Method for detecting total flavonoids content of traditional chinese medicine decoction pieces based on raman spectrum

Abstract

The application belongs to the technical field of medicine detection, and discloses a traditional Chinese medicine decoction piece total flavone content detection method based on Raman spectrum, which comprises the following steps: obtaining standard spectrum data of standard flavone compounds and characteristic spectrum data of traditional Chinese medicine decoction piece samples; performing correlation degree evaluation according to the characteristic spectrum data and the standard spectrum data to obtain a flavone characteristic peak identification matrix of each traditional Chinese medicine decoction piece sample; screening effective characteristic wave bands based on the flavone characteristic peak identification matrix; compiling a flavone quantitative characterization vector of the sample according to the spectrum absorption intensity and structural information of the effective characteristic wave bands of each traditional Chinese medicine decoction piece sample; and detecting the total flavone content in the traditional Chinese medicine decoction piece based on the flavone quantitative characterization vector, so that the accuracy of traditional Chinese medicine decoction piece total flavone content detection is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of drug detection, and more particularly to a method for detecting the total flavonoid content of Chinese herbal medicine slices based on Raman spectroscopy. Background Art

[0002] The total flavonoid content in traditional Chinese medicine slices is an important indicator for evaluating their efficacy and quality. However, the current testing process is plagued by numerous issues, including complex sample pretreatment, long testing times, cumbersome procedures, and insufficient accuracy. Complex sample pretreatment increases labor costs and testing errors; long testing times lead to inefficient drug quality control, failing to meet the rapid testing needs of modern Chinese medicine production; cumbersome procedures require highly skilled operators, increasing the risk of human error; and insufficient accuracy can lead to biased quality evaluations, impacting the clinical safety and efficacy of traditional Chinese medicine slices. Therefore, a rapid, accurate, and simple method for determining the total flavonoid content in traditional Chinese medicine slices is needed to ensure quality control.

[0003] Existing detection methods mainly use ultraviolet spectrophotometry and high-performance liquid chromatography (HPLC); ultraviolet spectrophotometry requires complex sample extraction, dilution and color development reaction, and there are differences in the sensitivity of the color development reagent between different batches; although the HPLC method has high sensitivity, it requires the sample to undergo tedious pre-treatment steps, including extraction, filtration, concentration, etc., and the detection cycle is long and the solvent consumption is high; due to differences in the source of raw materials, processing technology and storage conditions of Chinese herbal medicines, and the complexity and diversity of Chinese herbal medicine components, traditional detection methods are difficult to eliminate the interference of these factors on the test results, resulting in large errors in the determination results of total flavonoids content, reducing the accuracy and reliability of the quality evaluation of Chinese herbal medicines.

[0004] In view of this, the present invention proposes a method for detecting the total flavonoids content in Chinese herbal medicine slices based on Raman spectroscopy to solve the above problems. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art and to achieve the above-mentioned purpose, the present invention provides the following technical solution: a method for detecting the total flavonoid content of Chinese herbal medicine slices based on Raman spectroscopy, comprising:

[0006] Step S1: acquiring standard spectrum data of standard flavonoid compounds and characteristic spectrum data of Chinese herbal medicine slice samples;

[0007] Step S2: performing correlation evaluation between the characteristic spectrum data and the standard spectrum data to obtain a flavonoid characteristic peak identification matrix for each Chinese herbal medicine sample; and screening effective characteristic bands based on the flavonoid characteristic peak identification matrix;

[0008] Step S3: compiling a flavonoid quantitative characterization vector of each Chinese herbal medicine sample according to the spectral absorption intensity and structural information of the effective characteristic band of each Chinese herbal medicine sample; and detecting the content of total flavonoids in the Chinese herbal medicine sample based on the flavonoid quantitative characterization vector.

