A method and system for constructing a polysaccharide fingerprint of a polysaccharide raw material

Abstract

The present application relates to the technical field of polysaccharide detection, and particularly relates to a method and system for constructing a polysaccharide fingerprint of polysaccharide raw materials. The method first decomposes spectral data of the polysaccharide raw materials to obtain principal components and waveband intervals; determines the importance score of the waveband intervals in the full waveband for the structural differences of the polysaccharide; screens the main waveband intervals in the principal components; determines the structural influence index of the main waveband intervals; according to the structural influence index, corrects the matching degree of the measured value and the theoretical value of the CCS value of the polysaccharide raw materials to obtain the verification accuracy of the main waveband intervals; based on the verification accuracy, obtains the main waveband intervals affected by the matrix interference, and through the matrix signal stripping by the deconvolution splitting technology, extracts the true characteristic peaks of the polysaccharide raw materials to construct the fingerprint. The present application improves the anti-interference ability of the fingerprint of the polysaccharide raw materials.

Description

Technical Field

[0001] This invention relates to the field of polysaccharide detection technology, specifically to a method and system for constructing polysaccharide fingerprints in polysaccharide raw materials. Background Technology

[0002] Natural polysaccharides are branched polymers formed by various monosaccharides linked by different glycosidic bonds. Their molecular weight distribution, monosaccharide composition, and glycosidic bond types exhibit significant batch-to-batch variations. In the pharmaceutical, food, and cosmetic fields, the bioactivity of polysaccharides is precisely related to their structure. Polysaccharide fingerprinting is a quality control method that uses characteristic peaks to confirm the "identity" of polysaccharides. However, polysaccharides have complex structures (many isomers), require high detection sensitivity, and necessitate chemometric support.

[0003] Establishing a comprehensive and stable "fingerprint" information system to characterize the polysaccharide components (molecular weight distribution, monosaccharide composition, glycosidic bond type, structural features, etc.) in specific polysaccharide raw materials is crucial for identifying authenticity, evaluating quality uniformity, tracing origin, and monitoring process stability. In the process of constructing polysaccharide fingerprint profiles based on spectral data, differences directly affect the pharmacological activities of polysaccharides (such as immunomodulation and antitumor activity). Failure to differentiate these fingerprints will lead to a breakdown in the correlation between quality control and efficacy.

[0004] In existing technologies, the construction of polysaccharide fingerprints faces numerous challenges: due to the complex structure of polysaccharides, overlapping of characteristic peaks is common in the preliminary spectral data, and traditional methods, which simply screen band interval combinations, are insufficient to accurately capture key structural information; at the same time, the separation and detection process is easily affected by matrix co-eluenting, resulting in insufficient spectral separation. Furthermore, when dealing with overlapping characteristic peaks, the lack of quantitative analysis of the structural influence index and external verification methods makes it impossible to effectively distinguish the structural origin of overlapping peaks, causing the interfered band intervals to be mistakenly included in the analysis. Ultimately, the constructed fingerprint spectrum cannot fully reflect the fine structural characteristics of polysaccharides, seriously affecting the accuracy of quality control and the correlation analysis of active ingredients. Summary of the Invention

[0005] To address the technical problem of failing to effectively distinguish the structural origins of overlapping peaks when constructing polysaccharide fingerprint spectra, thus preventing the inclusion of interfering bands in the analysis, this invention aims to provide a method and system for constructing polysaccharide fingerprint spectra from polysaccharide raw materials. The specific technical solution adopted is as follows:

[0006] In a first aspect, embodiments of the present invention provide a method for constructing a polysaccharide fingerprint spectrum in a polysaccharide raw material, the method comprising:

[0007] Acquire the spectral data of the polysaccharide raw material; decompose the spectral data to obtain multiple principal components and the band range of each principal component;

[0008] Based on the structural differences, transmittance, and distribution of different band intervals in different principal components, the importance score of each band interval to the structural differences of polysaccharides across the entire band is determined; combined with the importance score and the contribution rate of the principal components, the main band intervals in the principal components are screened out.

[0009] Based on the positional separation characteristics of adjacent characteristic peaks in each major band interval and the disordered distribution of spectral data in the band interval, the structural influence index of the major band interval is determined.

[0010] Based on the structure influence index, the matching degree between the measured and theoretical CCS values ​​of the polysaccharide raw material is corrected to obtain the verification accuracy of the main band intervals. Based on the verification accuracy, the main band intervals affected by matrix interference are obtained. For the main band intervals affected by matrix interference, the matrix signal is stripped off by deconvolution splitting technology, the true characteristic peaks of the polysaccharide raw material are extracted, and a fingerprint spectrum is constructed.

[0011] Secondly, a system for constructing polysaccharide fingerprint profiles in polysaccharide raw materials is provided, the system comprising the following modules:

[0012] The data preprocessing module is used to acquire the spectral data of the polysaccharide raw material; decompose the spectral data to obtain multiple principal components and the band range of each principal component;

[0013] The band interval screening module is used to determine the importance score of the band interval to the polysaccharide structural differences in the whole band based on the structural differences, transmittance, and distribution of the band interval in different principal components. Combining the importance score and the contribution rate of the principal components, the main band intervals in the principal components are screened out.

