Quantitative analysis method of potato amino acid liquid chromatography-tandem mass spectrometry

Abstract

The invention discloses a liquid chromatography-tandem mass spectrometry quantitative analysis method for potato amino acid, and relates to the technical field of analysis and detection.The method comprises the following steps that an amino acid sample in potatoes and chemical property data of the amino acid sample are collected, and the obtained data are preprocessed; the method comprises the following steps: acquiring specific rotation of an amino acid sample solution in potatoes by combining a linear regression algorithm based on an amino acid sample in preprocessed potatoes and chemical property data of the amino acid sample, dividing types of the amino acid sample solution through historical data of the amino acid sample in the potatoes, and determining the specific rotation of the amino acid sample solution in the potatoes according to the types of the amino acid sample solution in the potatoes. According to the method, a linear regression algorithm, a convolutional neural network algorithm, a neural network algorithm and other technologies in the method are closely combined with modern information technologies, a liquid chromatographic column distribution result is obtained, and a liquid chromatographic column distribution model is constructed according to the liquid chromatographic column distribution result. And the intelligent degree in the quantitative analysis process of the potato amino acid liquid chromatography-tandem mass spectrometry is obviously enhanced.

Description

Technical Field

[0001] The invention relates to the technical field of data fusion and information processing, in particular to a quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry. Background Art

[0002] In the field of quantitative analysis of potato amino acids by liquid chromatography-tandem mass spectrometry, accurate analysis of potato amino acids is extremely important. Traditional amino acid analysis methods have the limitations of low sensitivity, poor resolution, and inability to simultaneously determine multiple amino acids, making it difficult to meet the needs of in-depth research on potato amino acids. Although liquid chromatography technology can achieve amino acid separation, its qualitative ability is limited when used alone. Mass spectrometry technology, with its high sensitivity, can accurately determine the molecular weight and structural information of compounds. Liquid chromatography and mass spectrometry are connected in series, integrating the advantages of both, providing technical support for potato amino acid research. Therefore, a quantitative analysis method of potato amino acids by liquid chromatography-tandem mass spectrometry came into being. Although the prior art has made great progress in the quantitative analysis of potato amino acids by liquid chromatography-tandem mass spectrometry, there are still some problems that need to be optimized. In the process of quantitative analysis of potato amino acids by liquid chromatography-tandem mass spectrometry, potatoes contain a variety of amino acids with similar chemical properties, which have similar retention behaviors on liquid chromatography columns, resulting in overlapping chromatographic peaks, affecting the quantitative analysis results of potato amino acids by liquid chromatography-tandem mass spectrometry. Summary of the invention

[0003] In view of the deficiencies in the prior art, the present invention provides a quantitative analysis method for potato amino acids by liquid chromatography-tandem mass spectrometry, which solves the problems in the above-mentioned background technology.

[0004] To achieve the above object, the present invention is implemented by the following technical scheme: a quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry, comprising the following steps: Step 1: obtaining amino acid samples and their chemical property data in potatoes, and preprocessing the obtained data to provide data support for subsequent steps; Step 2: Based on the obtained optical rotation angle of the amino acid sample solution in potatoes, combined with a linear regression algorithm, the specific optical rotation of the amino acid sample solution in potatoes is obtained; Step 3: Determine the classification criteria of amino acid sample solutions through historical data of amino acid samples in potatoes, and provide data support for building a classification model of amino acid sample solutions; Step 4: Based on the classification criteria of the amino acid sample solution types, combined with the convolutional neural network algorithm, a model for classifying the amino acid sample solution types is constructed to obtain the types of the amino acid sample solution in potatoes; Step 5: According to the types of amino acid sample solutions in potatoes, the liquid chromatography column distribution results are obtained, and a liquid chromatography column distribution model is constructed using the liquid chromatography column distribution results, so as to solve the problem that potatoes contain multiple amino acids with similar chemical properties, resulting in overlapping chromatographic peaks and affecting the quantitative analysis results of potato amino acids by liquid chromatography tandem mass spectrometry; Step 6: Combine the output results of the liquid chromatography column distribution model to perform quantitative analysis on potato amino acids by liquid chromatography tandem mass spectrometry.