[0009] Furthermore, the step of obtaining the flavonoid characteristic peak identification matrix of each Chinese herbal medicine piece sample includes:

[0010] Perform peak analysis on the wavelength points in the characteristic spectrum data of all samples to obtain the absorption intensity of each wavelength point in the characteristic spectrum data of each sample;

[0011] The wavelength points where the difference in absorption intensity of adjacent wavelength points within the analysis band of each sample exceeds the preset intensity threshold are counted and recorded as the flavonoid characteristic peak of each sample;

[0012] The correlation coefficient between the absorption intensity of all wavelength points in the analysis band of each sample and the standard spectrum data was calculated as the flavonoid spectrum similarity of each sample;

[0013] The number of flavonoid characteristic peaks of each sample and the flavonoid spectrum similarity are combined to form a flavonoid characteristic peak recognition matrix.

[0014] Furthermore, the screening of effective characteristic bands based on the flavonoid characteristic peak recognition matrix includes:

[0015] Perform difference analysis on the characteristic spectral data of all samples to obtain the distinguishing contribution of each band in the characteristic spectral data;

[0016] Perform a modulo operation on the flavonoid characteristic peak recognition matrix to obtain the sample eigenvalues. Perform redundancy analysis based on the sample eigenvalues ​​and the discrimination contribution of any band in the characteristic spectral data to obtain the information redundancy of the corresponding two bands.

[0017] The continuous intervals formed by bands in the characteristic spectral data whose information redundancy is less than a preset redundancy threshold are recorded as candidate bands; the information contribution of the candidate bands in the characteristic spectral data is evaluated to obtain effective characteristic bands.

[0018] Furthermore, the information redundancy is the ratio of the discrimination contribution to the sample characteristic value.

[0019] Furthermore, the information contribution evaluation of the candidate bands in the characteristic spectrum data to obtain the effective characteristic bands includes:

[0020] Traverse the candidate bands in the characteristic spectrum data, use the result of each traversal as the reference band, use the reference band as the evaluation point for band screening, record the other candidate bands that meet the preset contribution conditions except the evaluation point as the candidate bands, and all the candidate bands and the evaluation point constitute the candidate set; record the candidate set after each traversal; after the traversal is completed, take the intersection of all recorded candidate sets to obtain the corresponding valid characteristic band;

[0021] The difference between the maximum and minimum values ​​of the absorption intensity ratio of any two candidate bands is recorded as the absorption intensity difference;

[0022] The preset contribution conditions include: the wavelength interval between the evaluation point and the evaluation point is less than a preset interval threshold and the absorption intensity difference between the evaluation point and the evaluation point is greater than a preset intensity difference threshold.

[0023] Furthermore, the method of compiling the flavonoid quantitative characterization vector of the sample includes:

[0024] Determine the absorption peak area of ​​the effective characteristic band of each Chinese herbal medicine sample, normalize and vectorize the absorption peak area to obtain a standardized spectral intensity matrix; obtain characteristic indicators of the standardized spectral intensity matrix, wherein the characteristic indicators include peak intensity ratio;

[0025] According to the spectral shape of the effective characteristic band of each Chinese herbal medicine sample and the peak intensity ratio, the flavonoid content parameter of the corresponding sample is obtained;

[0026] The flavonoid content parameter of each Chinese herbal medicine slice sample and the remaining characteristic indicators except the peak intensity ratio constitute a flavonoid quantitative characterization vector of the corresponding sample.

[0027] Furthermore, the detection of the total flavonoid content in the Chinese herbal medicine slices based on the flavonoid quantitative characterization vector includes:

[0028] Obtain the standard quantitative vector of the standard Chinese herbal medicine samples with known total flavonoid content;

[0029] establishing a content mapping model between the standard quantitative vector and the known total flavonoid content;

[0030] The flavonoid quantitative characterization vector of the Chinese herbal medicine sample to be tested is substituted into the content mapping model to calculate the total flavonoid content in the Chinese herbal medicine sample.

[0031] Furthermore, the method for performing difference analysis on the characteristic spectrum data of all samples is a characteristic wavelength screening algorithm.

[0032] Furthermore, the method for acquiring the characteristic spectrum data and the standard spectrum data is Raman spectrum acquisition technology.

[0033] Furthermore, the content mapping model adopts a nonlinear spectral inversion model and is established through the correspondence between the characteristic absorption peak area of ​​the standard flavonoid compound and the actual content. The input of the content mapping model is the flavonoid quantitative characterization vector of the Chinese herbal medicine sample to be tested, and the output is the content of total flavonoids in the Chinese herbal medicine sample.