[0014] The analysis module is used to separate features based on the positions of adjacent characteristic peaks in each major band interval and the disordered distribution of spectral data in the band interval, and to determine the structural influence index of the major band interval.

[0015] The fingerprint spectrum construction module is used to correct the matching degree between the measured and theoretical values ​​of the CCS value of the polysaccharide raw material according to the structure influence index, and obtain the verification accuracy of the main band intervals; based on the verification accuracy, the main band intervals affected by matrix interference are obtained; for the main band intervals affected by matrix interference, the matrix signal is stripped off by deconvolution splitting technology, the true characteristic peaks of the polysaccharide raw material are extracted, and the fingerprint spectrum is constructed.

[0016] Thirdly, embodiments of the present invention provide an electronic device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, it implements the various possible implementations of the first aspect.

[0017] Fourthly, embodiments of the present invention provide a computer program product comprising: computer program code, which, when executed on a computer, causes the computer to perform the method described in the first aspect or any possible implementation thereof.

[0018] Fifthly, embodiments of the present invention provide a computer-readable storage medium having a computer program stored thereon, which, when executed in a computer, causes the computer to perform the various possible implementations of the first aspect.

[0019] The embodiments of the present invention have at least the following beneficial effects:

[0020] This invention decomposes the spectral data into several principal components from the initial detection data. Then, combining the characteristics of different band intervals within these principal components, several band interval combinations are selected to lay the foundation for subsequent analysis. Furthermore, addressing the issue of overlapping characteristic peaks caused by complex polysaccharide structures, the structural influence index of each major band interval is analyzed to quantitatively assess the characterization ability of each band to polysaccharide structural differences, providing a basis for accurately identifying key structural features. Based on this, CCS values ​​from ion mobility mass spectrometry are integrated to verify the matching of overlapping characteristic peaks, accurately identifying the major band intervals with "significant discrepancies" due to matrix interference, laying the foundation for eliminating matrix interference and purifying effective signals. Finally, the characteristic peaks of these interfered major band intervals are specifically processed to supplement the spectral data, providing high-quality data support for the accurate differentiation of polysaccharide structures. This results in the final constructed fingerprint spectrum of the polysaccharide raw material possessing superior characteristics such as more distinct features, stronger anti-interference ability, and higher structural resolution accuracy, providing a reliable chemical characterization basis for the identification, classification, and quality evaluation of polysaccharide raw materials. Attached Figure Description

[0021] To more clearly illustrate the technical solutions and advantages in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for constructing a polysaccharide fingerprint spectrum in a polysaccharide raw material according to an embodiment of the present invention;

[0023] Figure 2 This is a schematic diagram of a sample detection spectrum provided in one embodiment of the present invention;

[0024] Figure 3This is a system block diagram of a polysaccharide fingerprinting system for polysaccharide raw materials, provided in one embodiment of the present invention. Detailed Implementation

[0025] To further illustrate the technical means and effects of the present invention in achieving the intended purpose, the following detailed description, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of a method and system for constructing polysaccharide fingerprints in polysaccharide raw materials according to the present invention.

[0026] In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments may be combined in any suitable form.

[0027] In the description of the embodiments of the present invention, unless otherwise stated, " / " means "or". For example, A / B can mean A or B. The "and / or" in the text is merely a description of the relationship between related objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist simultaneously, and B exists alone. In addition, in the description of the embodiments of the present invention, "multiple" means two or more.

[0028] Hereinafter, the terms "first" and "second" are used for descriptive purposes only and should not be construed as implying or suggesting relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature.

[0029] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0030] The embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art will recognize that, with technological advancements and the emergence of new scenarios, the technical solutions provided by the embodiments of the present invention are also applicable to similar technical problems.

[0031] The following description, in conjunction with the accompanying drawings, details the specific scheme of the method and system for constructing polysaccharide fingerprints in polysaccharide raw materials provided by the present invention.

[0032] Please see Figure 1 The diagram illustrates a flowchart of a method for constructing a polysaccharide fingerprint spectrum in a polysaccharide raw material according to an embodiment of the present invention. The method includes the following steps:

[0033] Step S100: Obtain the spectral data of the polysaccharide raw material; decompose the spectral data to obtain multiple principal components and the band range of each principal component.

[0034] Polysaccharide fingerprinting in polysaccharide raw materials refers to the characteristic spectra or datasets of polysaccharide samples obtained through specific analytical techniques. These specific analytical techniques include chromatography, mass spectrometry, and nuclear magnetic resonance. These fingerprint spectra can effectively reflect the composition, structural characteristics, and relative abundance of polysaccharides, thus serving as a basis for polysaccharide identification, quantitative analysis, and quality control. In this embodiment of the invention, the analysis of cassia seed polysaccharides is selected. By controlling acid concentration, temperature, and time, the polysaccharides are selectively degraded into oligosaccharide fragments that retain linkage site information, rather than completely dissociating into monosaccharides; this facilitates subsequent analysis of structural isomerism. Specifically, in this embodiment of the invention, the polysaccharide raw material is cassia seed polysaccharide, which is a mixture of polysaccharides extracted from the dried, mature seeds of the legume *Cassia tora* or *Cassia obtusifolia*.

