Polarized Raman spectrum measurement method for microfibril orientation of plant cell wall
By constructing a nonlinear parameter fitting model and a polarization Raman spectroscopy measurement method with forced symmetry processing, the problem of unstable detection results in the existing technology is solved, and efficient and accurate microfiber orientation measurement is achieved.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- ANHUI CONCH IND TECHNOLOGY RESEARCH INSTITUTE CO LTD
- Filing Date
- 2025-12-17
- Publication Date
- 2026-04-21
AI Technical Summary
Existing Raman spectroscopy techniques for determining microfiber orientation suffer from several problems, including spectral attenuation due to multiple detections, sample damage caused by excessive local laser power, long data acquisition time, cumbersome manual control of rotating polarizers, complex fitting algorithms, high noise, and unstable detection results due to experimental errors.
The polarization Raman spectroscopy method is adopted. By constructing a nonlinear parameter fitting model, optimizing it using the constraint rules and error function initialized by Raman tensor components, and combining forced symmetry processing, the polarizer rotation is automatically controlled to obtain data with high signal-to-noise ratio, achieving fitting convergence and result stability.
It improves the fitting convergence rate, ensures the stability and reliability of the detection results, and enables accurate measurement of microfibril orientation in complex biomaterials, thereby improving detection efficiency and accuracy.
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Figure CN121899102A_ABST
Abstract
Description
Technical Field
[0001] This invention belongs to the field of spectroscopic measurement technology, specifically relating to a polarization Raman spectroscopy method for measuring the orientation of plant cell wall microfibrils. Background Technology
[0002] Raman spectroscopy can identify the main chemical components in plant cell walls, such as cellulose, hemicellulose, and lignin, and provide direct characterization of the crystal orientation of microfibrils. In particular, polarized Raman spectroscopy can obtain the orientation information of microfibrils by analyzing Raman scattering signals in different polarization directions. Compared with traditional methods, it has advantages such as high spatial resolution, non-destructive nature, and the ability to perform measurements at the microscale.
[0003] However, the application of Raman spectroscopy in microfibril orientation determination still faces challenges. For example, multiple detections cause significant spectral attenuation, affecting the results; excessive local laser power damages the sample; excessive data collection leads to long acquisition times; and manual rotation of the polarizer is required, making the detection process cumbersome and difficult to control simultaneously. More importantly, while there are attempts to use Raman tensor models for microfibril angle measurement, two major technical obstacles exist when dealing with complex, heterogeneous biological materials like plant cell walls with strong fluorescence backgrounds: 1) Conventional nonlinear fitting algorithms are difficult to converge due to model complexity and high signal noise, or converge to physically meaningless local optima; 2) Even if the fitting converges, experimental errors often result in non-physical asymmetries in the obtained Raman tensor, leading to large deviations in the calculated microfibril angles, low reliability, and difficulty in reproducing the results across different laboratories. Furthermore, Raman tensor modeling methods need further optimization to ensure better fitting convergence after detection, guaranteeing the stability and reliability of the detection results. Summary of the Invention
[0004] The purpose of this invention is to provide a polarization Raman spectroscopy measurement method for the orientation of plant cell wall microfibrils, in order to solve the technical problems of poor fitting convergence effect and insufficient stability and reliability of detection results in the existing technology due to the complexity of nonlinear fitting algorithms, susceptibility to noise and experimental errors.
[0005] The method for measuring the orientation of plant cell wall microfibrils by polarization Raman spectroscopy includes: Step 1: Acquire polarization Raman spectroscopy data of the sample to be tested; Step 2: Obtain the required data based on the data acquisition results of polarization Raman spectroscopy; Step 3: Perform nonlinear parameter fitting on the required data, obtain the optimal parameter combination and corresponding coefficients by minimizing the error function, and perform forced symmetry post-processing on the optimal Raman tensor matrix; Step 4: Calculate the microfiber angle corresponding to the symmetric tensor parameters; In step four, based on the symmetric tensor matrix, the angle required to rotate it to the principal axis direction is calculated; the calculated angle value is then normalized.
