A method and system for detecting the degree of hydrogenation of fatty acids based on spectral analysis

Abstract

The present invention discloses a method and system for detecting the hydrogenation degree of fatty acids based on spectral analysis, specifically relating to the field of fatty acid hydrogenation detection, including S1 obtaining the first preprocessing features of the sample to be tested, S2 performing preprocessing operations on the first preprocessing features of the sample to be tested, S3 obtaining the second preprocessing features, S4 obtaining the second key features, S5 obtaining the third key features, S6 obtaining the comprehensive fatty acid hydrogenation evaluation model, and S7 presenting the comprehensive fatty acid hydrogenation evaluation report. A method for detecting the hydrogenation degree of fatty acids based on spectral analysis maximizes the extraction of the purity of fatty acids in the sample to be tested through preprocessing operations, removes impurities and interfering substances, and improves the accuracy and reliability of the detection results; by constructing a learning model and a preset database, it reduces the excessive dependence on the professional skills of operators; by comprehensively applying a variety of spectral technologies, it can give full play to the advantages of various technologies and make up for their respective limitations.

Description

Technical Field

[0001] The present invention relates to the technical field of fatty acid hydrogenation detection, and more specifically, the present invention relates to a method and system for detecting the degree of fatty acid hydrogenation based on spectral analysis. Background Art

[0002] With the rapid development of the food industry and the increasing attention of people to food safety and health, the accurate detection of the degree of hydrogenation of food components, especially fatty acids, has become particularly important. Partial hydrogenation products such as trans fatty acids widely present in modern diets, and the detection of the degree of fatty acid hydrogenation can also help food processing enterprises optimize production processes, improve production efficiency, and reduce production costs.

[0003] As a non-destructive and rapid detection method, spectral analysis can quickly and accurately determine the degree of hydrogenation in foods by analyzing the absorption characteristics of fatty acid molecules to light of specific wavelengths, greatly improving the quality control efficiency in the food processing process.

[0004] However, in actual use, there are still some disadvantages, such as the influence of interfering substances in the sample matrix, resulting in a decrease in the accuracy of the detection results, high requirements for equipment accuracy, strict requirements for the professional skills of operators, insufficient detection sensitivity for trace components in some complex matrices, and the existence of respective limitations in the application of different spectral technologies. Summary of the Invention

[0005] In order to overcome the above-mentioned defects of the prior art, the present invention provides a method and system for detecting the degree of fatty acid hydrogenation based on spectral analysis, through the following solutions to solve the problems raised in the above background art.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] A method for detecting the degree of fatty acid hydrogenation based on spectral analysis, comprising:

[0008] S1. Obtain the first preprocessing feature of the sample to be tested: Based on the analysis requirements of the sample, the sample to be tested is denoted as the sample to be tested, and the first preprocessing feature of the sample to be tested is obtained;

[0009] S2. Perform a preprocessing operation on the first preprocessing feature of the sample to be tested: Perform a preprocessing operation on the first preprocessing feature of the sample to be tested, and the preprocessing operation is used to obtain the first key feature corresponding to the first preprocessing feature of the sample to be tested;

[0010] S3. Obtain the second preprocessing feature: Obtain the second preprocessing feature corresponding to the first key feature, and the second preprocessing feature is to collect spectral data on the first key feature using a high-resolution spectrometer, and the collection method uses multi-modal spectral technology;

[0011] S4. Obtain the second key feature: Obtain a spectral calibration model, and according to the spectral calibration model and through the second preprocessing feature, obtain the second key feature corresponding to the second preprocessing feature;

[0012] S5. Obtain the third key feature: According to chemometric analysis and through the second key feature, obtain the third key feature corresponding to the second key feature;

[0013] S6. Obtain a comprehensive fatty acid hydrogenation evaluation model: Obtain a comprehensive fatty acid hydrogenation evaluation model, and according to the comprehensive fatty acid hydrogenation evaluation model and through the third key feature, obtain the analysis result of the third key feature;

[0014] S7. Display a comprehensive fatty acid hydrogenation evaluation report: According to the comprehensive fatty acid hydrogenation evaluation model, through the third key feature, send the hydrogenation degree of the fatty acid corresponding to the third key feature to the user in a preset display manner based on the result of comprehensive analysis.

[0015] Preferably, the steps for obtaining the first preprocessing feature of the sample to be tested in S1 include:

[0016] A1: Record the basic information of each sample to be tested in the set of samples to be tested. The basic information includes the name of the sample to be tested, the source of the sample to be tested, the batch of the sample to be tested, and the storage conditions of the sample to be tested;

[0017] A2: Measure the physical properties of each sample to be tested in the set of samples to be tested. The physical properties include the color of the sample to be tested, the transparency of the sample to be tested, and the texture of the sample to be tested;

[0018] A3: Analyze the chemical properties of each sample to be tested in the set of samples to be tested. The chemical properties include the pH value of the sample to be tested, the moisture content of the sample to be tested, and the fatty acid composition of the sample to be tested;

[0019] A4: Uniquely mark the original features of each sample to be tested in the set of samples to be tested. Use numbers to mark each sample to be tested in the set of samples to be tested, and associate the marking information of each sample with the original features of each sample to form the first preprocessing feature.