[0035] First, the polysaccharide raw material was processed and purified. Then, FT-IR spectra were acquired as the spectral data of the polysaccharide raw material. Preprocessing of the spectral data included baseline correction, smoothing and denoising, and normalization to eliminate spectral drift and retain absorption peaks that accurately reflect the polysaccharide structure, providing reliable raw data for subsequent analysis. Please refer to [link to relevant documentation]. Figure 2 , Figure 2 This is a schematic diagram of the sample detection spectrum.

[0036] The core characteristics of the preliminary spectral data are reflected in the continuity and structural correlation across the entire wavelength range. The continuity across the entire wavelength range is manifested in the continuous absorption signal from low wavenumber to high wavenumber, which contains the vibrational information of all chemical bonds in the sample molecules of cassia seed polysaccharide raw material. Among them, the chemical bonds in the sample molecules of cassia seed polysaccharide raw material include CO, OH, and CC. The structural correlation refers to the implicit correlation between the absorption peak intensities of different wavenumbers, such as the coupling effect between hydroxyl stretching vibration and sugar ring skeleton vibration.

[0037] Principal Component Analysis (PCA) was used to reduce the dimensionality of the full-band raw data. These principal components are essentially weighted combinations of information from the entire band, reflecting the most significant structural differences in the data, such as the differences in polysaccharide polymerization degrees caused by different extraction processes. PCA was used to analyze the spectral data of the polysaccharide raw materials, obtaining several principal components and extracting the contribution rate of each principal component. Based on the contribution rates, the top m principal components with a cumulative contribution rate greater than 90% were selected to ensure that most structural information was retained; that is, only the top m principal components with a cumulative contribution rate greater than 90% were retained as the principal components used in subsequent analysis steps.

[0038] In any principal component, there are several band intervals. The band intervals corresponding to the principal component are obtained by peak detection. The start and end points of each peak form a band interval on the horizontal axis.

[0039] Step S200: Based on the structural differences, transmittance, and distribution of different band intervals in different principal components, determine the importance score of the band intervals to the polysaccharide structural differences in the entire band; combine the importance score with the contribution rate of the principal components to screen out the main band intervals in the principal components.

[0040] For any band interval in the principal component, the larger the loading coefficient, the more significant the contribution to that principal component. Furthermore, considering the intersection of different band intervals with the characteristic peak intervals of the polysaccharide raw material in prior knowledge, the larger the intersection, the more effective the identification of the currently analyzed band interval. In this embodiment of the invention, the characteristic peak interval of the polysaccharide raw material is 3200-3600 cm⁻¹. -1 1000-1200 cm -1 It should be noted that obtaining the loading coefficients of the band intervals in the principal components is a well-known technique among those skilled in the art, and will not be elaborated upon here.

[0041] Therefore, based on the loading coefficient and effective length of different band intervals, the significant structural differences of polysaccharide raw materials can be determined.

[0042] Use any principal component as the target principal component and any band interval as the target band interval;

[0043] The length of the intersection between the target band range of the target principal component and the characteristic peak range of the polysaccharide raw material is obtained as the effective range;

[0044] Calculate the ratio of the length of the effective interval to the length of the target band interval of the target principal component, and use this as the effective length percentage.

[0045] The product of the loading factor and the effective length ratio of the target band interval is calculated as the significant structural difference of the polysaccharide raw material.

[0046] In some embodiments, the a-th principal component is taken as the target principal component, and the b-th band interval is taken as the target band interval. Then, the significant structural differences of the polysaccharide raw material within the b-th band interval of the a-th principal component are... The calculation formula is: ;in, , where is the loading coefficient for the b-th band interval of the a-th principal component; Let be the length of the b-th band interval of the a-th principal component; It is the length of the effective interval corresponding to the b-th band interval of the a-th principal component.

[0047] Furthermore, different principal components contain several band intervals, and a certain band interval may exist in multiple principal components. When a certain band interval appears more frequently, the information of it in the whole band interval of cassia seed polysaccharide is richer. At the same time, when the average transmittance of the band interval in the spectral data is greater, that is, the peak value of the characteristic peak is higher, it is directly related to the structure of cassia seed polysaccharide.

[0048] A higher average transmittance results in a higher importance score for the band interval, and a greater band significance in the corresponding principal component further enhances its importance score. The importance score indicates the direct correlation between the functional group or chemical bond represented by the band interval and the polysaccharide structure.

[0049] Therefore, further, based on the frequency of occurrence of band intervals in different principal components, significant structural differences, and transmittance of band intervals, the importance score of band intervals to polysaccharide structural differences in the entire band is determined.

[0050] By combining the significant structural differences of the target band interval in the target principal component and the average pass rate of the spectral data in the target band interval, the single representation ability of the target band interval in the target principal component is determined. Both significant structural differences and average pass rate are positively correlated with the single representation ability. More specifically, the product of the significant structural differences of the target band interval in the target principal component and the average pass rate of the spectral data in the target band interval is used as the single representation ability of the target band interval in the target principal component.