[0034] The technical effects and advantages of the method for detecting the total flavonoid content in Chinese herbal medicine slices based on Raman spectroscopy of the present invention are as follows:

[0035] The present invention realizes the rapid and accurate detection of the total flavonoid content in Chinese herbal medicine slices by analyzing the spectral data of standard flavonoid compounds and the characteristic spectral data of Chinese herbal medicine slice samples, and can transform the traditional chemical analysis method into a non-destructive and efficient spectral analysis method, thereby enhancing the convenience and practicality of the detection. By constructing a flavonoid characteristic peak recognition matrix and screening effective characteristic bands, the flavonoid characteristic information in complex Chinese herbal medicine matrices can be effectively identified, and the accurate identification of flavonoid components can be achieved. This characteristic recognition strategy can ensure accurate detection under the interference of complex components, greatly improves the accuracy of Chinese herbal medicine slice quality control, and avoids the problems of complex sample pretreatment and long detection time in traditional methods. Through peak analysis and flavonoid spectral similarity calculation, the system can comprehensively identify the flavonoid characteristic peaks in Chinese herbal medicine samples, cope with the complexity and diversity requirements of different Chinese herbal medicine matrices, and screen out redundant bands through information redundancy analysis, thereby improving the specificity and robustness of the method. By introducing an information contribution evaluation mechanism for effective characteristic bands, the correlation and differentiation contribution between bands are analyzed in real time, the optimal characteristic band combination is predicted, and the characteristic band selection strategy can be dynamically optimized through the intersection operation of the candidate set to ensure efficient component identification when the characteristic bands change. The flavonoid quantitative characterization vector compiled based on the spectral absorption intensity and structural information of the effective characteristic bands can automatically generate a quantitative analysis model and perform content detection based on multidimensional characteristic indicators, thereby improving the adaptability to different Chinese medicine samples and significantly improving the detection accuracy. The content mapping relationship based on the nonlinear spectral inversion model can effectively address the limitations of traditional linear models in complex sample analysis and achieve more accurate content quantification. In this way, automated content detection is performed for the complex matrix of Chinese herbal medicine samples and the diversity of flavonoid components, reducing human operation errors and achieving more scientific and reliable quality evaluation of Chinese herbal medicine pieces. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 Schematic diagram of the method for detecting the total flavonoid content of Chinese herbal medicine slices based on Raman spectroscopy of the present invention;

[0037] Figure 2 Schematic diagram of the total flavonoids content detection system of Chinese herbal medicine slices based on Raman spectroscopy of the present invention. DETAILED DESCRIPTION

[0038] The technical solutions in the embodiments of the present application will be clearly and completely described with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative effort belong to the scope of protection of the present application.

[0039] Embodiment 1

[0040] Please refer to Figure 1 The total flavonoids content detection method of traditional Chinese medicine decoction pieces based on Raman spectrum in the embodiment includes:

[0041] Step S1: obtaining standard spectrum data of standard flavonoid compounds and characteristic spectrum data of traditional Chinese medicine decoction piece samples.

[0042] In the embodiments of the present application, a Raman spectrometer is used to scan standard flavonoid compounds and traditional Chinese medicine decoction piece samples to obtain spectrum data. The standard flavonoid compounds include, but are not limited to, rutin, quercetin, kaempferol and other known content flavonoid compound standards. The traditional Chinese medicine decoction piece samples include, but are not limited to, radix puerariae, astragalus root, ginkgo leaf and other traditional Chinese medicine decoction pieces containing flavonoids.

[0043] For standard flavonoid compounds, a Raman spectrometer is used to collect spectrum data in the range of 400 to 3200 cm-1, to obtain the standard spectrum data of each standard flavonoid compound. For traditional Chinese medicine decoction piece samples, pretreatment is first performed, including crushing, sieving and other steps, to ensure uniform sample particle size and improve the accuracy and repeatability of spectrum collection. Then, the same Raman spectrometer and parameter settings as the standard are used to collect spectrum data of the traditional Chinese medicine decoction piece samples in the same wave number range, to obtain the characteristic spectrum data of each traditional Chinese medicine decoction piece sample.