[0020] Preferably, the preprocessing steps for the first preprocessing feature of the sample to be tested in S2 include:

[0021] B1: Solvent optimization: According to the physical and chemical properties of the sample in the first preprocessing feature of the sample to be tested, select a solvent to maximize the purity of fatty acids extracted from the sample to be tested;

[0022] B2: Sample purification: The sample to be tested passes through solvent borrowing purification technology and homogenization technology to remove impurities and interfering substances in the sample to be tested, and obtain a pure fatty acid extract in the sample to be tested;

[0023] B3: Sample concentration: The pure fatty acid extract in the sample to be measured is concentrated by a concentration technique to reduce the solvent volume, so as to increase the concentration of fatty acids and obtain a fatty acid sample after concentration of the sample to be measured.

[0024] B4: Obtain the first key feature: Analyze the fatty acid sample after concentration of the sample to be measured to obtain the first key feature related to the degree of fatty acid hydrogenation. The first key feature includes the double bond position, cis-trans configuration, and carbon chain length information of fatty acids.

[0025] Preferably, obtaining the spectral correction model in S4 specifically includes:

[0026] According to the second preprocessing feature, perform a correction operation on the second preprocessing feature to obtain the second key feature corresponding to the second preprocessing feature. The second key feature is the target correction spectral feature corresponding to the second preprocessing feature, and the target correction spectral feature includes the chemical bond vibration frequency, the change range of molecular polarizability, and the change mode of fluorescence emission intensity.

[0027] In a preset spectral database, obtain the spectral correction model corresponding to the key feature. The preset spectral database is used to store the correspondence between the key feature and the spectral correction model.

[0028] Preferably, obtaining the second key feature corresponding to the second preprocessing feature in S4 specifically includes:

[0029] Obtain the correction feature correlation value. The correction feature correlation value is the correlation degree between the second preprocessing feature and the target correction spectral feature to determine whether the correlation degree reaches a preset threshold.

[0030] If the correlation degree reaches the preset threshold, confirm that the target correction spectral feature is the second key feature corresponding to the second preprocessing feature.

[0031] Preferably, before obtaining the spectral correction model in S4 and obtaining the target correction spectral feature corresponding to the second key feature according to the spectral correction model, constructing the spectral correction model specifically includes:

[0032] Obtain the spectral correction set corresponding to the target correction spectral feature;

[0033] According to the spectral correction set, obtain the characteristic parameters and characteristic modes corresponding to the target correction spectral feature;

[0034] According to the characteristic parameters and characteristic modes, construct the spectral correction model.

[0035] Preferably, the spectral correction set obtained in S4 includes feature recognition technology, separation signal algorithm, signal enhancement technology, and resolution optimization.

[0036] Preferably, the step of obtaining the third key feature of the sample to be tested in S5 includes:

[0037] C1: Detect the second key feature with the aid of a chemometric tool;

[0038] C2: Calculate the degree of hydrogenation in the second key feature using a variety of chemometric methods;

[0039] C3: Use a machine learning algorithm to perform pattern recognition and classification on the second key feature to determine the degree of hydrogenation of the fatty acid;

[0040] C4: Compare the physical and chemical properties of the second key feature in the sample to be tested, and verify and form the third key feature after verification.

[0041] To achieve the above object, the present invention provides the following technical solution: A fatty acid hydrogenation degree detection system based on spectral analysis, including a system operation database, a system central processing module, and a user information section. Implementing the above-mentioned fatty acid hydrogenation degree detection method based on spectral analysis further includes:

[0042] The system operation database includes all data texts of the fatty acid hydrogenation degree detection system, and real-time collects the information texts output by each module. The system central processing module is used to control the information text instructions output by each module in the system. The user information terminal is an information output device for receiving the fatty acid hydrogenation degree detection system;

[0043] The first preprocessing feature acquisition module: Based on the analysis requirements of the sample, the sample to be tested is denoted as the sample to be tested, and the first preprocessing feature of the sample to be tested is obtained;

[0044] The first preprocessing feature preprocessing module: Perform preprocessing operations on the first preprocessing feature of the sample to be tested. The preprocessing operation is used to obtain the first key feature corresponding to the first preprocessing feature of the sample to be tested;

[0045] The second preprocessing feature acquisition module: Obtain the second preprocessing feature corresponding to the first key feature. The second preprocessing feature is to collect spectral data for the first key feature using a high-resolution spectrometer, and the collection method uses multimodal spectroscopy technology;

[0046] The second key feature acquisition module: Obtain a spectral correction model, and obtain the second key feature corresponding to the second preprocessing feature through the second preprocessing feature according to the spectral correction model;

[0047] The third key feature acquisition module: Obtain the third key feature corresponding to the second key feature through the second key feature according to chemometric analysis;

[0048] Comprehensive Fatty Acid Hydrogenation Evaluation Model Acquisition Module: Obtain the comprehensive fatty acid hydrogenation evaluation model, and according to the comprehensive fatty acid hydrogenation evaluation model, obtain the analysis result of the third key feature through the third key feature;

[0049] Comprehensive Fatty Acid Hydrogenation Evaluation Report Output Module: According to the comprehensive fatty acid hydrogenation evaluation model, through the third key feature, send the hydrogenation degree of the fatty acid corresponding to the third key feature to the user in a preset display manner based on the result of comprehensive analysis.

[0050] Preferably, the acquisition steps in the second key feature acquisition module are as follows: According to the second preprocessing feature, perform a correction operation on the second preprocessing feature to obtain the second key feature corresponding to the second preprocessing feature. The second key feature is the target correction spectral feature corresponding to the second preprocessing feature, and the target correction spectral feature includes the chemical bond vibration frequency, the change range of molecular polarizability, and the change mode of fluorescence emission intensity; Obtain the correction feature correlation value, which is the correlation degree between the second preprocessing feature and the target correction spectral feature, to determine whether the correlation degree reaches a preset threshold; If the correlation degree reaches the preset threshold, confirm that the target correction spectral feature is the second key feature corresponding to the second preprocessing feature; Obtain the spectral correction set corresponding to the target correction spectral feature. The spectral correction set includes feature recognition technology, separation signal algorithm, signal enhancement technology, and resolution optimization; Obtain the feature parameters and feature patterns corresponding to the target correction spectral feature; According to the feature parameters and feature patterns, construct a spectral correction model.