[0051] The comprehensive characterization capability is obtained by combining the individual characterization capabilities of the target band interval among all principal components. More specifically, the mean of the individual characterization capabilities of the target band interval among all principal components is calculated as the comprehensive characterization capability.

[0052] By combining the frequency of occurrence of the target band interval in different principal components and its comprehensive characterization ability, the importance score of the target band interval for polysaccharide structural differences across the entire band is determined. The frequency of occurrence of the target band interval in different principal components and its comprehensive characterization ability are both positively correlated with the importance score. More specifically, the product of the frequency of occurrence of the target band interval in different principal components and its comprehensive characterization ability is calculated as the importance score of the target band interval for polysaccharide structural differences across the entire band.

[0053] In some embodiments, the b-th band interval is used as the target band interval, and the importance score of the target band interval to polysaccharide structural differences in the entire band is determined. The calculation formula is: ;in, The number of times the target band interval appears in different principal components; The significant structural differences of the target band interval in the target principal component; The average pass rate of spectral data within the target band interval; The single representation capability of the target band interval in the target principal component; This is the comprehensive characterization capability of the target band interval among all principal components.

[0054] Score the importance of each band interval in each principal component. The contribution rate of its corresponding principal component Multiplying them yields the structural benefit in different band intervals. The larger the value, the more directly the functional group or chemical bond represented by the corresponding wavelength range is related to the polysaccharide structure.

[0055] Based on the structural benefit of all different band intervals, all structural benefits are sorted from smallest to largest. The benefit difference between two adjacent band intervals is calculated, and the two band intervals corresponding to the largest benefit difference are selected as candidate boundary band intervals. The two candidate boundary band intervals are selected as the actual decomposition band intervals. The actual decomposition band intervals and the band intervals with benefit differences greater than those of the actual decomposition band intervals are selected as the principal band intervals. In other words, the maximum difference between two adjacent band intervals is selected as the boundary, and the band intervals corresponding to the larger value are recorded as the principal bands.

[0056] Through the above logical process and formula calculation, the final selected band combination covers the core characteristic peaks of cassia seed polysaccharide, such as hydroxyl groups, glycosidic bonds, and sugar ring configurations, thereby completing the screening of the main band ranges.

[0057] Step S300: Based on the positional separation features of adjacent characteristic peaks in each major band interval and the disordered distribution of spectral data in the band interval, determine the structural influence index of the major band interval.

[0058] In the spectral analysis of cassia seed polysaccharides, the overlap of characteristic peaks may occur. This is essentially the coupling of vibrational signals from different functional groups at similar wavenumbers, reflecting the spatial complexity of the polysaccharide structure. This overlap of characteristic peaks affects spectral separation, leading to an unclear understanding of the polysaccharide structure during the construction of polysaccharide fingerprints.

[0059] Therefore, based on the positional characteristics of adjacent characteristic peaks in each major band interval, the characteristic peak separation degree of the major band interval is determined, specifically:

[0060] Using any major band interval as the target major band interval, calculate the distance between the peak position of the characteristic peak corresponding to the target major band interval and the peak position of the next adjacent characteristic peak, and use it as the peak spacing;

[0061] The average peak width is calculated as the average base width of the characteristic peak corresponding to the main band interval of the target and the next adjacent characteristic peak. The average peak width is calculated to avoid the actual separation effect being obscured by the excessive width of a single peak. The average peak width represents the average baseline width of the two peaks and reflects the "diffusion degree" of the peak. The wider the average peak width, the greater the corresponding peak spacing.

[0062] The ratio of peak spacing to average peak width is used as the characteristic peak separation degree of the main target band interval. When the peak spacing is much greater than the average peak width, the separation degree is high and the peaks are completely separated; when the peak spacing is close to or less than the sum of the peak widths, the separation degree is low and peak overlap may exist.

[0063] In some embodiments, characteristic peak separation The calculation formula is: ;in, The coordinates of the peak position of the next adjacent characteristic peak corresponding to the main band interval of the target; The coordinates of the peak position of the characteristic peak corresponding to the main band interval of the target; The distance between the peak coordinates of the characteristic peak corresponding to the main band interval of the target and the next adjacent characteristic peak. The width of the base of the next adjacent characteristic peak corresponding to the characteristic peak of the target main band interval; The width of the base of the characteristic peak corresponding to the main band interval of the target; The average peak width corresponding to the main target band interval.

[0064] When characteristic peaks overlap, any characteristic peak of cassia seed polysaccharide, that is, the principal component in which it is located, has a greater contribution rate, and thus it is the core characteristic peak interval. The smaller the separation within the band interval, the more significant the characteristic peak overlap phenomenon in the current band interval. Furthermore, the greater the structural benefit of the characteristic band interval in the entire polysaccharide band, the greater its influence on the polysaccharide structure. When characteristic peaks overlap, the curve in the spectral data shows that there are more extreme points of its second derivative.