[0044] It should be noted that the laser wavelength of the Raman spectrometer is set to 785 , the power is set to 200 , the integration time is 10 seconds, and each sample is repeated 3 times to take the average value to improve the reliability of the data.

[0045] The obtained spectrum data is preprocessed for denoising, baseline correction and normalization to eliminate the influence of background interference and instrument drift and improve the accuracy of subsequent analysis. In the embodiments of the present application, the Savitzky-Golay smoothing algorithm is used for denoising, the polynomial fitting method is used for baseline correction, and the vector normalization method is used for normalization.

[0046] Step S2: performing correlation evaluation between the characteristic spectrum data and the standard spectrum data to obtain a flavonoid characteristic peak identification matrix for each Chinese herbal medicine sample; and screening effective characteristic bands based on the flavonoid characteristic peak identification matrix.

[0047] Flavonoids have specific characteristic peaks in Raman spectroscopy. By comparing these peaks with the spectral data of standard flavonoid compounds, the characteristic peaks of flavonoids in traditional Chinese medicine slices can be identified. Due to the complex and variable matrix composition of different traditional Chinese medicine slices, characteristic peaks may shift or be interfered with by characteristic peaks of other components. Therefore, it is necessary to conduct a correlation assessment, construct a flavonoid characteristic peak identification matrix, and screen the most representative and effective characteristic bands to improve the accuracy of flavonoid content detection.

[0048] Preferably, the method for obtaining the flavonoid characteristic peak identification matrix includes: performing peak analysis on the wavelength points in the characteristic spectrum data of all samples to obtain the absorption intensity of each wavelength point in the characteristic spectrum data of each sample; counting the wavelength points whose absorption intensity differences of adjacent wavelength points in the analysis band of each sample exceed a preset intensity threshold, and recording them as the flavonoid characteristic peaks of each sample; calculating the correlation coefficient between the absorption intensity of all wavelength points in the analysis band of each sample and the standard spectrum data as the flavonoid spectrum similarity of each sample; and combining the number of flavonoid characteristic peaks of each sample with the flavonoid spectrum similarity to form a flavonoid characteristic peak identification matrix.

[0049] Peaks in Raman spectra represent the vibrations of specific chemical bonds or functional groups. Flavonoids have specific chemical structures and exhibit characteristic peaks in Raman spectra. By identifying these characteristic peaks through peak analysis and calculating the correlation with the spectra of standard flavonoid compounds, the presence and relative content of flavonoids in the sample can be determined.

[0050] In the embodiment of the present invention, the preset intensity threshold is set to 10% of the characteristic peak intensity of the standard flavonoid compound to filter out weak peaks that may be caused by noise or non-flavonoid components.

[0051] The Pearson correlation coefficient was used to calculate the similarity between the sample spectrum and the standard spectrum in terms of overall pattern. The closer the correlation coefficient is to 1, the more distinct the flavonoid characteristics of the sample are.

[0052] The flavonoid signature peak identification matrix is ​​a two-dimensional matrix. One dimension is the number of flavonoid signature peaks, reflecting the richness of flavonoid species in the sample; the other dimension is the flavonoid spectral similarity, reflecting the overall similarity between the flavonoid components in the sample and the standard flavonoid compounds. The combination of these two dimensions provides more comprehensive flavonoid signature identification information.

[0053] After obtaining the flavonoid characteristic peak identification matrix, it is necessary to further screen effective characteristic bands to improve the accuracy and efficiency of subsequent quantitative analysis. In an embodiment of the present invention, the method for screening effective characteristic bands includes: performing a difference analysis on the characteristic spectrum data of all samples to obtain the discrimination contribution of each band in the characteristic spectrum data; performing a modulo operation on the flavonoid characteristic peak identification matrix to obtain sample eigenvalues, performing a redundancy analysis based on the sample eigenvalues ​​and the discrimination contribution of any band in the characteristic spectrum data to obtain the information redundancy of the corresponding two bands; recording the continuous interval consisting of bands in the characteristic spectrum data whose information redundancy is less than a preset redundancy threshold as candidate bands; and evaluating the information contribution of the candidate bands in the characteristic spectrum data to obtain effective characteristic bands.