[0051] Technical Effects and Advantages of the Present Invention:

[0052] Through the preprocessing operations of solvent optimization, sample purification, and sample concentration, the present invention maximizes the extraction of the purity of fatty acids in the sample to be measured, removes impurities and interfering substances, and improves the accuracy of subsequent analysis and the reliability of detection results;

[0053] Through the constructed learning model and preset database, the present invention is automatically executed, reducing the excessive dependence on the professional skills of operators;

[0054] By comprehensively applying a variety of spectral technologies, the present invention can give full play to the advantages of various technologies and make up for their respective limitations. Description of the Drawings

[0055] Figure 1 It is a method step diagram of the present invention.

[0056] Figure 2 It is a system flow chart of the present invention.

[0057] Figure 3 It is a system structure schematic diagram of the present invention. Detailed Embodiments

[0058] Next, in combination with the accompanying drawings in the embodiments of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without making creative efforts belong to the scope of protection of the present invention.

[0059] The terms used in the following embodiments of this application are only for the purpose of describing specific embodiments and are not intended to limit this application. As used in the specification of this application, the singular forms "a", "an", "the", "above-mentioned", "said", and "this" are also intended to include the plural forms, unless clearly indicated to the contrary in the context. It should also be understood that the term "and / or" used in this application refers to and includes any or all possible combinations of one or more of the listed items.

[0060] Hereinafter, the terms "first", "second", and "third" are only used for descriptive purposes and cannot be construed as implying or suggesting relative importance or implicitly indicating the quantity of the indicated technical features. Thus, the features defined with "first", "second", and "third" may explicitly or implicitly include one or more of such features. In the description of the embodiments of this application, unless otherwise specified, the meaning of "a plurality" is two or more.

[0061] As shown in the attached Figure 1 A method for detecting the degree of fatty acid hydrogenation based on spectral analysis includes S1, obtaining the first preprocessing feature of the sample to be tested; S2, performing a preprocessing operation on the first preprocessing feature of the sample to be tested; S3, obtaining the second preprocessing feature; S4, obtaining the second key feature; S5, obtaining the third key feature; S6, obtaining a comprehensive fatty acid hydrogenation evaluation model; and S7, presenting a comprehensive fatty acid hydrogenation evaluation report.

[0062] S1. Obtaining the first preprocessing feature of the sample to be tested: Based on the analysis requirements of the sample, the sample to be detected is denoted as the sample to be tested, and the first preprocessing feature of the sample to be tested is obtained.

[0063] Specifically, through the requirement analysis of multiple samples to be tested, the types of samples to be tested include, but are not limited to, foods, oil products, other samples containing fatty acids, etc. The types of samples to be tested in this embodiment are not limited; after the requirement analysis of multiple samples to be tested, a set of samples to be detected is constructed as the sample set to be tested. In this embodiment, an oil sample will be used as an example for illustration. The first preprocessing feature is to obtain the original features of each sample to be tested in the sample set to be tested before preliminary processing, and the original features include the basic information, physical properties, and chemical properties of the sample.

[0064] In a possible implementation manner, the step of obtaining the first pretreatment feature of the sample to be tested in step S1 includes:

[0065] A1: Record the basic information of each sample to be tested in the set of samples to be tested. The basic information includes the name of the sample to be tested, the source of the sample to be tested, the batch of the sample to be tested, and the storage conditions of the sample to be tested;

[0066] A2: Measure the physical properties of each sample to be tested in the set of samples to be tested. The physical properties include the color of the sample to be tested, the transparency of the sample to be tested, and the texture of the sample to be tested;

[0067] A3: Analyze the chemical properties of each sample to be tested in the set of samples to be tested. The chemical properties include the pH value of the sample to be tested, the moisture content of the sample to be tested, and the fatty acid composition of the sample to be tested;

[0068] A4: Uniquely mark the original features of each sample to be tested in the set of samples to be tested. Use numbers to mark each sample to be tested in the set of samples to be tested, and associate the marking information of each sample to be tested with the original features of each sample to be tested to form the first pretreatment feature.

[0069] In step S1, by recording the basic information of the samples to be tested, it is ensured that the source and storage conditions of each sample are accurately recorded, facilitating traceability and management; by measuring the physical properties of the samples to be tested, the appearance quality and preliminary state of the samples are evaluated, providing an intuitive reference for subsequent analysis; by analyzing the chemical properties of the samples to be tested, the basic composition of the samples is understood, laying a foundation for subsequent chemometric analysis; by uniquely marking each sample to be tested and associating the marking information with the original features, the uniqueness and traceability of each sample during the entire detection process are ensured, avoiding confusion and errors.

[0070] S2. Perform a pretreatment operation on the first pretreatment feature of the sample to be tested: Perform a pretreatment operation on the first pretreatment feature of the sample to be tested. The pretreatment operation is used to obtain the first key feature corresponding to the first pretreatment feature of the sample to be tested.