[0005] A further improvement of the technical solution of the present invention is that in step 1, the process of obtaining the amino acid sample and the chemical property data of the amino acid sample includes: Deploy different collection equipment to obtain amino acid samples and chemical property data in potatoes, wherein the collection equipment includes filter paper, centrifuge tubes, ultrasonic extractors, solid phase extraction columns, nitrogen blowers, sample tubes, high performance liquid chromatographs, pH meters, and polarimeters; The amino acid sample chemical property data include water delivery parameters, pH value and optical rotation angle of the amino acid sample solution in potatoes; Select potatoes, rinse the potatoes with ionized water, dry the potatoes with filter paper, peel the potatoes, cut them into small pieces, freeze them in liquid nitrogen, obtain frozen potatoes, weigh frozen potato samples, place the frozen potato samples in a centrifuge tube filled with an extraction solvent, place the centrifuge tube in an ultrasonic extractor, release amino acids in the frozen potato samples, obtain a potato sample extraction solution, centrifuge the potato sample extraction solution, absorb the supernatant in the potato sample extraction solution after centrifugation, activate a solid phase extraction column with methanol and water in sequence, pass the supernatant through the activated solid phase extraction column, remove the initial effluent, obtain an amino acid solution, elute the amino acid solution with an organic solvent eluent, collect the eluent, dry the collected eluent on a nitrogen blower, and obtain an amino acid sample in the potato; The amino acid sample in potato is processed to obtain a sample solution of the amino acid in potato, and the water transport parameters of the amino acid sample solution in potato are collected based on the distribution coefficient of different amino acids between the stationary phase and the mobile phase using a high performance liquid chromatograph; the pH value of the amino acid sample solution in potato is collected using a pH meter; and the optical rotation angle of the amino acid sample solution in potato is collected using a polarimeter.

[0006] A further improvement of the technical solution of the present invention is that in step 1, the process of preprocessing the collected data includes: The water transmission parameters, pH value and optical rotation angle of the collected amino acid sample solutions in potatoes were cleaned, and the average values ​​of the water transmission parameters, pH value and optical rotation angle of the amino acid sample solutions in potatoes were calculated respectively. The corresponding calculated average values ​​were used to fill the missing values ​​in the water transmission parameters, pH value and optical rotation angle of the collected amino acid sample solutions.

[0007] A further improvement of the technical solution of the present invention is that in step 2, the process of obtaining the specific rotation of the amino acid sample solution in potatoes includes: The optical rotation angle of the amino acid sample solution in potato is taken as the first data set, and is divided into a training set and a test set in a ratio of 8:2. The amino acid specific optical rotation model of the first data set is taken as input, and the specific optical rotation of the amino acid sample solution in potato is taken as output. The training set data and the linear regression algorithm are used to adjust the intercept term and the regression coefficient of the optical rotation angle of the amino acid sample solution in potato, learn the linear relationship between the optical rotation angle of the amino acid sample solution in potato and the specific optical rotation of the amino acid sample solution in potato, and train the amino acid specific optical rotation model; Input the test set data into the amino acid specific optical rotation model, evaluate the performance of the amino acid specific optical rotation model, adjust the parameters of the amino acid specific optical rotation model, optimize the amino acid specific optical rotation model, deploy the optimized amino acid specific optical rotation model into the system, and obtain the final amino acid specific optical rotation model; The optical rotation angle of the amino acid sample solution in potato is input into the amino acid specific optical rotation model to obtain the specific optical rotation of the amino acid sample solution in potato.