[0054] The discrimination contribution reflects the ability of a specific wavelength band to distinguish the flavonoid content of different samples and is calculated using a characteristic wavelength screening algorithm. The sample eigenvalue is a scalar value obtained by applying mathematical operations to the flavonoid characteristic peak identification matrix, reflecting the overall strength of the sample's flavonoid characteristics.

[0055] Information redundancy is used to assess the degree of information duplication between different wavelengths. In this embodiment of the present invention, information redundancy is defined as the ratio of the discrimination contribution to the sample characteristic value. The lower the information redundancy, the more unique the information provided by the wavelength, and the greater its contribution to flavonoid content detection.

[0056] In one implementation of the embodiment of the present invention, the preset redundancy threshold is set to 0.3.

[0057] The method for evaluating the information contribution of candidate bands includes: traversing the candidate bands in the characteristic spectrum data, taking the result of each traversal as the reference band, taking the reference band as the evaluation point for band screening, recording the other candidate bands except the evaluation point that meet the preset contribution conditions as the candidate bands, and all the candidate bands and the evaluation point constitute a candidate set; recording the candidate set after each traversal; after the traversal is completed, taking the intersection of all recorded candidate sets to obtain the corresponding valid characteristic bands; recording the difference between the maximum and minimum values ​​of the absorption intensity ratio of any two candidate bands as the absorption intensity difference; the preset contribution conditions include: the wavelength interval between the evaluation point is less than the preset interval threshold and the absorption intensity difference with the evaluation point is greater than the preset intensity difference threshold.

[0058] The difference in absorption intensity between different bands reflects the response differences of flavonoid components at different wavelengths. Larger absorption intensity differences help distinguish flavonoid components with different contents. Bands with wavelengths that are too far apart may represent different types of chemical bonds or functional groups and should not be used as effective characteristic bands.

[0059] In one implementation of the embodiment of the present invention, the preset interval threshold is set to 200 , the preset intensity difference threshold is set to 0.25.

[0060] Step S3: compiling a flavonoid quantitative characterization vector of each Chinese herbal medicine sample according to the spectral absorption intensity and structural information of the effective characteristic band of each Chinese herbal medicine sample; and detecting the content of total flavonoids in the Chinese herbal medicine sample based on the flavonoid quantitative characterization vector.

[0061] The effective characteristic bands screened out through the above steps can reflect the characteristic information of the flavonoid components in the sample to the greatest extent. Based on the spectral information of these bands, the flavonoid quantitative characterization vector is compiled to accurately detect the total flavonoid content in Chinese herbal medicines.

[0062] Preferably, the method of compiling the flavonoid quantitative characterization vector of the sample includes: determining the absorption peak area of ​​the effective characteristic band of each Chinese herbal medicine sample, normalizing and vectorizing the absorption peak area to obtain a standardized spectral intensity matrix; obtaining characteristic indicators of the standardized spectral intensity matrix, the characteristic indicators including the peak intensity ratio; obtaining the flavonoid content parameters of the corresponding sample based on the spectral shape of the effective characteristic band of each Chinese herbal medicine sample and the peak intensity ratio; and forming the flavonoid quantitative characterization vector of the corresponding sample by the flavonoid content parameters of each Chinese herbal medicine sample and the remaining characteristic indicators except the peak intensity ratio.

[0063] The absorption peak area contains more information than the single-point absorption intensity and can more comprehensively reflect the content of flavonoid components. The peak area of ​​the effective characteristic band is calculated by integration and then normalized to eliminate the influence of factors such as sample size and instrument sensitivity, resulting in a standardized spectral intensity matrix.

[0064] The characteristic indicators of the standardized spectral intensity matrix include parameters such as peak intensity ratio, peak width, and peak position shift. The peak intensity ratio refers to the intensity ratio between different characteristic peaks, reflecting the relative content distribution of different types of flavonoids in the sample; the peak width reflects the purity and uniformity of the flavonoid components in the sample; and the peak position shift reflects the structural differences between the flavonoids in the sample and the standard.