[0006] Preferably, step three includes: Step 1: Construct a nonlinear relationship model for fitting nonlinear parameters; Step 2: Initialize the parameters for nonlinear fitting based on the constraint rules for Raman tensor component initialization; Step 3: Construct an error function as the optimization objective for fitting nonlinear parameters; Step 4: Starting with the initial parameters obtained in Step 2, perform nonlinear optimization, iteratively optimize the corresponding parameters of the nonlinear relationship model, and obtain the optimal parameter combination that minimizes the global error function value. Step 5: Calculate the final model prediction value of the nonlinear relationship model using the optimal parameter combination, and then calculate the goodness of fit and correlation coefficient based on the model prediction value and the experimental measurement value; Step 6: Perform forced symmetry post-processing on the output Raman tensor matrix.
[0007] Preferably, in step 1, the nonlinear relationship model between microfiber orientation and polarization Raman intensity is as follows:
[0008] Where: I represents Raman intensity For the Raman tensor, Let be the polarization vector of the incident light. Let be the polarization vector of the scattered light, A be the scaling factor, and a, b, c, and d be the Raman tensor components in different directions.
[0009] Preferably, in step 2, constraint rules for the initialization of Raman tensor components are constructed, including: 1) Let the initial values of the dominant components a and d be: a0 = d0 = k1 * I avg , where I avg The current measurement point is 1097 cm at all polarization angles. -1 The average peak intensity, k1 is the initial coefficient of the dominant components a and d, with a value range of [0.8, 1.2]; 2) Let the initial values of the cross components b and c be: b0 = c0 = k2 * a0, where k2 is the initialization coefficient of the cross components b and c, and the value range is [-0.2, 0.2]. 3) Set the scaling factor A=1.
[0010] Preferably, in step 3, the error function is: , among which, I model I is the model's predicted value.exp These are experimentally measured values and error function values. The objective that needs to be minimized in the optimization process.
[0011] Preferably, in step 6, the original matrix of the Raman tensor matrix in the optimal parameter combination is: The transformation formula for symmetric matrices is: Where R is the original matrix of the Raman tensor, R sym The transformed symmetric matrix is: .
[0012] Preferably, in step one, a Raman spectrometer is used for measurement; during measurement, the polarizer is rotated from 0° to 180° in 10° increments, and 1097 cm⁻¹ samples are collected at each angle corresponding to the vibrational mode of the cellulose Iβ crystal. -1 The Raman characteristic peak signal.
[0013] Preferably, in step two, the required data includes the corresponding incident light angle, scattered light angle, and peak intensity of the corresponding Raman characteristic peak signal.
[0014] The technical advantages of this invention are as follows: Conventional random or zero-value initialization in existing technologies causes the optimization algorithm to fall into an "error search space" at the initial stage, reducing optimization efficiency. This invention, based on parameter initialization constraints using cellulose crystal symmetry and a cell wall structure model, provides a "high-quality starting point" with a high probability of approaching the global optimum. Therefore, this invention improves the fitting convergence success rate for complex samples such as plant cell walls with high fluorescence and strong scattering. Simultaneously, this invention uses symmetric matrix transformation on the tensor matrix used in fitting, re-anchoring the measurement results to fundamental physical principles, actively removing non-physical components introduced by noise, and ensuring that the tensor used for calculation strictly meets crystallographic requirements regardless of how small the experimental conditions change. Combined with a reasonable nonlinear fitting algorithm and error optimization algorithm, this invention overcomes the technical problems of existing technologies, such as poor fitting convergence due to the complexity of nonlinear fitting algorithms, susceptibility to noise and experimental errors, and insufficient stability and reliability of detection results. Attached Figure Description
[0015] Figure 1 This is a basic flowchart of a polarization Raman spectroscopy measurement method for the orientation of plant cell wall microfibrils according to the present invention.
[0016] Figure 2 This is a diagram showing the orientation of the microfibrils of the cell wall in the sample of Example 1 of the present invention.
[0017] Figure 3 This is a diagram showing the orientation of the microfibrils of the cell wall in the sample of Example 2 of the present invention.