[0008] A further improvement of the technical solution of the present invention is that in step 3, the process of determining the classification standard of the amino acid sample solution types includes: Obtaining historical data of amino acid samples in potatoes by searching PubMed database, wherein the historical data of amino acid samples in potatoes include historical water delivery parameters, historical pH values ​​and historical specific rotations of amino acid sample solutions in potatoes; Setting a water transfer parameter threshold of the amino acid sample solution in potatoes, classifying the amino acid sample solution in potatoes with a historical water transfer parameter higher than the water transfer parameter threshold as a hydrophobic amino acid sample solution, and classifying the amino acid sample solution in potatoes with a historical water transfer parameter lower than the water transfer parameter threshold as a non-hydrophobic amino acid sample solution, and obtaining a hydrophobicity classification result of the amino acid sample solution; The amino acid sample solution in potatoes with a historical pH value less than 5 is classified as an acidic amino acid sample solution, the amino acid sample solution in potatoes with a historical pH value between 5 and 7 is classified as a neutral amino acid sample solution, and the amino acid sample solution in potatoes with a historical pH value greater than 7 is classified as an alkaline amino acid sample solution, and the acidity and alkalinity classification results of the amino acid sample solution are obtained; The amino acid sample solution in potato with a historical specific optical rotation of 0 is divided into a non-chiral amino acid sample solution, and the amino acid sample solution in potato with a historical specific optical rotation not being 0 is divided into a chiral amino acid sample solution, and the optical rotation division result of the amino acid sample solution is obtained.

[0009] A further improvement of the technical solution of the present invention is that in step 4, the process of constructing a classification model of amino acid sample solutions and then obtaining the types of amino acid sample solutions in potatoes includes: The water transport parameters of the amino acid sample solutions in potatoes and the corresponding hydrophobicity classification standards of the amino acid sample solutions, the pH values ​​of the amino acid sample solutions in potatoes and the corresponding acidity and alkalinity classification standards of the amino acid sample solutions, and the specific optical rotation of the amino acid sample solutions in potatoes and the corresponding optical rotation classification standards of the amino acid sample solutions are used as the second data set, and divided into a training set and a test set in a ratio of 8:2; Using the training set data and the convolutional neural network algorithm, a convolutional neural network architecture is designed, the water delivery parameters of the amino acid sample solution in potatoes, the pH value of the amino acid sample solution in potatoes, and the specific optical rotation of the amino acid sample solution in potatoes are taken as input, and the hydrophobicity division results of the amino acid sample solution, the acid-base division results of the amino acid sample solution, and the optical rotation division results of the amino acid sample solution are taken as output, the convolutional neural network output value is calculated by forward propagation, the gradient is calculated by back propagation, and the convolutional neural network model parameters are updated according to the gradient, the nonlinear relationship between the water delivery parameters of the amino acid sample solution in potatoes and the hydrophobicity division results of the corresponding amino acid sample solution, the nonlinear relationship between the pH value of the amino acid sample solution in potatoes and the acid-base division results of the corresponding amino acid sample solution, and the nonlinear relationship between the specific optical rotation of the amino acid sample solution in potatoes and the optical rotation division results of the corresponding amino acid sample solution are learned, and the amino acid sample solution type division model is trained; Input the test set data into the amino acid sample solution type classification model, compare the output results of the amino acid sample solution type classification model with the types of amino acid sample solutions in actual potatoes, evaluate the performance of the amino acid sample solution type classification model, adjust the parameters of the amino acid sample solution type classification model, optimize the amino acid sample solution type classification model, deploy the optimized amino acid sample solution type classification model to the system, and obtain the final amino acid sample solution type classification model; The water delivery parameters of the amino acid sample solution in potatoes, the pH value of the amino acid sample solution in potatoes and the specific rotation of the amino acid sample solution in potatoes are input into the amino acid sample solution type classification model, and the amino acid sample solution type classification model is used to output the corresponding amino acid sample solution type in potatoes.