[0065] Spectral shape describes the morphology of a Raman spectral curve, including characteristics such as peak symmetry and sharpness. Different flavonoid compounds exhibit distinct spectral shapes due to differences in their molecular structures. Combining spectral shape with peak intensity ratio analysis can more accurately characterize the structure and content of flavonoids in a sample.

[0066] In an embodiment of the present invention, the flavonoid content parameter calculation method is the weighted sum of the spectral shape factor and the peak intensity ratio, wherein the spectral shape factor is the main component extracted from the spectral shape characteristics by the principal component analysis method, reflecting the main changes in the sample spectral shape.

[0067] The flavonoid quantitative characterization vector is a multidimensional vector that includes multiple characteristic indicators such as flavonoid content parameters, peak width, peak position shift, etc. It comprehensively characterizes the content and structural characteristics of flavonoid components in the sample, providing a reliable data basis for subsequent quantitative analysis.

[0068] The method for detecting the content of total flavonoids in Chinese herbal medicine slices based on flavonoid quantitative characterization vectors includes: obtaining a standard quantitative vector of a standard Chinese herbal medicine slice sample with known total flavonoid content; establishing a content mapping model between the standard quantitative vector and the known total flavonoid content; substituting the flavonoid quantitative characterization vector of the Chinese herbal medicine slice sample to be tested into the content mapping model to calculate the content of total flavonoids in the Chinese herbal medicine slice.

[0069] A standard Chinese herbal medicine sample refers to a Chinese herbal medicine sample whose total flavonoid content has been measured, and its flavonoid quantitative characterization vector is called a standard quantitative vector.

[0070] The content mapping model uses a nonlinear spectral inversion model and is established by the correspondence between the characteristic absorption peak area of ​​the standard flavonoid compound and the actual content. In embodiments of the present invention, the content mapping model is established using methods such as partial least squares regression, support vector regression, or neural networks. These models can handle complex nonlinear relationships between multidimensional variables and are suitable for processing the mapping relationship between spectral data and chemical component content.

[0071] The content mapping model takes as input the flavonoid quantitative representation vector of the TCM decoction piece sample to be tested, and outputs the total flavonoid content in the TCM decoction piece. Through training and validation on a standard sample set, the model establishes a quantitative relationship between the dimensional features of the flavonoid quantitative representation vector and the total flavonoid content, enabling accurate prediction of the total flavonoid content in unknown samples.

[0072] In one implementation of the embodiment of the present invention, a partial least squares regression method is used to establish a content mapping model, the number of principal components is set to 5, the cross-validation method is the leave-one-out method, and the model performance evaluation indicators are the determination coefficient and the root mean square error.

[0073] This embodiment achieves rapid and accurate detection of the total flavonoid content in Chinese herbal medicine slices by analyzing the spectral data of standard flavonoid compounds and the characteristic spectral data of Chinese herbal medicine slice samples, and can transform traditional chemical analysis methods into non-destructive and efficient spectral analysis methods, thereby enhancing the convenience and practicality of detection. By constructing a flavonoid characteristic peak recognition matrix and screening effective characteristic bands, it is possible to effectively identify the flavonoid characteristic information in complex Chinese herbal medicine matrices and achieve accurate identification of flavonoid components. This characteristic recognition strategy can ensure accurate detection under the interference of complex components, greatly improve the accuracy of Chinese herbal medicine slice quality control, and avoid the problems of complex sample pretreatment and long detection time in traditional methods. Through peak analysis and flavonoid spectral similarity calculation, the system can comprehensively identify the flavonoid characteristic peaks in Chinese herbal medicine samples, cope with the complexity and diversity requirements of different Chinese herbal medicine matrices, and screen out redundant bands through information redundancy analysis, thereby improving the specificity and robustness of the method. By introducing an information contribution evaluation mechanism for effective characteristic bands, the correlation and differentiation contribution between bands are analyzed in real time, the optimal characteristic band combination is predicted, and the characteristic band selection strategy can be dynamically optimized through the intersection operation of the candidate set to ensure efficient component identification when the characteristic bands change. The flavonoid quantitative characterization vector compiled based on the spectral absorption intensity and structural information of the effective characteristic bands can automatically generate a quantitative analysis model and perform content detection based on multidimensional characteristic indicators, thereby improving the adaptability to different Chinese medicine samples and significantly improving the detection accuracy. The content mapping relationship based on the nonlinear spectral inversion model can effectively address the limitations of traditional linear models in complex sample analysis and achieve more accurate content quantification. In this way, automated content detection is performed for the complex matrix of Chinese herbal medicine samples and the diversity of flavonoid components, reducing human operation errors and achieving more scientific and reliable quality evaluation of Chinese herbal medicine pieces.