[0018] Figure 4This is a nonlinear fitting curve of the sample in Example 3 of the present invention. Figure 5 This is a nonlinear fitting polar coordinate diagram of the sample in Example 3 of the present invention. Detailed Implementation
[0019] The following detailed description of the embodiments, with reference to the accompanying drawings, will further illustrate the specific implementation of the present invention, in order to help those skilled in the art to have a more complete, accurate, and in-depth understanding of the inventive concept and technical solution of the present invention.
[0020] like Figures 1-5 As shown, this invention provides a polarization Raman spectroscopy method for measuring the orientation of plant cell wall microfibrils, comprising the following steps: Step 1: Acquire polarization Raman spectroscopy data of the sample to be tested.
[0021] The specific steps are as follows: a Raman spectrometer is used for measurement; during measurement, the polarizer is rotated from 0° to 180° in 10° increments, and Raman characteristic peak signals corresponding to the vibrational modes of cellulose Iβ crystals are collected at each angle.
[0022] At 1097 cm -1 At the Raman characteristic peak signal, the vibrational modes of cellulose Iβ crystal exhibit specific polarization response characteristics; therefore, the Raman spectral information acquired during measurement must include the 1097 cm⁻¹ peak. -1 Raman characteristic peak signals of cellulose.
[0023] The Raman spectrometer is a confocal micro Raman spectrometer, equipped with a 532nm laser, a 50x objective lens, and an electric polarization stage.
[0024] Step 2: Obtain the required data based on the data acquisition results of polarization Raman spectroscopy.
[0025] This step specifically includes: reading the Raman spectrum information and calculating the 1097 cm⁻¹. -1 The peak intensity corresponding to the Raman characteristic peak signal is obtained; the polarizer rotation angle is converted to radians, and then the incident light angle and scattered light angle in the Raman spectroscopy measurement are calculated. The required data include the corresponding incident light angle, scattered light angle, and peak intensity of the corresponding Raman characteristic peak signal.
[0026] Step 3: Perform nonlinear parameter fitting on the required data, obtain the optimal parameter combination and corresponding coefficients by minimizing the error function, and perform forced symmetry post-processing on the optimal Raman tensor matrix.
[0027] This step includes the following sub-steps: Step 1: Construct a nonlinear relationship model for fitting nonlinear parameters.
[0028] The nonlinear relationship model between microfiber orientation and polarization Raman intensity is as follows:
[0029] Where: I represents Raman intensity For the Raman tensor, Let be the polarization vector of the incident light. Let be the polarization vector of the scattered light, A be the scaling factor, and a, b, c, and d be the Raman tensor components in different directions.
[0030] Step 2: Initialize the parameters for nonlinear fitting based on the constraint rules for Raman tensor component initialization.
[0031] Theoretical basis: The dominant components (a, d) of the Raman tensor corresponding to the vibrational modes of cellulose Iβ crystals should theoretically be directly related to the average scattering intensity of that mode. The Raman scattering intensity is directly proportional to the dominant components of the tensor. Structural a priori: In a nearly ordered arrangement of microfibrils, the cross component characterizing directional coupling should be a small quantity, the range of which is determined by the maximum possible perturbation of the cell wall structural heterogeneity. Since the microfibril orientation has a dominant direction in the intact S2 layer of the cell wall, the off-diagonal cross component (b, c) of its tensor should be much smaller than the dominant component, and due to the quasi-symmetry of the structure, its value should fluctuate slightly around zero.
[0032] Based on the aforementioned physical theoretical foundation and structural priors, this step constructs the constraint rules for initializing the Raman tensor components, as follows: 1) Let the initial values of the dominant components a and d be: a0 = d0 = k1 * I avg , where I avg The current measurement point is 1097 cm at all polarization angles. -1 The average peak intensity, k1 is the initial coefficient of the dominant components a and d, with a value range of [0.8, 1.2]. This rule is a constraint derived from the principle that "Raman scattering intensity is directly proportional to the dominant components of the tensor".
[0033] 2) Let the initial values of the cross components b and c be: b0 = c0 = k2 * a0, where k2 is the initialization coefficient of the cross components b and c, with a value range of [-0.2, 0.2]. This rule is a constraint derived from the fact that "in a nearly ordered arrangement of microfibrils, the cross component representing directional coupling should be a small quantity, the range of which is determined by the maximum possible degree of perturbation of the heterogeneity of the cell wall structure".