[0010] A further improvement of the technical solution of the present invention is that in step 5, the process of obtaining the liquid chromatography column distribution result includes: For the hydrophobicity partition result of the amino acid sample solution, when the hydrophobicity partition result of the amino acid sample solution is a hydrophobic amino acid sample solution, a reverse phase liquid chromatography column is assigned; when the hydrophobicity partition result of the amino acid sample solution is a non-hydrophobic amino acid sample solution, a normal phase liquid chromatography column is assigned to obtain a hydrophobic liquid chromatography column assignment result; For the acid-base partition result of the amino acid sample solution, when the acid-base partition result of the amino acid sample solution is an acidic amino acid sample solution, a strong cation exchange liquid chromatography column is assigned; when the acid-base partition result of the amino acid sample solution is a neutral amino acid sample solution, a size exclusion liquid chromatography column is assigned; when the acid-base partition result of the amino acid sample solution is an alkaline amino acid sample solution, a strong anion exchange liquid chromatography column is assigned to obtain the acid-base liquid chromatography column assignment result; For the optical rotation partition result of the amino acid sample solution, when the optical rotation partition result of the amino acid sample solution is a chiral amino acid sample solution, a chiral liquid chromatography column is assigned; when the optical rotation partition result of the amino acid sample solution is an achiral amino acid sample solution, a reversed phase liquid chromatography column is assigned to obtain an optical rotation liquid chromatography column assignment result.

[0011] A further improvement of the technical solution of the present invention is that in step 5, the process of constructing the liquid chromatography column distribution model includes: The hydrophobicity partition results of the amino acid sample solutions and their corresponding hydrophobicity liquid chromatography column distribution results, the acidity and alkalinity partition results of the amino acid sample solutions and their corresponding acidity and alkalinity liquid chromatography column distribution results, and the optical rotation partition results of the amino acid sample solutions and their corresponding optical rotation liquid chromatography column distribution results are used as the third data set, and divided into a training set and a test set in a ratio of 7:3; Combine the training set data with the neural network algorithm, take the hydrophobicity partition results of the amino acid sample solution, the acid-base partition results of the amino acid sample solution and the optical rotation partition results of the amino acid sample solution as input, take the hydrophobic liquid chromatography column distribution results, the acid-base liquid chromatography column distribution results and the optical rotation liquid chromatography column distribution results as output, combine forward propagation and back propagation, learn the nonlinear relationship between the hydrophobicity partition results of the amino acid sample solution and its corresponding hydrophobic liquid chromatography column distribution results, the nonlinear relationship between the acid-base partition results of the amino acid sample solution and its corresponding acid-base liquid chromatography column distribution results, and the nonlinear relationship between the optical rotation partition results of the amino acid sample solution and its corresponding optical rotation liquid chromatography column distribution results, and obtain a trained liquid chromatography column distribution model; The test set data is input into the trained liquid chromatography column allocation model. The MSE function is used to evaluate the error between the output result of the liquid chromatography column allocation model and the actual liquid chromatography column allocation result. According to the evaluation result, the parameters of the liquid chromatography column allocation model are adjusted to optimize the performance of the liquid chromatography column allocation model. The optimized liquid chromatography column allocation model is deployed into the system to obtain the liquid chromatography column allocation model.

[0012] A further improvement of the technical solution of the present invention is that in step 5 and step 6, the process of obtaining the liquid chromatography column distribution result includes: Inputting the hydrophobicity partition result, the acidity and alkalinity partition result and the optical rotation partition result of the amino acid sample solution into the liquid chromatography column partition model, the liquid chromatography column partition model outputs the hydrophobicity liquid chromatography column partition result, the acidity and alkalinity liquid chromatography column partition result and the optical rotation liquid chromatography column partition result respectively; When the liquid chromatography column allocation model outputs a single liquid chromatography column allocation result, the liquid chromatography column corresponding to the single liquid chromatography column allocation result is allocated to the corresponding amino acid sample solution in potatoes; When the liquid chromatography column allocation model outputs more than one liquid chromatography column allocation result, it indicates that the amino acid sample solution contains more than one chemical property at the same time. According to the liquid chromatography column allocation results output by the liquid chromatography column allocation model, the corresponding liquid chromatography columns are allocated in sequence to separate the amino acid sample solution in potatoes.