[0074] Example 2

[0075] See also Figure 2 As shown, for the parts not described in detail in this embodiment, please refer to the description of Example 1. A system for detecting the total flavonoid content of Chinese herbal medicine slices based on Raman spectroscopy is provided, comprising:

[0076] Data acquisition module: acquires standard spectral data of standard flavonoid compounds and characteristic spectral data of Chinese herbal medicine samples;

[0077] Band screening module: evaluate the correlation between characteristic spectrum data and standard spectrum data to obtain the flavonoid characteristic peak identification matrix of each Chinese herbal medicine sample; and screen effective characteristic bands based on the flavonoid characteristic peak identification matrix;

[0078] Content detection module: compiles a flavonoid quantitative characterization vector of each Chinese herbal medicine sample based on the spectral absorption intensity and structural information of the effective characteristic band of each Chinese herbal medicine sample; and detects the content of total flavonoids in the Chinese herbal medicine sample based on the flavonoid quantitative characterization vector;

[0079] The modules are connected via wired and / or wireless means to achieve data transmission between modules.

[0080] The foregoing description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art will be able to modify the technical solutions described in the foregoing embodiments or to substitute equivalents for some of the technical features. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention shall be included within the scope of protection of the present invention.

[0081] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0082] In the description of the present invention, it should be understood that the terms "first", "second", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0083] In the description of the present invention, unless otherwise specified, "plurality" means two or more.

[0084] In the description of the present invention, “several” means one or more, and “a large number” means two or more.

[0085] Throughout this specification, reference to terms such as "one embodiment," "some embodiments," "examples," "specific examples," or "some examples" means that a specific feature, structure, material, or characteristic described in conjunction with that embodiment or example is included in at least one embodiment or example of the present invention. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0086] The formulas in this manual are all dimensionless and calculated using numerical values. The formulas are obtained by collecting a large amount of data and performing software simulation to obtain the most recent real situation. The preset parameters and thresholds in the formulas are set by technicians in this field based on actual conditions.

[0087] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to the embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the claims and their equivalents.