[0065] Therefore, by combining the structural benefit, characteristic peak separation, and the disordered distribution of spectral data in the band intervals, the structural influence index of the main band intervals is determined.

[0066] The initial influence index is determined based on structural benefit and characteristic peak separation. Structural benefit and the initial influence index are positively correlated, while characteristic peak separation and the initial influence index are negatively correlated. More specifically, the ratio of structural benefit to characteristic peak separation is calculated as the initial influence index.

[0067] The initial influence index reflects the abnormal influence of the main band x on the structure of cassia polysaccharide in the entire spectral data. The greater the structural benefit, the greater the importance of its band interval in the entire band. The smaller the separation of characteristic peaks, the more likely there is overlap of characteristic peaks in the current band interval. Furthermore, the more extreme points of its second derivative, the more chaotic the distribution of characteristic peaks, and the greater the influence of functional groups and other structures corresponding to the current band interval on the polysaccharide structure.

[0068] The structural influence index of the main band interval is determined by combining the initial influence index and the number of extreme points of the second derivative of the spectral data in the main band interval; both the initial influence index and the number of extreme points are positively correlated with the structural influence index. More specifically: the structural influence index of the main band interval is calculated as the product of the initial influence index and the number of extreme points of the second derivative of the spectral data in the main band interval.

[0069] Step S400: Based on the structure influence index, correct the matching degree between the measured and theoretical CCS values ​​of the polysaccharide raw material to obtain the verification accuracy of the main band intervals; based on the verification accuracy, obtain the main band intervals affected by matrix interference; for the main band intervals affected by matrix interference, use deconvolution splitting technology to remove matrix signals, extract the true characteristic peaks of the polysaccharide raw material, and construct a fingerprint spectrum.

[0070] The overlapping of characteristic peaks is difficult to distinguish, thus affecting the accurate construction of fingerprint spectra. When identifying the overlap of characteristic peaks based on their spectral performance, interference from organic matter impurities left over during the experiment may occur. In this case, the CCS value of the corresponding characteristic band may not match the abnormal performance of the characteristic band data. Spectral features only reflect chemical functional groups, while CCS values ​​reveal the molecular spatial conformation. Therefore, for each band interval in the spectral data, combining its structure influence index with the ion mobility mass spectrometry CCS value, a higher structure influence index indicates a stronger ability to characterize the differences in polysaccharide structure in that band, and then performing characteristic peak overlap matching verification.

[0071] The CCS value is the collision cross section data of the fused ion mobility mass spectrometry. Ion mobility mass spectrometry experiments were performed on the current Cassia seed polysaccharide sample, including sample pretreatment, ionization and mobility separation. Then, based on the drift time of ions in the mobility cell, combined with instrument parameters, the CCS value of each ion peak in the ion mobility mass spectrometry was obtained based on existing technology.

[0072] In the infrared spectrum of cassia seed polysaccharides, based on the characteristic wavelength ranges corresponding to the target compound types, the compound category to which overlapping peaks belong can be determined first. For example, if an overlapping peak is at 1600 cm⁻¹... -1 It exhibits strong absorption, and based on the database, it can be preliminarily determined to contain conjugated double bonds;

[0073] Then, the target ion peak is located in the ion mobility mass spectrometry, and the CCS data of the corresponding ion peak is obtained.

[0074] Based on the structure influence index, the matching degree between the measured and theoretical CCS values ​​of the polysaccharide raw material is corrected to obtain the verification accuracy of the main band interval: the measured and theoretical CCS values ​​of the polysaccharide raw material are obtained; the difference between the measured and theoretical CCS values ​​of the polysaccharide raw material is negatively correlated to obtain the matching degree between the measured and theoretical CCS values ​​of the polysaccharide raw material; the matching degree is weighted using the structure influence index to obtain the verification accuracy of the main band interval.

[0075] In some embodiments, the verification accuracy is the product of the structural influence index and the degree of matching.

[0076] Based on theoretical data of different ion peaks in the database, the CCS value matching degree is obtained by combining it with the CCS data of actual ion peaks. The normalized value CM is calculated by subtracting the relative deviation between the measured value and the theoretical value from 1. The closer CM is to 1, the higher the matching degree. When the phenomenon of co-retaining matrix impurities occurs during the experiment, the change of CCS value in the corresponding characteristic band does not match the structure influence index of the characteristic band in the spectral data. It may show that a certain band range has a large structure influence index, but its CSS matching degree is relatively small. This indicates the presence of co-retaining matrix impurities.

[0077] After obtaining the verification accuracy for the main band intervals, compare the verification accuracy with the preset verification threshold. For the main band intervals where the verification accuracy is less than the preset verification threshold, impurity localization is performed using the following steps:

[0078] In this embodiment of the invention, the preset verification threshold is 0.3. In other embodiments, the implementer may adjust this value according to the actual situation.