[0013] A further improvement of the technical solution of the present invention is that in step 6, the process of quantitatively analyzing potato amino acids by liquid chromatography tandem mass spectrometry includes: S1. Using an amino acid sample extracted from a potato, an amino acid sample solution of different concentrations is prepared, wherein the amino acid sample solution includes a target amino acid and an internal standard, the concentration of the amino acid sample solution is collected using a fully automatic amino acid analyzer, the chemical properties of the amino acid sample solution are detected, and a liquid chromatography column is allocated to the amino acid sample solution according to the chemical properties of the amino acid sample solution, the temperature of the liquid chromatography column is set to 30 degrees Celsius, a gradient is set according to the separation requirements of the amino acids, and amino acids of different chemical properties in the amino acid sample solution are separated; S2. Using an electrospray ion source in a positive ion mode, respectively set the spray voltage, capillary temperature, sheath gas flow rate, and auxiliary gas flow rate, determine the parent ion and characteristic daughter ion of each amino acid with different chemical properties, set the corresponding monitoring ion pairs and collision energy, and convert the amino acid solutions with different chemical properties flowing out of the liquid chromatography column into gaseous ions; S3. Using a liquid chromatography tandem mass spectrometer to receive gaseous ions generated from an electrospray ion source, separating gaseous ions of different mass-to-charge ratios according to the motion characteristics of gaseous ions in electric and magnetic fields, detecting the separated gaseous ions, and recording the peak areas of the target amino acid and the internal standard at each amino acid sample solution concentration; S4. Draw a standard curve with the concentration of the amino acid sample solution as the horizontal axis and the peak area of ​​the target amino acid and the internal standard as the vertical axis, and obtain the equation and correlation coefficient of the standard curve through linear regression analysis; S5. Calculate the concentration of the amino acid in the amino acid sample solution based on the peak areas of the target amino acid and the internal standard and the equation of the standard curve.

[0014] The beneficial effects of the present invention are as follows: a quantitative analysis method of potato amino acids by liquid chromatography-tandem mass spectrometry. Compared with the traditional quantitative analysis method of potato amino acids by liquid chromatography-tandem mass spectrometry, the linear regression algorithm, convolutional neural network algorithm and neural network algorithm in the method of the present invention are closely combined with modern information technology. The chemical property data of the amino acid sample is accurately captured by a high performance liquid chromatograph, a pH meter and a polarimeter. The specific rotation of the amino acid sample solution in the potato is obtained by using a linear regression algorithm. The type classification model of the amino acid sample solution is constructed by using a convolutional neural network algorithm to obtain the type of the amino acid sample solution in the potato, and then the liquid chromatography column distribution result is obtained. The neural network algorithm is used to A liquid chromatography column distribution model is constructed, and a liquid chromatography column is distributed to an amino acid sample solution in a potato through the liquid chromatography column distribution model, thereby achieving the monitoring of the chemical properties of the amino acid sample, solving the problem that a plurality of amino acids with similar chemical properties in a potato have similar retention behaviors on a liquid chromatography column, resulting in overlapping chromatographic peaks and affecting the quantitative analysis results, ensuring that the method of the present invention can refine the dynamic monitoring standard for a quantitative analysis method for potato amino acids by liquid chromatography-tandem mass spectrometry within a more precise range, making the monitored data a more accurate indicator under the same conditions, and the development and application of this method significantly enhance the degree of intelligence in the process of the quantitative analysis method for potato amino acids by liquid chromatography-tandem mass spectrometry. BRIEF DESCRIPTION OF THE DRAWINGS

[0015] Figure 1 The present invention is a flowchart of a quantitative analysis method of potato amino acids by liquid chromatography-tandem mass spectrometry according to an embodiment of the present application. DETAILED DESCRIPTION