Claims

1. A method for detecting the total flavonoid content of Chinese herbal medicine slices based on Raman spectroscopy, characterized in that: include: Step S1: acquiring standard spectrum data of standard flavonoid compounds and characteristic spectrum data of Chinese herbal medicine slice samples; Step S2: performing correlation evaluation between the characteristic spectrum data and the standard spectrum data to obtain a flavonoid characteristic peak identification matrix for each Chinese herbal medicine sample; and screening effective characteristic bands based on the flavonoid characteristic peak identification matrix; Step S3: compiling a flavonoid quantitative characterization vector of each Chinese herbal medicine sample based on the spectral absorption intensity and structural information of the effective characteristic band of each sample; Detecting the content of total flavonoids in Chinese herbal medicine slices based on the flavonoid quantitative characterization vector; The step of obtaining the flavonoid characteristic peak identification matrix of each Chinese herbal medicine piece sample comprises: Perform peak analysis on the wavelength points in the characteristic spectrum data of all samples to obtain the absorption intensity of each wavelength point in the characteristic spectrum data of each sample; The wavelength points where the difference in absorption intensity of adjacent wavelength points within the analysis band of each sample exceeds the preset intensity threshold are counted and recorded as the flavonoid characteristic peak of each sample; The correlation coefficient between the absorption intensity of all wavelength points in the analysis band of each sample and the standard spectrum data was calculated as the flavonoid spectrum similarity of each sample; The number of flavonoid characteristic peaks of each sample and the flavonoid spectrum similarity are combined to form a flavonoid characteristic peak recognition matrix; The screening of effective characteristic bands based on the flavonoid characteristic peak recognition matrix includes: Perform difference analysis on the characteristic spectral data of all samples to obtain the distinguishing contribution of each band in the characteristic spectral data; Perform a modulo operation on the flavonoid characteristic peak recognition matrix to obtain the sample eigenvalues. Perform redundancy analysis based on the sample eigenvalues ​​and the discrimination contribution of any band in the characteristic spectral data to obtain the information redundancy of the corresponding two bands. The continuous intervals formed by the bands in the characteristic spectrum data whose information redundancy is less than the preset redundancy threshold are recorded as candidate bands; the information contribution of the candidate bands in the characteristic spectrum data is evaluated to obtain effective characteristic bands; The information redundancy is the ratio of the distinction contribution to the sample characteristic value; The step of evaluating the information contribution of candidate bands in the characteristic spectrum data to obtain effective characteristic bands includes: Traverse the candidate bands in the characteristic spectrum data, use the result of each traversal as the reference band, use the reference band as the evaluation point for band screening, record the other candidate bands that meet the preset contribution conditions except the evaluation point as the candidate bands, and all the candidate bands and the evaluation point constitute the candidate set; record the candidate set after each traversal; after the traversal is completed, take the intersection of all recorded candidate sets to obtain the corresponding valid characteristic band; The difference between the maximum and minimum values ​​of the absorption intensity ratio of any two candidate bands is recorded as the absorption intensity difference; The preset contribution conditions include: the wavelength interval from the evaluation point is less than a preset interval threshold and the absorption intensity difference from the evaluation point is greater than a preset intensity difference threshold; The method of preparing the flavonoid quantitative characterization vector of the sample includes: Determine the absorption peak area of ​​the effective characteristic band of each Chinese herbal medicine sample, normalize and vectorize the absorption peak area to obtain a standardized spectral intensity matrix; obtain characteristic indicators of the standardized spectral intensity matrix, wherein the characteristic indicators include peak intensity ratio; According to the spectral shape of the effective characteristic band of each Chinese herbal medicine sample and the peak intensity ratio, the flavonoid content parameter of the corresponding sample is obtained; The flavonoid content parameter of each Chinese herbal medicine slice sample and the remaining characteristic indicators except the peak intensity ratio constitute a flavonoid quantitative characterization vector of the corresponding sample.

2. The method for detecting the total flavonoid content of Chinese herbal medicine slices based on Raman spectroscopy according to claim 1, wherein: The method of detecting the content of total flavonoids in Chinese herbal medicine pieces based on the flavonoid quantitative characterization vector comprises: Obtain the standard quantitative vector of the standard Chinese herbal medicine samples with known total flavonoid content; establishing a content mapping model between the standard quantitative vector and the known total flavonoid content; The flavonoid quantitative characterization vector of the Chinese herbal medicine sample to be tested is substituted into the content mapping model to calculate the total flavonoid content in the Chinese herbal medicine sample.

3. The method for detecting the total flavonoid content of Chinese herbal medicine slices based on Raman spectroscopy according to claim 2, wherein: The method for performing difference analysis on the characteristic spectrum data of all samples is a characteristic wavelength screening algorithm.

4. The method for detecting the total flavonoid content of Chinese herbal medicine slices based on Raman spectroscopy according to claim 3, characterized in that: The method for acquiring the characteristic spectrum data and the standard spectrum data is Raman spectrum acquisition technology.

5. The method for detecting the total flavonoid content of Chinese herbal medicine slices based on Raman spectroscopy according to claim 4, characterized in that: The content mapping model adopts a nonlinear spectral inversion model and is established through the correspondence between the characteristic absorption peak area and the actual content of the standard flavonoid compound. The input of the content mapping model is the flavonoid quantitative characterization vector of the Chinese herbal medicine sample to be tested, and the output is the content of total flavonoids in the Chinese herbal medicine sample.

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