[0071] Specifically, after the fatty acid hydrogenation degree detection system obtains the first pretreatment feature of the sample to be tested, through the pretreatment operation, multiple key features corresponding to the first pretreatment feature of the sample to be tested, that is, the first key feature, can be obtained. In a possible implementation manner, the pretreatment step of the first pretreatment feature of the sample to be tested in step S2 includes:

[0072] B1: Solvent optimization: According to the physical and chemical properties of the sample in the first pretreatment feature of the sample to be tested, select a solvent to maximize the extraction purity of fatty acids in the sample to be tested;

[0073] Specifically, the physical properties of the sample in the first pretreatment feature of the sample to be tested include the polarity, solubility, and viscosity of the sample to determine the compatibility between the sample and the solvent; the chemical properties of the sample in the first pretreatment feature of the sample to be tested include the composition of fatty acids, the saturation content, and the stability to improve the extraction efficiency and purity; adjust the ratio of the solvent to the sample, the extraction time, and the temperature conditions to optimize the extraction process to maximize the extraction purity of fatty acids;

[0074] In this embodiment, polar solvents are used to extract polar fatty acids, and non-polar solvents are used to extract non-polar fatty acids;

[0075] B2: Sample purification: The sample to be tested is purified by solvent borrowing purification technology and homogenization technology to remove impurities and interfering substances in the sample to be tested, and a pure fatty acid extract in the sample to be tested is obtained;

[0076] Specifically, the purification technologies include liquid-liquid extraction, solid-phase extraction, and membrane separation technology; liquid-liquid extraction is to achieve the purpose of separation, extraction, and purification by the different distribution ratios of different components in the sample in two immiscible solvents; solid-phase extraction is to adsorb and elute specific components in the sample through a solid adsorbent to achieve separation and purification; membrane separation technology is to utilize the selective permeability of a semi-permeable membrane to achieve the separation of components through pressure driving;

[0077] Specifically, the homogenization technology includes using a homogenizer and an ultrasonic crushing method to break the fat in the sample into smaller particles, thereby making the whole product system more stable;

[0078] Specifically, the pure fatty acid extract in the sample to be tested is a sample containing target fatty acids and having a low impurity content obtained through solvent borrowing purification technology and homogenization technology;

[0079] B3: Sample concentration: The pure fatty acid extract in the sample to be tested is concentrated by concentration technology to reduce the solvent volume to increase the concentration of fatty acids and obtain a concentrated fatty acid sample of the sample to be tested;

[0080] Specifically, the concentration technologies include evaporation, freeze-drying, and a rotary evaporator: Evaporation technology is to reduce the solvent volume by heating to concentrate fatty acids and is carried out under reduced pressure conditions to lower the boiling point of the solvent and reduce the risk of damage to heat-sensitive substances; Freeze-drying is a process of directly vaporizing the solvent from a solid state without passing through a liquid state under low temperature and low pressure conditions, which is particularly suitable for the concentration of heat-sensitive substances; A rotary evaporator is to use a vacuum pump to lower the boiling point of the solvent and at the same time increase the liquid surface area by rotation to accelerate the evaporation process;

[0081] B4: Obtain the first key feature: Analyze the fatty acid sample after concentrating the sample to be tested, and obtain the first key feature related to the degree of fatty acid hydrogenation. The first key feature includes the melting point of the fatty acid sample, the boiling point of the fatty acid sample, the iodine value of the fatty acid sample, and the odor of the fatty acid sample.

[0082] Specifically, since the increase in the degree of hydrogenation leads to an increase in the melting point of the fatty acid, use a melting point measuring instrument to measure according to the standard operating procedure; the change in the boiling point reflects the degree of hydrogenation of the fatty acid. Measure the boiling point of the sample with a boiling point measuring instrument and compare and analyze it with the known data; the degree of hydrogenation of the fatty acid is reflected by the iodine value determination. The Wijs method is used for the determination. After reacting the sample with the Wijs reagent, the iodine value is determined by titration; the odor components of the sample are analyzed by gas chromatography-mass spectrometry.

[0083] In step S2, through solvent optimization, the purity of the fatty acid in the sample to be tested is maximized to improve the accuracy of subsequent analysis; through sample purification, impurities and interfering substances in the sample to be tested are removed to obtain a pure fatty acid extract and improve the reliability of the detection result; through sample concentration, the solvent volume is reduced, the concentration of the fatty acid is increased, and a clearer signal is provided for spectral analysis; by obtaining the first key feature, analyze the fatty acid sample after concentrating the sample to be tested, and obtain the characteristic information related to the degree of fatty acid hydrogenation, providing key data support for the final evaluation.

[0084] S3. Obtain the second preprocessing feature: Obtain the second preprocessing feature corresponding to the first key feature. The second preprocessing feature is to collect spectral data of the first key feature using a high-resolution spectrometer, and the collection method uses multimodal spectroscopy.

[0085] Specifically, the fatty acid hydrogenation degree detection system obtains the second preprocessing feature, which is to collect spectral data of the sample to be tested corresponding to the first key feature using a high-resolution spectrometer, and the collection method uses multimodal spectroscopy.

[0086] In this embodiment, the process by which the fatty acid hydrogenation degree detection system obtains the second preprocessing feature is as follows: Use a spectrometer with high sensitivity and precise resolution. The spectrometer includes a Fourier transform infrared spectrometer and an ultraviolet-visible spectrophotometer to collect spectral data of the first key feature; during the collection process, multimodal spectroscopy is used to obtain richer and more comprehensive spectral information; multimodal spectroscopy comprehensively uses various different types of spectral techniques. The spectral techniques include infrared spectroscopy, Raman spectroscopy, and fluorescence spectroscopy to comprehensively obtain the second preprocessing feature of the sample. The second preprocessing feature includes the absorption intensity of the sample at different infrared bands, the frequency shift of the scattered light of the sample, and the fluorescence emission intensity at different excitation wavelengths.