[0079] By comparing the spectrum of the blank sample (detected separately by the matrix), if the blank sample shows absorption in this wavelength range, it is determined that the main wavelength range is affected by matrix interference and is classified as "significantly inconsistent," indicating the presence of co-eluting matrix impurities. For example, flavonoid impurities in cassia seed extract are located in the 3200-3600 cm⁻¹ range. -1 The absorption peak of the (hydroxyl region) overlaps with that of polysaccharides. At this time, the structure influence index of this band is relatively high. However, due to the significant difference between the CCS value of flavonoids (180 Ų) and the theoretical value of polysaccharides (220 Ų), the CSS matching degree is relatively small, clearly indicating the presence of interference.

[0080] For the main band ranges affected by matrix interference, it is convenient to purify the subsequent spectral data, ultimately improving the reliability of the fingerprint spectrum. It solves the problem of overlapping band ranges corresponding to characteristic peaks caused by matrix interference, and only retains the bands of the actual reaction polysaccharide structure for fingerprint spectrum construction, ensuring that each characteristic peak corresponds to the real structure of the target cassia polysaccharide.

[0081] The main band intervals affected by matrix interference are also the band intervals where the "significantly inconsistent" characteristic peaks overlap due to matrix interference. For the main band intervals affected by matrix interference, the matrix signal needs to be removed by deconvolution splitting technology to extract the true characteristic peaks of polysaccharides. Finally, the effective data is integrated to complete the fingerprint spectrum construction. The method is as follows:

[0082] For the main band intervals identified as being affected by matrix interference, a deconvolution model is first used to optimize parameters such as half-peak width and number of iterations, splitting the overlapping peaks into multiple sub-peaks. The reliability of the splitting is ensured by controlling the fitting residual (<0.02).

[0083] After splitting, based on the known characteristic peak library of cassia seed polysaccharides, sub-peaks that conform to the vibrational characteristics of polysaccharide functional groups (wavenumber deviation <2cm) were selected. -1 (Peak shape symmetrical), remove sub-peaks that match the characteristics of matrix impurities to obtain the true characteristic peaks (e.g., deviation from the standard wavenumber > 5 cm⁻¹). -1 (broadened peaks). In this embodiment of the invention, the deconvolution model can be a Gaussian-Lorentzian hybrid model; the known characteristic peak library of cassia seed polysaccharides includes, for example, the standard wavenumber ranges of hydroxyl groups and glycosidic bonds.

[0084] The selected true characteristic peaks were quantified, and the structural influence index of the corresponding band intervals was recalculated. True characteristic peaks with a normalized structural image index greater than 0.6 were selected as qualified true characteristic peaks to ensure the sub-peaks' ability to characterize polysaccharide structural differences. Finally, all qualified true characteristic peaks were integrated to construct a fingerprint spectrum containing "wavenumber-peak area ratio-structural annotation," and the functional group information (such as hydroxyl groups and glycosidic bonds) corresponding to each characteristic peak was labeled to complete the characteristic characterization of Cassia tora polysaccharides.

[0085] Please see Figure 3 , Figure 3 This invention provides a system block diagram of a polysaccharide fingerprinting system for polysaccharide raw materials. The system includes:

[0086] The data preprocessing module is used to acquire the spectral data of the polysaccharide raw material; decompose the spectral data to obtain multiple principal components and the band range of each principal component;

[0087] The band interval screening module is used to determine the importance score of the band interval to the polysaccharide structural differences in the whole band based on the structural differences, transmittance, and distribution of the band interval in different principal components. Combining the importance score and the contribution rate of the principal components, the main band intervals in the principal components are screened out.

[0088] The analysis module is used to separate features based on the positions of adjacent characteristic peaks in each major band interval and the disordered distribution of spectral data in the band interval, and to determine the structural influence index of the major band interval.

[0089] The fingerprint spectrum construction module is used to correct the matching degree between the measured and theoretical values ​​of the CCS value of the polysaccharide raw material according to the structure influence index, and obtain the verification accuracy of the main band intervals; based on the verification accuracy, the main band intervals affected by matrix interference are obtained; for the main band intervals affected by matrix interference, the matrix signal is stripped off by deconvolution splitting technology, the true characteristic peaks of the polysaccharide raw material are extracted, and the fingerprint spectrum is constructed.

[0090] Alternatively, the transmission medium may be a wired link, such as, but not limited to, coaxial cable, fiber optic cable and digital subscriber line, or a wireless link, such as, but not limited to, wireless Fidelity (WIFI), Bluetooth and mobile device networks.

[0091] It should be noted that the device provided in the above embodiments is only an example of the division of the above functional modules. In actual applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the computer device can be divided into different functional modules to complete all or part of the functions described above.

[0092] This invention provides a computer device. Exemplarily, the computer device includes: a memory, a processor, and a computer program stored in the memory and running on the processor, wherein when the processor executes the computer program, the computer device can perform the aforementioned method for constructing polysaccharide fingerprint profiles from any polysaccharide raw material.

[0093] Furthermore, embodiments of the present invention also protect an apparatus that may include a memory and a processor, wherein the memory stores executable program code, and the processor is used to call and execute the executable program code to execute the method for constructing polysaccharide fingerprint profiles in polysaccharide raw materials provided in embodiments of the present invention.