[0034] 3) Set the scaling factor A=1. This setting normalizes the system's optical response, focusing the optimization objective on the tensor itself.
[0035] Research has revealed that the main reason for the non-convergence of Raman tensor fitting in the complex system of plant cell walls, which is highly ordered but also contains local perturbations, lies in the fact that conventional random or zero-value initialization causes the nonlinear optimization algorithm to fall into an "erroneous search space" far from the physical true solution at the initial stage. Therefore, this step transforms the inherent structural characteristics of cellulose microfibrils in plant cell walls into prior constraints for the optimization algorithm.
[0036] Initialization through constraints is equivalent to introducing strong physical constraints, providing the optimization algorithm with a "high-quality starting point" that has a high probability of approaching the global optimum. This enables the method to improve the fitting convergence success rate of complex samples such as plant cell walls with high fluorescence and strong scattering; and allows the Raman tensor model used in this step to be stably and reliably applied to the quantitative measurement of plant cell walls for the first time.
[0037] Step 3: Construct an error function as the optimization objective for fitting nonlinear parameters.
[0038] The error function is: Used to calculate the model prediction value I model Compared with experimental measurement value I exp The sum of squares of the differences between them, i.e., the error function value optimized by the least squares criterion. The objective that needs to be minimized in the optimization process.
[0039] Step 4: Starting with the initialization parameters obtained in Step 2, call the nonlinear optimization program to iteratively optimize the corresponding parameters of the nonlinear relationship model, and obtain the error function value. The optimal parameter combination for global minimization is params={A,a,b,c,d}.
[0040] Step 5: Use the optimal parameter combination params to calculate the final model prediction value of the nonlinear relationship model, and then calculate the goodness of fit and correlation coefficient based on the model prediction value and the experimental measurement value.
[0041] Step 6: Perform forced symmetry post-processing on the output Raman tensor matrix.
[0042] This step is based on the original matrix of the Raman tensor matrix in the optimal parameter combination. Then, a symmetric matrix transformation is performed to obtain the corresponding symmetric matrix. The transformation formula for a symmetric matrix is: Where R is the original matrix of the Raman tensor, R sym The transformed symmetric matrix is: .
[0043] One of the fundamental reasons for the large deviation and poor reproducibility of the measurement results of the existing technology is that: even if the fitting algorithm converges, due to the inherent small alignment error of the polarization optical system, the local birefringence effect of the sample, and the nonlinear response of the photodetector, the experimental noise that cannot be completely eliminated, the Raman tensor matrix of the fitting output often violates the symmetry required for it to be a second-order tensor (i.e. b≠c).
[0044] In response, this step actively removes the non-physical components introduced by noise through symmetric matrix transformation, ensuring that the tensor used for the final calculation strictly meets crystallographic requirements.
[0045] Step 4: Calculate the microfiber angle corresponding to the symmetric tensor parameters.
[0046] This step includes: calculating the angle required to rotate the tensor matrix to the principal axis direction based on the symmetric tensor matrix; and normalizing the calculated angle value.
[0047] Overall, the innovation of this method lies in providing a dedicated fitting analytical algorithm that implements the following steps: automatically loading data, initializing according to preset rules, performing nonlinear fitting, triggering symmetry post-processing, and calculating and outputting the microfiber angle.
[0048] The method was further verified through the following specific embodiments.
[0049] Example 1: Five-year-old poplar sapwood was taken and cut into strips measuring 10mm × 10mm × 25mm. The strips were then placed in boiling water (90-100℃) to soften them until they completely sank to the bottom of the container, indicating that softening was complete. A flatbed microtome was used to prepare tangential sections with a thickness of 5±4.5μm. The sections were then sandwiched between two glass slides. To eliminate the influence of air on the test results, the edges of the coverslips were sealed with sealant, neutral resin, nail polish, or transparent tape, thus obtaining a sample in a sealed environment.
[0050] The sample to be tested was measured using the polarization Raman spectroscopy method described above.