[0087] In this embodiment, the infrared spectrum provides information about the vibration of chemical bonds in fatty acid molecules, the Raman spectrum reflects the change in molecular polarizability, and the fluorescence spectrum detects the fluorescence characteristics of fatty acid molecules. By comprehensively applying multiple spectral techniques, the fatty acid is analyzed from multiple perspectives to improve the accuracy and reliability of the second pretreatment feature.

[0088] In step S3, by using a high-resolution spectrometer, high-precision spectral data is obtained to improve the accuracy and reliability of the detection results; by adopting multimodal spectral techniques, richer and more comprehensive spectral information is obtained to analyze the characteristics of fatty acids from multiple perspectives, thereby improving the comprehensiveness and reliability of the detection; by comprehensively applying multiple spectral techniques such as infrared spectrum, Raman spectrum, and fluorescence spectrum, the second pretreatment feature of the sample is comprehensively obtained to ensure the accuracy and reliability of the detection results; by obtaining the absorption intensity of the sample at different infrared bands, the frequency shift of the scattered light of the sample, and the fluorescence emission intensity at different excitation wavelengths, the structure and properties of fatty acids are deeply understood at the molecular level, providing a solid data basis for subsequent comprehensive analysis.

[0089] S4. Obtain the second key feature: Obtain a spectral correction model, and according to the spectral correction model and through the second pretreatment feature, obtain the second key feature corresponding to the second pretreatment feature.

[0090] Specifically, the spectral correction model is a pre-constructed learning model. By inputting the second pretreatment feature into the spectral correction model, the spectral correction model obtains the second key feature according to the second pretreatment feature.

[0091] In a possible implementation manner, step S4 includes: performing a correction operation on the second pretreatment feature according to the second pretreatment feature to obtain the second key feature corresponding to the second pretreatment feature. The second key feature is the target correction spectral feature corresponding to the second pretreatment feature, and the target correction spectral feature includes the chemical bond vibration frequency, the change range of molecular polarizability, and the change mode of fluorescence emission intensity; in a preset spectral database, obtain the spectral correction model corresponding to the key feature. The preset spectral database is used to store the correspondence between the key feature and the spectral correction model.

[0092] In this embodiment, when performing the correction operation, a detailed analysis of the second pretreatment feature is carried out; specifically, the chemical bond vibration frequency reflects the change in the vibration frequency of different chemical bonds in fatty acid molecules under different hydrogenation degrees; the change range of molecular polarizability reflects the change in the polarization degree of fatty acid molecules under the action of an electric field; the change mode of fluorescence emission intensity reflects the information of the structure and electronic transition of fatty acid molecules; after obtaining the second pretreatment feature, the key feature matching it will be searched in the database, and the corresponding spectral correction model will be obtained.

[0093] In a possible implementation, step S4 further includes: obtaining a calibration feature correlation value, where the calibration feature correlation value is the degree of correlation between the second preprocessed feature and the target calibration spectral feature, to determine whether the degree of correlation reaches a preset threshold; if the degree of correlation reaches the preset threshold, confirm that the target calibration spectral feature is the second key feature corresponding to the second preprocessed feature.

[0094] In this embodiment, the calibration feature correlation value is calculated by a statistical method to reflect the correlation between the second preprocessed feature and the target calibration spectral feature; the preset threshold is determined according to historical data. If the degree of correlation reaches or exceeds the threshold, it is considered that the target calibration spectral feature is highly correlated with the second preprocessed feature and is confirmed as the second key feature.

[0095] In a possible implementation, step S4 further includes: obtaining a spectral calibration set corresponding to the target calibration spectral feature. According to the spectral calibration set, the spectral calibration set includes feature recognition technology, signal separation algorithm, signal enhancement technology, and resolution optimization; obtaining the feature parameters and feature patterns corresponding to the target calibration spectral feature; constructing a spectral calibration model according to the feature parameters and feature patterns.

[0096] Specifically, the feature recognition technology is used to identify the feature information related to the degree of fatty acid hydrogenation; the signal separation algorithm removes noise and interference signals; the signal enhancement technology improves the intensity and clarity of the signal; the resolution optimization improves the resolution of the spectral data.

[0097] Step S4 obtains the second key feature according to the second preprocessed feature by obtaining the spectral calibration model, improving the accuracy and reliability of the detection result; by performing a calibration operation on the second preprocessed feature to obtain the target calibration spectral feature, thus more accurately reflecting the characteristics of fatty acid molecules and improving the detection accuracy; by obtaining the calibration feature correlation value to judge the degree of correlation between the second preprocessed feature and the target calibration spectral feature, ensuring that the obtained second key feature conforms to the actual situation of the sample; by constructing a spectral calibration model to perform feature recognition, signal separation, signal enhancement, and resolution optimization on the spectral data, thus improving the analysis quality of the spectral data and the reliability of the detection result.

[0098] S5. Obtain the third key feature: According to chemometric analysis through the second key feature, obtain the third key feature corresponding to the second key feature.

[0099] In a possible implementation, the steps for obtaining the third key feature of the sample to be tested in S5 include:

[0100] C1: Detect the second key feature with the help of chemometric tools;

[0101] C2: Use a variety of chemometric methods to calculate the degree of hydrogenation in the second key feature;

[0102] C3: Use machine learning algorithms to perform pattern recognition and classification on the second key feature to determine the degree of hydrogenation of fatty acids;

[0103] C4: Compare the physical and chemical properties of the second key feature in the sample to be tested, and after verification, form the third key feature.