[0094] In this embodiment of the invention, the device can be divided into functional modules according to the above method example. For example, each module can correspond to a separate function, or two or more functions can be integrated into one processing module. The integrated module can be implemented in hardware. It should be noted that the module division in this embodiment is illustrative and is only a logical functional division. In actual implementation, there may be other division methods.

[0095] When each module is divided according to its function, the device may also include a signal uploading module, a determination module, and an adjustment module. It should be noted that all relevant content of each step involved in the above method embodiments can be referenced from the functional descriptions of the corresponding functional modules, and will not be repeated here.

[0096] It should be understood that the apparatus provided in this embodiment of the invention is used to perform the above-described method for constructing polysaccharide fingerprints in polysaccharide raw materials, and thus can achieve the same effect as the above-described implementation method.

[0097] When using integrated units, the device may include a processing module and a storage module. When applied to a device, the processing module can be used to control and manage the device's operations. The storage module can be used to support the device in executing program code, etc. The processing module may be a processor or a controller, which can implement or execute various exemplary logic blocks, modules, and circuits as described in this disclosure. The processor may also be a combination that implements computing functions, such as a combination of one or more microprocessors, a combination of Digital Signal Processing (DSP) and a microprocessor, etc., and the storage module may be a memory.

[0098] In addition, the device provided in the embodiments of the present invention may specifically be a chip, component or module. The chip may include a connected processor and a memory. The memory is used to store instructions. When the processor calls and executes the instructions, the chip can execute the polysaccharide fingerprint spectrum construction method in polysaccharide raw materials provided in the above embodiments.

[0099] This invention also provides a computer-readable storage medium storing computer program code. When the computer program code is run on a computer, the computer executes the above-described method steps to implement the method for constructing polysaccharide fingerprint profiles in polysaccharide raw materials provided in the above embodiments.

[0100] This invention also provides a computer program product that, when run on a computer, causes the computer to perform the aforementioned steps to implement the method for constructing polysaccharide fingerprint profiles in polysaccharide raw materials provided in the above embodiments.

[0101] In this invention, the apparatus, computer-readable storage medium, computer program product, or chip provided in the embodiments are all used to execute the corresponding methods described above. Therefore, the beneficial effects they achieve can be referred to the beneficial effects in the corresponding methods described above, and will not be repeated here. Through the above description of the embodiments, those skilled in the art will understand that, for the sake of convenience and brevity, only the division of the above functional modules is used as an example. In practical applications, the above functions can be assigned to different functional modules as needed, that is, the internal structure of the device can be divided into different functional modules to complete all or part of the functions described above. In the embodiments provided by this invention, it should be understood that the disclosed apparatus and method can be implemented in other ways.

[0102] The device embodiments described above are merely illustrative. For example, the division of modules or units is only a logical functional division. In actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0103] It should also be noted that, 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 terminal device 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 terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0104] It should be noted that the order of the above embodiments of the present invention is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. The processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0105] The various embodiments in this specification are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0106] The above content is only a specific embodiment of the present invention, but the protection scope of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the protection scope of the present invention.

Claims

1. A method for constructing a polysaccharide fingerprint of a polysaccharide raw material, characterized in that, The method comprises the following steps: Obtaining spectral data of the polysaccharide raw material; decomposing the spectral data to obtain a plurality of principal components and a wave band interval of each principal component; According to the structural differences, transmittance of different wave band intervals and the distribution of the wave band intervals in different principal components, the importance score of the wave band interval in the full wave band to the structural differences of the polysaccharide is determined; the main wave band interval in the principal component is screened out by combining the importance score and the contribution rate of the principal component; According to the position separation characteristics of adjacent characteristic peaks of each main wave band interval and the distribution confusion of spectral data in the wave band interval, the structural influence index of the main wave band interval is determined; According to the structural influence index, the matching degree of the measured value and the theoretical value of the CCS value of the polysaccharide raw material is corrected to obtain the verification accuracy of the main wave band interval; based on the verification accuracy, the main wave band interval interfered by the matrix is obtained, the matrix signal is stripped by the deconvolution splitting technology, the real characteristic peak of the polysaccharide raw material is extracted, and the fingerprint spectrum is constructed.

2. The method according to claim 1, wherein the method is characterized in that, The importance score of the wave band interval in the full wave band to the structural differences of the polysaccharide is determined according to the structural differences, transmittance of different wave band intervals and the distribution of the wave band intervals in different principal components, comprising: According to the load coefficient of different wave band intervals and the effective length of the wave band interval, the significant structural differences of the polysaccharide raw material are determined; according to the number of times of the wave band interval appearing in different principal components, the significant structural differences and the transmittance of the wave band interval, the importance score of the wave band interval in the full wave band to the structural differences of the polysaccharide is determined.