[0051] Before measurement, a confocal micro Raman spectroscopy system equipped with a 532nm laser, a 50x objective lens, and an electric polarization rotating stage was used for testing. The laser power was set to 5mW to avoid sample damage. During measurement, 20 polarization combinations were randomly selected from 0° to 90°, and the Raman characteristic peak signal of cellulose at 1097 cm⁻¹ was acquired at each angle. The CCD integration time was set to 1 second.
[0052] Then, nonlinear fitting is performed based on the parameter fitting method in step three, and finally the microfiber angle corresponding to the angle is calculated based on the symmetric tensor parameters.
[0053] The microfibril orientation pattern of the cell wall of the sample obtained by this measurement is as follows: Figure 2 As shown in the figure, this figure illustrates the overall orientation of the microfibrils in the poplar wood sample under test, and demonstrates the overall distribution of microfibril orientation within the selected area of the poplar chord section.
[0054] The blue horizontal lines in the image represent the microfibril angles of individual pixels. Their direction intuitively reflects the orientation of the main axis of the microfibril arrangement. The more vertical the blue lines are, the closer the microfibril angle is to 90°. A rightward deviation of the blue lines indicates a positive microfibril angle, while a leftward deviation indicates a negative microfibril angle. As can be seen from the image, the distribution of microfibril orientation in the selected area basically conforms to the structural characteristics of the cell wall. The blue horizontal lines are concentrated between 60° and 70°, accounting for approximately 62%, which highly matches the typical angle of the spiral arrangement of microfibrils in the S2 layer of the secondary cell wall.
[0055] To assess the fitting efficiency and stability of Example 1, this example also employs a random initialization method for comparative experiments, and the comparison table of fitting performance data is shown in Table 1.
[0056] Table 1: Comparison of data related to fitting performance
[0057] As shown in Table 1, the method of the present invention significantly improves the fitting convergence rate from 45% to 95%, which is more than double, demonstrating its effectiveness in solving the nonlinear fitting non-convergence problem in complex biological samples.
[0058] Example 2: This embodiment uses 5-year-old cedar sapwood as the test object, and the specific operation steps of this embodiment are the same as those of embodiment 1.
[0059] The microfibril orientation pattern of the cell wall of the sample obtained by this measurement is as follows: Figure 3 As shown in the figure, the overall orientation of the microfibrils in the selected area of Chinese fir can be seen. Compared with the overall orientation of the microfibrils in poplar, it can be found that the arrangement of microfibrils in the selected area of Chinese fir is more disordered.
[0060] Example 3: This embodiment uses mature cotton linter fibers as the test object. The specific operation steps of this embodiment differ from those of Embodiment 1 in that: during sample preparation, the selected fibers are arranged in parallel and sandwiched between two coverslips. To eliminate the influence of air on the test results, the edges of the coverslips are sealed with sealant, neutral resin, nail polish, or transparent tape to ensure that the sample to be tested is in a sealed environment. Other operation steps are the same as in Embodiment 1.
[0061] During the measurement process in Example 3, the nonlinear fitting curve of the corresponding test sample is shown in the figure below. Figure 4 As shown, the corresponding polar coordinate graph is shown in Figure 1. Figure 5 As shown. The sample to be tested in Example 3 was a single-cell primary cell wall sample. The microfibril orientation of this sample was calibrated using the method of this patent. The fitting results showed that the model correlation coefficient was 0.9671, indicating that the model can accurately reflect the arrangement of microfibrils inside the fiber. The polar coordinate plot visually shows the distribution of normalized Raman intensity with polarization angle, which is consistent with the expected structure of highly oriented microfibrils.
[0062] The above embodiments demonstrate that the method can stably and reliably obtain measurement results that conform to actual physical laws in the measurement of plant cell wall microfibrils orientation, avoiding the technical problems of insufficient stability and reliability of detection results in the prior art.
[0063] The present invention has been described above by way of example with reference to the accompanying drawings. Obviously, the specific implementation of the present invention is not limited to the above-described manner. Any non-substantial improvements made using the inventive concept and technical solution of the present invention, or the direct application of the inventive concept and technical solution of the present invention to other occasions without modification, are all within the protection scope of the present invention.