[0104] Specifically, in the detection of the degree of hydrogenation of fatty acids, the stoichiometric tools used include spectrometers, chromatographs, and mass spectrometers; in the detection of the degree of hydrogenation of fatty acids, the chemometric methods used include partial least squares method, principal component analysis, and cluster analysis to extract useful information and establish a model to detect the degree of hydrogenation of fatty acids. The machine learning algorithms used in the detection of the degree of hydrogenation of fatty acids include support vector machines, artificial neural networks, and decision trees to determine the degree of hydrogenation of fatty acids;

[0105] Specifically, compare the physical properties of the second key feature in the sample to be tested with the sample data, and at the same time compare the chemical properties of the fatty acids in the second key feature with the expected hydrogenation effect; further verify the consistency between the second key feature and the expected degree of hydrogenation to confirm whether the actual degree of hydrogenation of the fatty acids is consistent with the situation reflected by the second key feature; based on the comprehensive comparison and verification results, integrate and refine the information related to the degree of hydrogenation in the second key feature to form the third key feature.

[0106] Step S5 detects the second key feature by means of stoichiometric tools to more accurately analyze the chemical composition of the sample and provide a reliable data basis for subsequent calculation of the degree of hydrogenation; calculate the degree of hydrogenation in the second key feature by using various chemometric methods to evaluate the hydrogenation state of fatty acids from different angles and dimensions and improve the accuracy and credibility of the results; perform pattern recognition and classification on the second key feature by using machine learning algorithms to automatically identify the degree of hydrogenation of fatty acids and improve the automation level and efficiency of detection; compare and verify the physical and chemical properties of the second key feature to ensure that the finally formed third key feature reflects the true sample situation and enhances the scientificity and reliability of the results.

[0107] S6. Obtain a comprehensive fatty acid hydrogenation evaluation model: Obtain a comprehensive fatty acid hydrogenation evaluation model, and according to the comprehensive fatty acid hydrogenation evaluation model, obtain the analysis result of the third key feature through the third key feature.

[0108] Specifically, the comprehensive fatty acid hydrogenation evaluation model is a pre-constructed learning model. The third key feature is input into the comprehensive fatty acid hydrogenation evaluation model, and the comprehensive fatty acid hydrogenation evaluation model obtains the analysis result of the third key feature according to the third key feature.

[0109] Specifically, after obtaining the third key feature, the fatty acid hydrogenation degree detection system analyzes the third key feature through the comprehensive fatty acid hydrogenation evaluation model to obtain the evaluation result of the fatty acid hydrogenation degree. The comprehensive fatty acid hydrogenation evaluation model includes multiple linear regression, artificial neural network, and extreme learning machine. The selection of the comprehensive fatty acid hydrogenation evaluation model depends on the characteristics of the third key feature and obtains fast and accurate analysis results.

[0110] S7. Display the comprehensive fatty acid hydrogenation evaluation report: According to the comprehensive fatty acid hydrogenation evaluation model, through the third key feature, the analysis result of the comprehensive analysis sends the fatty acid hydrogenation degree corresponding to the third key feature to the user according to the preset display method.

[0111] Specifically, the preset display method includes intuitive charts and report forms, enabling users to clearly understand the specific situation of the fatty acid hydrogenation degree. Among them, the specific numerical value of the hydrogenation degree can provide accurate quantitative information, allowing users to intuitively understand the hydrogenation degree of the fatty acid. At the same time, the comprehensive analysis also includes possible uncertainty analysis, helping users comprehensively understand the limitations and potential risks of the evaluation results, so that users can fully consider these factors when making decisions.

[0112] As shown in the appendix Figure 2 A fatty acid hydrogenation degree detection system based on spectral analysis, including a system operation database, a system central processing module, and a user information section, further includes a first preprocessing feature acquisition module, a first preprocessing feature preprocessing module, a second preprocessing feature acquisition module, a second key feature acquisition module, a third key feature acquisition module, a comprehensive fatty acid hydrogenation evaluation model acquisition module, and a comprehensive fatty acid hydrogenation evaluation report output module.

[0113] Specifically, the system operation database includes all data texts of the fatty acid hydrogenation degree detection system and collects the information texts output by each module in real time. The system central processing module is used to control the information text instructions output by each module in the system, and the user information terminal is an information output device that receives the fatty acid hydrogenation degree detection system.

[0114] The first preprocessing feature acquisition module: Based on the analysis requirements of the sample, the sample to be detected is denoted as the sample to be measured, and the first preprocessing feature of the sample to be measured is obtained.

[0115] First preprocessing feature preprocessing module: Perform preprocessing operations on the first preprocessing feature of the sample to be measured. The preprocessing operations are used to obtain the first key feature corresponding to the first preprocessing feature of the sample to be measured;

[0116] Second preprocessing feature acquisition module: Acquire the second preprocessing feature corresponding to the first key feature. The second preprocessing feature is to collect spectral data of the first key feature using a high-resolution spectrometer, and the acquisition method uses multimodal spectroscopy technology;

[0117] Second key feature acquisition module: Obtain a spectral correction model, and based on the spectral correction model and through the second preprocessing feature, obtain the second key feature corresponding to the second preprocessing feature;

[0118] Third key feature acquisition module: Based on chemometric analysis and through the second key feature, obtain the third key feature corresponding to the second key feature;

[0119] Comprehensive fatty acid hydrogenation evaluation model acquisition module: Obtain a comprehensive fatty acid hydrogenation evaluation model, and based on the comprehensive fatty acid hydrogenation evaluation model and through the third key feature, obtain the analysis result of the third key feature;

[0120] Comprehensive fatty acid hydrogenation evaluation report output module: Based on the comprehensive fatty acid hydrogenation evaluation model and through the third key feature, send the degree of fatty acid hydrogenation corresponding to the third key feature to the user in a preset display manner according to the comprehensive analysis result.