3. The method according to claim 2, wherein the method is characterized in that, The importance score of the wave band interval in the full wave band to the structural differences of the polysaccharide is determined according to the number of times of the wave band interval appearing in different principal components, the significant structural differences and the transmittance of the wave band interval, comprising: Taking any principal component as a target principal component and any wave band interval as a target wave band interval; Combining the significant structural differences of the target wave band interval in the target principal component and the average transmittance of the spectral data in the target wave band interval, the single representation ability of the target wave band interval in the target principal component is determined; The comprehensive representation ability is obtained by comprehensively considering the single representation ability of the target wave band interval in all principal components; The importance score of the target wave band interval in the full wave band to the structural differences of the polysaccharide is determined by combining the number of times of the target wave band interval appearing in different principal components and the comprehensive representation ability.

4. The method according to claim 1, wherein the method is characterized by, The main wave band interval in the principal component is screened out by combining the importance score and the contribution rate of the principal component, comprising: For any principal component, the product of the importance score of the wave band interval and the contribution rate of each principal component is calculated to obtain the structural benefit of the wave band interval in each principal component; according to the structural benefit, the main wave band interval in each principal component is screened out.

5. The method according to claim 4, wherein the method is characterized in that, The main wave band interval in each principal component is screened out according to the structural benefit, comprising: For any principal component, the structure benefit degree of the wave band interval corresponding to the principal component is sorted from small to large, the benefit difference value of the structure benefit degrees of two adjacent wave band intervals is calculated, and the two wave band intervals corresponding to the maximum benefit difference value are taken as the candidate dividing wave band intervals; the two candidate dividing wave band intervals are taken as the actual dividing wave band intervals; and the actual dividing wave band interval and the wave band interval with a structure benefit degree greater than that of the actual dividing wave band interval are taken as the main wave band interval.

6. The method according to claim 4, wherein the method is characterized by, The structure influence index of the main wave band interval is determined according to the position separation of the adjacent characteristic peaks and the distribution confusion of the spectral data in the main wave band interval, and the structure influence index of the main wave band interval comprises: The distance between the peak top position of the characteristic peak corresponding to the target main wave band interval and the next adjacent characteristic peak is taken as the peak distance; the average peak width of the characteristic peak corresponding to the target main wave band interval and the next adjacent characteristic peak is calculated; and the ratio of the peak distance and the average peak width is taken as the characteristic peak separation degree of the target main wave band interval. The structure influence index of the main wave band interval is determined in combination with the structure benefit degree, the characteristic peak separation degree and the distribution confusion of the spectral data in the wave band interval.

7. The method according to claim 6, wherein the method is characterized by, The structure influence index of the main wave band interval is determined in combination with the structure benefit degree, the characteristic peak separation degree and the distribution confusion of the spectral data in the wave band interval. The initial influence index is determined according to the structure benefit degree and the characteristic peak separation degree; the structure benefit degree and the initial influence index are positively correlated; and the characteristic peak separation degree and the initial influence index are negatively correlated. The structure influence index of the main wave band interval is determined in combination with the initial influence index and the number of extreme points of the second derivative of the spectral data in the main wave band interval; the initial influence index and the number of extreme points are positively correlated with the structure influence index.

8. The method according to claim 1, wherein the method is characterized by, The matching degree of the measured value and the theoretical value of the CCS value of the polysaccharide raw material is corrected according to the structure influence index, and the verification accuracy of the main wave band interval is obtained, and the verification accuracy of the main wave band interval comprises: The measured value and the theoretical value of the CCS value of the polysaccharide raw material are obtained; The difference between the measured value and the theoretical value of the CCS value of the polysaccharide raw material is negatively correlated, and the matching degree of the measured value and the theoretical value of the CCS value of the polysaccharide raw material is obtained; The matching degree is weighted by taking the structure influence index as the weight, and the verification accuracy of the main wave band interval is obtained.

9. The method according to claim 1, wherein the method is characterized by, The main wave band interval interfered by the matrix is obtained based on the verification accuracy, and the main wave band interval interfered by the matrix comprises: The spectrum of the blank sample is compared, and if the blank sample has absorption in the main wave band interval with a verification accuracy less than a preset verification threshold, it is judged that the main wave band interval is interfered by the matrix.

10. A system for constructing polysaccharide fingerprint profiles in polysaccharide raw materials, characterized in that, The system comprises the following modules: A data preprocessing module is configured to obtain spectral data of a polysaccharide raw material, and decompose the spectral data to obtain a plurality of principal components and a wave band interval of each principal component. The wave band interval screening module is configured to determine the importance score of the wave band interval in the full wave band for the structural difference of the polysaccharide according to the structural difference, the transmittance and the distribution of the wave band interval in different principal components; and to screen the main wave band interval in the principal components in combination with the importance score and the contribution rate of the principal component; The analysis module is configured to determine the structural influence index of the main wave band interval according to the position separation feature of the adjacent characteristic peaks of each main wave band interval and the distribution confusion of the spectral data in the wave band interval; The fingerprint spectrum construction module is configured to correct the matching degree between the measured value and the theoretical value of the CCS value of the polysaccharide raw material according to the structural influence index, to obtain the verification accuracy of the main wave band interval; and to obtain the main wave band interval interfered by the matrix based on the verification accuracy, to strip the matrix signal through the deconvolution splitting technology, to extract the real characteristic peaks of the polysaccharide raw material, and to construct the fingerprint spectrum.

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