Claims
1. A polarization Raman spectroscopy method for measuring the orientation of plant cell wall microfibrils, characterized in that, include: Step 1: Acquire polarization Raman spectroscopy data of the sample to be tested; Step 2: Obtain the required data based on the data acquisition results of polarization Raman spectroscopy; Step 3: Perform nonlinear parameter fitting on the required data, obtain the optimal parameter combination and corresponding coefficients by minimizing the error function, and perform forced symmetry post-processing on the optimal Raman tensor matrix; Step 4: Calculate the microfiber angle corresponding to the symmetric tensor parameters; In step four, based on the symmetric tensor matrix, the angle required to rotate it to the principal axis direction is calculated; The calculated angle values are normalized.
2. The polarization Raman spectroscopy method for measuring the orientation of plant cell wall microfibrils according to claim 1, characterized in that, Step three includes: Step 1: Construct a nonlinear relationship model for fitting nonlinear parameters; Step 2: Initialize the parameters for nonlinear fitting based on the constraint rules for Raman tensor component initialization; Step 3: Construct an error function as the optimization objective for fitting nonlinear parameters; Step 4: Starting with the initial parameters obtained in Step 2, perform nonlinear optimization, iteratively optimize the corresponding parameters of the nonlinear relationship model, and obtain the optimal parameter combination that minimizes the global error function value. Step 5: Calculate the final model prediction value of the nonlinear relationship model using the optimal parameter combination, and then calculate the goodness of fit and correlation coefficient based on the model prediction value and the experimental measurement value; Step 6: Perform forced symmetry post-processing on the output Raman tensor matrix.
3. The polarization Raman spectroscopy method for measuring the orientation of plant cell wall microfibrils according to claim 2, characterized in that, In step 1, the nonlinear relationship model between microfiber orientation and polarization Raman intensity is as follows: Where: I represents Raman intensity For the Raman tensor, Let be the polarization vector of the incident light. Let be the polarization vector of the scattered light, A be the scaling factor, and a, b, c, and d be the Raman tensor components in different directions.
4. The polarization Raman spectroscopy method for measuring the orientation of plant cell wall microfibrils according to claim 2, characterized in that, In step 2, the constraint rules for initializing the Raman tensor components were constructed, including: 1) Let the initial values of the dominant components a and d be: a0 = d0 = k1 * I avg , where I avg The current measurement point is 1097 cm at all polarization angles. -1 The average peak intensity, k1 is the initial coefficient of the dominant components a and d, with a value range of [0.8, 1.2]; 2) Let the initial values of the cross components b and c be: b0 = c0 = k2 * a0, where k2 is the initialization coefficient of the cross components b and c, and the value range is [-0.2, 0.2]. 3) Set the scaling factor A=1.
5. The polarization Raman spectroscopy method for measuring the orientation of plant cell wall microfibrils according to claim 2, characterized in that, In step 3, the error function is: , among which, I model I is the model's predicted value. exp These are experimentally measured values and error function values. The objective that needs to be minimized in the optimization process.
6. The polarization Raman spectroscopy method for measuring the orientation of plant cell wall microfibrils according to claim 2, characterized in that, In step 6, the original matrix of the Raman tensor matrix in the optimal parameter combination is: The transformation formula for symmetric matrices is: Where R is the original matrix of the Raman tensor, R sym The transformed symmetric matrix is: .
7. The polarization Raman spectroscopy method for measuring the orientation of plant cell wall microfibrils according to claim 1, characterized in that, In step one, a Raman spectrometer was used for measurement. During the measurement, the polarizer was rotated from 0° to 180° in 10° increments, and 1097 cm⁻¹ samples were collected at each angle corresponding to the vibrational mode of the cellulose Iβ crystal. -1 The Raman characteristic peak signal.
8. The polarization Raman spectroscopy method for measuring the orientation of plant cell wall microfibrils according to claim 1, characterized in that, In step two, the required data include the corresponding incident light angle, scattered light angle, and peak intensity of the corresponding Raman characteristic peak signal.