[0121] In this embodiment, the acquisition steps in the second key feature acquisition module are as follows: Based on the second preprocessing feature, perform a correction operation on the second preprocessing feature to obtain the second key feature corresponding to the second preprocessing feature. The second key feature is the target corrected spectral feature corresponding to the second preprocessing feature, and the target corrected spectral feature includes the chemical bond vibration frequency, the change range of molecular polarizability, and the change mode of fluorescence emission intensity; Obtain the correction feature correlation value, where the correction feature correlation value is the correlation degree between the second preprocessing feature and the target corrected spectral feature to determine whether the correlation degree reaches a preset threshold; If the correlation degree reaches the preset threshold, confirm that the target corrected spectral feature is the second key feature corresponding to the second preprocessing feature; Obtain the spectral correction set corresponding to the target corrected spectral feature. The spectral correction set includes feature recognition technology, separation signal algorithm, signal enhancement technology, and resolution optimization; Obtain the feature parameters and feature patterns corresponding to the target corrected spectral feature; Based on the feature parameters and feature patterns, construct a spectral correction model.

[0122] Secondly: In the accompanying drawings of the disclosed embodiments of the present invention, only the structures related to the disclosed embodiments are involved. For other structures, reference can be made to the usual designs. Without conflict, the same embodiment and different embodiments of the present invention can be combined with each other;

[0123] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for detecting the degree of hydrogenation of fatty acids based on spectral analysis, characterized in that, Including: S1. Obtain the first preprocessing feature of the sample to be tested: Based on the analysis requirements of the sample, mark the sample to be detected as the sample to be tested, and obtain the first preprocessing feature of the sample to be tested; S2. Perform a preprocessing operation on the first preprocessing feature of the sample to be tested: Perform a preprocessing operation on the first preprocessing feature of the sample to be tested, and the preprocessing operation is used to obtain the first key feature corresponding to the first preprocessing feature of the sample to be tested; The first key feature includes the double bond position, cis-trans configuration of fatty acids, and carbon chain length information; S3. Obtain the second preprocessing feature: Obtain the second preprocessing feature corresponding to the first key feature. The second preprocessing feature is to collect spectral data for the first key feature using a high-resolution spectrometer, and the collection method uses multimodal spectroscopy technology; S4. Obtain the second key feature: Obtain a spectral correction model, and according to the spectral correction model, obtain the second key feature corresponding to the second preprocessing feature through the second preprocessing feature; The obtaining of the spectral correction model in S4 specifically includes: According to the second preprocessing feature, perform a correction operation on the second preprocessing feature to obtain the second key feature corresponding to the second preprocessing feature. The second key feature is the target correction spectral feature corresponding to the second preprocessing feature, and the target correction spectral feature includes the chemical bond vibration frequency, the change range of molecular polarizability, and the change mode of fluorescence emission intensity; In a preset spectral database, obtain the spectral correction model corresponding to the key feature. The preset spectral database is used to store the correspondence between the key feature and the spectral correction model; The obtaining of the second key feature corresponding to the second preprocessing feature in S4 specifically includes: Obtain a correction feature correlation value, where the correction feature correlation value is the correlation degree between the second preprocessing feature and the target correction spectral feature, so as to judge whether the correlation degree reaches a preset threshold; If the correlation degree reaches the preset threshold, confirm that the target correction spectral feature is the second key feature corresponding to the second preprocessing feature; S5. Obtain the third key feature: According to chemometric analysis, obtain the third key feature corresponding to the second key feature through the second key feature; The obtaining steps of the third key feature of the sample to be tested in S5 include: C1: Detect the second key feature with the help of chemometric tools; C2: Use a variety of chemometric methods to calculate the degree of hydrogenation in the second key feature; C3: Use machine learning algorithms to perform pattern recognition and classification on the second key feature to determine the degree of hydrogenation of fatty acids; C4: Compare the physical properties and chemical properties of the second key feature in the sample to be tested, and after verification, form the third key feature; S6. Obtain a comprehensive fatty acid hydrogenation evaluation model: Obtain a comprehensive fatty acid hydrogenation evaluation model, and according to the comprehensive fatty acid hydrogenation evaluation model, obtain the analysis result of the third key feature through the third key feature; S7. Display a comprehensive fatty acid hydrogenation evaluation report: According to the comprehensive fatty acid hydrogenation evaluation model, through the third key feature, send the degree of fatty acid hydrogenation corresponding to the third key feature to the user in a preset display manner according to the comprehensive analysis result.

