the model to different environments and equipment, making it have a wider applicability and higher practical value in practical applications.

[0056] S12: Perform principal component analysis on the residuals between x_t_b and x_t, with the projection axis being W. Extract the principal components T that explain 85% of the variance, and the corresponding loadings are P

[0057] .

[0058] S13: Use the p matrix to correct the modeling sample x0 to obtain the corrected modeling sample spectrum x0_c. Use x0_c and y0 as the independent and dependent variables for modeling

[0059]

[0060] Among them, it involves collecting the spectra x_t of standard samples with known contents, and calculating the standard spectrum x_t_b of x_t according to the distance weights from each value in y_t to the modeling sample y0. Key components are extracted through principal component analysis, and these components are used to correct the modeling samples. Finally, a prediction model that can adapt to the current environment and equipment is established. This method not only improves the stability of the analysis results, but also enhances the adaptability of the model to different environments and equipment, making it have wider applicability and higher practical value in practical applications.

[0061] In summary, through a series of technical improvements, the embodiments of the present invention effectively solve the key challenges in the analysis of particulate samples, including problems such as sample density changes, light source intensity fluctuations, and spectrometer CCD uniformity differences, thereby significantly enhancing the robustness and applicability of the model.

[0062] First, by precisely controlling the focusing and collimation of the incident light and the layout of the fiber optic receiving system, the present invention ensures the high-quality collection of spectral data, enhancing the intensity and stability of the signal. Second, by adjusting the baffle distance and adopting the variable integration time sampling technique, the system can adapt to the characteristics of different samples, reducing spectral shifts caused by changes in sample density and light source intensity. In addition, through the measurement of the energy intensity of the inner and outer ring light-receiving optical fibers and the splicing of spectral bands, the system further improves the accuracy and comprehensiveness of spectral data.

[0063] In terms of data processing, the present invention effectively reduces random errors by calculating the relative transmission spectrum and the average value of multiple acquisitions, improving the repeatability and reliability of the data. Through these technical means, the present invention not only improves the accuracy of single sample analysis, but also enhances the adaptability of the model to different environments and equipment through principal component analysis and the correction of modeling samples, making it have wider applicability and higher practical value in practical applications.

[0064] Generally speaking, through technological innovation, the embodiments of the present invention provide a solution for transmission near-infrared analysis of particulate samples with enhanced stability, which can meet the requirements for rapid, efficient, and accurate analysis in industrial production, and has important practical application value and broad market prospects.

[0065] The above are only the embodiments of the present invention, and do not limit the patent scope of the present invention accordingly. Any equivalent structural or equivalent process transformation made by using the content of the specification and drawings of the present invention, or directly or indirectly applied in other related technical fields, shall be equally included in the patent protection scope of the present invention.

Claims

1. A transmission near-infrared analysis method for particulate samples with enhanced stability, characterized in that, It includes the following steps: S1: The incident light is focused and collimated, the spot size is reduced, the intensity is increased, and after passing through the antireflection film glass baffle and the sample, it hits the fiber optic carrier plane baffle and is received by N fibers on it, where N is 16; S2: The servo motor controls the lateral movement of the glass baffle to control the distance H between the glass baffle and the fiber optic carrier plane baffle. Each sample will be subjected to spectral acquisition once at a distance h1 and once at a distance h2, where the difference between h1 and h2 is 0.5 cm, and both h1 and h2 are selected at a gradient of 0.5 cm within the range of 2 - 3 cm; S3: The light-receiving fibers are 16 fibers evenly arranged on two circumferences with radii r1 and r2 from the detection center, with 8 fibers in each of the inner and outer circles of light-receiving fibers. The energy intensities of the inner and outer circle light-receiving fibers are i_n and i_w respectively. Among them, the difference between r1 and r2 is 0.5 cm, and both r1 and r2 are selected within the range of 20 mm; S4: The spectrometer uses variable integration time sampling, and the bottom turntable controls the material feeding. After pausing for 3 - 5 seconds for each rotation, the spectra are collected successively with integration times of 50 ms and 200 ms; It also includes: defining the number of modeling sample sets as m, the number of spectral bands as b, and the independent variable spectral matrix as X0(m, 2b), the dependent variable is Y0(m, 1), then X0 is a matrix of size, Y0 is a matrix of size, and it includes the acquisition steps for each sample as follows: S5: Adjust the baffle distance H to h1; S6: Pour 300 ml of the sample, and the bottom turntable controls the material feeding. After pausing for 3 seconds for each rotation, the spectra are collected successively with integration times of 50 ms and 200 ms. The energies received by the inner and outer circle light-receiving fibers at 50 ms are I50_n and I50_w respectively, and the energies received by the inner and outer circle light-receiving fibers at 200 ms are I200_n and I200_w respectively; S7: Spectral band splicing, calculate the average ratio ka of the spectra in the non-saturated interval of I200_n and the corresponding spectral interval of I50_n. For the saturated interval of I200_n, the spectra of I50_n in the corresponding interval are multiplied by ka, so as to raise the spectra at 50 integration time to replace the saturated part at 200 integration time, and obtain I_n. In the same way, I_w is obtained; S8: Calculate k = I_n / I_w to obtain the relative transmission spectrum k of this time. In the same way, continue to rotate the bottom turntable and repeat the above steps more than 20 times. Save the average value of 20 relative transmission spectra k as Ki1, which is the spectrum of the current sample at H = h1, and i is the serial number of the current sample; S9: Adjust the baffle distance H to h2, and repeat steps S6 - 8 to obtain the spectrum Ki2 of the current sample at H = h2; S10: Merge the K1K2 spectral matrices to obtain the final spectrum Ai = [Ki1, Ki2] of the current sample.

2. A computer-readable storage medium, characterized in that, There is a computer program stored, and when the computer program is executed, it implements the method described in claim 1.

3. A computer device, characterized in that, It includes a processor, a memory, and a computer program stored on the memory. When the processor executes the computer program, it implements the method described in claim 1 as above.

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