[0016] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0017] like Figure 1 As shown, the present invention provides a technical solution: a quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry, which consists of the following steps: Step 1: obtaining amino acid samples and their chemical property data in potatoes, and preprocessing the obtained data to provide data support for subsequent steps; Step 2: Based on the obtained optical rotation angle of the amino acid sample solution in potatoes, combined with a linear regression algorithm, the specific optical rotation of the amino acid sample solution in potatoes is obtained; Step 3: Determine the classification criteria of amino acid sample solutions through historical data of amino acid samples in potatoes, and provide data support for building a classification model of amino acid sample solutions; Step 4: Based on the classification criteria of the amino acid sample solution types, combined with the convolutional neural network algorithm, a model for classifying the amino acid sample solution types is constructed to obtain the types of the amino acid sample solution in potatoes; Step 5: According to the types of amino acid sample solutions in potatoes, the liquid chromatography column distribution results are obtained, and a liquid chromatography column distribution model is constructed using the liquid chromatography column distribution results, so as to solve the problem that potatoes contain multiple amino acids with similar chemical properties, resulting in overlapping chromatographic peaks and affecting the quantitative analysis results of potato amino acids by liquid chromatography tandem mass spectrometry; Step 6: Combine the output results of the liquid chromatography column distribution model to perform quantitative analysis on potato amino acids by liquid chromatography tandem mass spectrometry.

[0018] Preferably, in step 1, the process of acquiring the amino acid sample and the chemical property data of the amino acid sample includes: Deploy different collection equipment to obtain amino acid samples and chemical property data in potatoes, wherein the collection equipment includes filter paper, centrifuge tubes, ultrasonic extractors, solid phase extraction columns, nitrogen blowers, sample tubes, high performance liquid chromatographs, pH meters, and polarimeters; The amino acid sample chemical property data include water delivery parameters, pH value and optical rotation angle of the amino acid sample solution in potatoes; Select potatoes, rinse the potatoes with ionized water, dry the potatoes with filter paper, peel the potatoes, cut them into small pieces, freeze them in liquid nitrogen, obtain frozen potatoes, weigh frozen potato samples, place the frozen potato samples in a centrifuge tube filled with an extraction solvent, place the centrifuge tube in an ultrasonic extractor, release amino acids in the frozen potato samples, obtain a potato sample extraction solution, centrifuge the potato sample extraction solution, absorb the supernatant in the potato sample extraction solution after centrifugation, activate a solid phase extraction column with methanol and water in sequence, pass the supernatant through the activated solid phase extraction column, remove the initial effluent, obtain an amino acid solution, elute the amino acid solution with an organic solvent eluent, collect the eluent, dry the collected eluent on a nitrogen blower, and obtain an amino acid sample in the potato; The amino acid sample collected from potatoes is placed in a sample tube, clean water is added to the sample tube, and the mixture is stirred to obtain a sample solution of the amino acids in potatoes. The water transport parameters of the amino acid sample solution in potatoes are collected based on the distribution coefficient of different amino acids between the stationary phase and the mobile phase using a high performance liquid chromatograph; the pH value of the amino acid sample solution in potatoes is collected using a pH meter; and the optical rotation angle of the amino acid sample solution in potatoes is collected using a polarimeter.

[0019] Preferably, in step 1, the process of preprocessing the collected data includes: The water transmission parameters, pH value and optical rotation angle of the collected amino acid sample solutions in potatoes were cleaned, and the average values ​​of the water transmission parameters, pH value and optical rotation angle of the amino acid sample solutions in potatoes were calculated respectively. The corresponding calculated average values ​​were used to fill the missing values ​​in the water transmission parameters, pH value and optical rotation angle of the collected amino acid sample solutions.

[0020] Preferably, in step 2, the process of obtaining the specific rotation of the amino acid sample solution in potatoes comprises: The optical rotation angle of the amino acid sample solution in potato is taken as the first data set, and is divided into a training set and a test set in a ratio of 8:2. The amino acid specific optical rotation model of the first data set is taken as input, and the specific optical rotation of the amino acid sample solution in potato is taken as output. The training set data and the linear regression algorithm are used to adjust the intercept term and the regression coefficient of the optical rotation angle of the amino acid sample solution in potato, learn the linear relationship between the optical rotation angle of the amino acid sample solution in potato and the specific optical rotation of the amino acid sample solution in potato, and train the amino acid specific optical rotation model; Input the test set data into the amino acid specific optical rotation model, evaluate the performance of the amino acid specific optical rotation model, adjust the parameters of the amino acid specific optical rotation model, optimize the amino acid specific optical rotation model, deploy the optimized amino acid specific optical rotation model into the system, and obtain the final amino acid specific optical rotation model; The optical rotation angle of the amino acid sample solution in potato is input into the amino acid specific optical rotation model to obtain the specific optical rotation of the amino acid sample solution in potato.