2. The method for detecting the degree of hydrogenation of fatty acids based on spectral analysis according to claim 1, wherein: The obtaining steps of the first preprocessing feature of the sample to be tested in S1 include: A1: Record the basic information of each sample to be tested in the set of samples to be tested. The basic information includes the name of the sample to be tested, the source of the sample to be tested, the batch number of the sample to be tested, and the storage conditions of the sample to be tested. A2: Measure the physical properties of each sample to be tested in the set of samples to be tested. The physical properties include the color of the sample to be tested, the transparency of the sample to be tested, and the texture of the sample to be tested. A3: Analyze the chemical properties of each sample to be tested in the set of samples to be tested. The chemical properties include the pH value of the sample to be tested, the moisture content of the sample to be tested, and the fatty acid composition of the sample to be tested. A4: Uniquely mark the original characteristics of each sample to be tested in the set of samples to be tested. Use numbers to mark each sample to be tested in the set of samples to be tested, and associate the marking information of each sample to be tested with the original characteristics of each sample to be tested to form the first preprocessing characteristics.

3. The method for detecting the degree of hydrogenation of fatty acids based on spectral analysis according to claim 1, wherein: The preprocessing steps of the first preprocessing characteristics of the sample to be tested in S2 include: B1: Solvent optimization: According to the physical and chemical properties of the sample in the first preprocessing characteristics of the sample to be tested, select a solvent to maximize the purity of fatty acids extracted from the sample to be tested. B2: Sample purification: The sample to be tested passes through solvent borrowing purification technology and homogenization technology to remove impurities and interfering substances in the sample to be tested, and obtain a pure fatty acid extract in the sample to be tested. B3: Sample concentration: The pure fatty acid extract in the sample to be tested passes through concentration technology to reduce the solvent volume, so as to increase the concentration of fatty acids and obtain a concentrated fatty acid sample of the sample to be tested. B4: Obtain the first key feature: Analyze the concentrated fatty acid sample of the sample to be tested to obtain the first key feature related to the degree of fatty acid hydrogenation.

4. The fatty acid hydrogenation degree detection method based on spectral analysis according to claim 1, characterized in that: In S4, before obtaining the spectral correction model and obtaining the target correction spectral feature corresponding to the second key feature according to the spectral correction model through the second key feature, the construction of the spectral correction model specifically includes: Obtain the spectral correction set corresponding to the target correction spectral feature; According to the spectral correction set, obtain the characteristic parameters and characteristic patterns corresponding to the target correction spectral feature; Construct a spectral correction model according to the characteristic parameters and characteristic patterns.

5. A method for detecting the degree of hydrogenation of fatty acids based on spectral analysis according to claim 1, characterized in that: The obtaining of the spectral correction set in S4 includes feature recognition technology, separation signal algorithm, signal enhancement technology, and resolution optimization.

6. A fatty acid hydrogenation degree detection system based on spectral analysis, comprising a system operation database, a system central processing module, and a user information section. According to the fatty acid hydrogenation degree detection method based on spectral analysis described in any one of claims 1-5 above, it is characterized in that It also includes: The system operation database includes all data texts of the fatty acid hydrogenation degree detection system, and real-time collects the information texts output by each module. The system central processing module is used to control the information text instructions output by each module in the control system, and the user information terminal is an information output device for receiving the fatty acid hydrogenation degree detection system. First preprocessing feature acquisition module: Based on the analysis requirements of the sample, record the sample to be tested as the sample to be tested, and obtain the first preprocessing characteristics of the sample to be tested. First preprocessing feature preprocessing module: Perform preprocessing operations on the first preprocessing characteristics of the sample to be tested. The preprocessing operations are used to obtain the first key feature corresponding to the first preprocessing characteristics of the sample to be tested. Second preprocessing feature acquisition module: Acquire the second preprocessing feature corresponding to the first key feature. The second preprocessing feature is to collect spectral data for the first key feature using a high-resolution spectrometer, and the acquisition method uses multimodal spectroscopy technology; Second key feature acquisition module: Acquire a spectral correction model, and according to the spectral correction model and through the second preprocessing feature, acquire the second key feature corresponding to the second preprocessing feature; Third key feature acquisition module: According to chemometric analysis and through the second key feature, acquire the third key feature corresponding to the second key feature; Comprehensive fatty acid hydrogenation evaluation model acquisition module: Acquire a comprehensive fatty acid hydrogenation evaluation model, and according to the comprehensive fatty acid hydrogenation evaluation model and through the third key feature, acquire the analysis result of the third key feature; Comprehensive fatty acid hydrogenation evaluation report output module: According to the comprehensive fatty acid hydrogenation evaluation model, through the third key feature, send the degree of fatty acid hydrogenation corresponding to the third key feature to the user in a preset display manner based on the result of comprehensive analysis.

7. The fatty acid hydrogenation degree detection system based on spectral analysis according to claim 6, wherein: The acquisition steps in the second key feature acquisition module are as follows: According to the second preprocessing feature, perform a correction operation on the second preprocessing feature to acquire the second key feature corresponding to the second preprocessing feature. The second key feature is the target correction spectral feature corresponding to the second preprocessing feature, and the target correction spectral feature includes the change range of chemical bond vibration frequency, molecular polarizability, and the change pattern of fluorescence emission intensity; In the preset spectral database, acquire the spectral correction model corresponding to the key feature. The preset spectral database is used to store the correspondence between the key feature and the spectral correction model; Acquire the correction feature correlation value, which is the correlation degree between the second preprocessing feature and the target correction spectral feature, to determine whether the correlation degree reaches the preset threshold; If the correlation degree reaches the preset threshold, confirm that the target correction spectral feature is the second key feature corresponding to the second preprocessing feature; Acquire the spectral correction set corresponding to the target correction spectral feature. According to the spectral correction set, the spectral correction set includes feature recognition technology, separation signal algorithm, signal enhancement technology, and resolution optimization; Acquire the feature parameters and feature patterns corresponding to the target correction spectral feature; Construct a spectral correction model according to the feature parameters and feature patterns.

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