[0021] Preferably, in step 3, the process of determining the classification criteria of the amino acid sample solutions includes: Obtaining historical data of amino acid samples in potatoes by searching PubMed database, wherein the historical data of amino acid samples in potatoes include historical water delivery parameters, historical pH values ​​and historical specific rotations of amino acid sample solutions in potatoes; Setting a water transfer parameter threshold of the amino acid sample solution in potatoes, classifying the amino acid sample solution in potatoes with a historical water transfer parameter higher than the water transfer parameter threshold as a hydrophobic amino acid sample solution, and classifying the amino acid sample solution in potatoes with a historical water transfer parameter lower than the water transfer parameter threshold as a non-hydrophobic amino acid sample solution, and obtaining a hydrophobicity classification result of the amino acid sample solution; The amino acid sample solution in potatoes with a historical pH value less than 5 is classified as an acidic amino acid sample solution, the amino acid sample solution in potatoes with a historical pH value between 5 and 7 is classified as a neutral amino acid sample solution, and the amino acid sample solution in potatoes with a historical pH value greater than 7 is classified as an alkaline amino acid sample solution, and the acidity and alkalinity classification results of the amino acid sample solution are obtained; The amino acid sample solution in potato with a historical specific optical rotation of 0 is divided into a non-chiral amino acid sample solution, and the amino acid sample solution in potato with a historical specific optical rotation not being 0 is divided into a chiral amino acid sample solution, and the optical rotation division result of the amino acid sample solution is obtained.

[0022] Preferably, in step 4, the process of constructing a classification model of amino acid sample solutions to obtain the types of amino acid sample solutions in potatoes includes: The water transport parameters of the amino acid sample solutions in potatoes and the corresponding hydrophobicity classification standards of the amino acid sample solutions, the pH values ​​of the amino acid sample solutions in potatoes and the corresponding acidity and alkalinity classification standards of the amino acid sample solutions, and the specific optical rotation of the amino acid sample solutions in potatoes and the corresponding optical rotation classification standards of the amino acid sample solutions are used as the second data set, and divided into a training set and a test set in a ratio of 8:2; Using the training set data and the convolutional neural network algorithm, a convolutional neural network architecture is designed, the water delivery parameters of the amino acid sample solution in potatoes, the pH value of the amino acid sample solution in potatoes, and the specific optical rotation of the amino acid sample solution in potatoes are taken as input, and the hydrophobicity division results of the amino acid sample solution, the acid-base division results of the amino acid sample solution, and the optical rotation division results of the amino acid sample solution are taken as output, the convolutional neural network output value is calculated by forward propagation, the gradient is calculated by back propagation, and the convolutional neural network model parameters are updated according to the gradient, the nonlinear relationship between the water delivery parameters of the amino acid sample solution in potatoes and the hydrophobicity division results of the corresponding amino acid sample solution, the nonlinear relationship between the pH value of the amino acid sample solution in potatoes and the acid-base division results of the corresponding amino acid sample solution, and the nonlinear relationship between the specific optical rotation of the amino acid sample solution in potatoes and the optical rotation division results of the corresponding amino acid sample solution are learned, and the amino acid sample solution type division model is trained; Input the test set data into the amino acid sample solution type classification model, compare the output results of the amino acid sample solution type classification model with the types of amino acid sample solutions in actual potatoes, evaluate the performance of the amino acid sample solution type classification model, adjust the parameters of the amino acid sample solution type classification model, optimize the amino acid sample solution type classification model, deploy the optimized amino acid sample solution type classification model to the system, and obtain the final amino acid sample solution type classification model; The water delivery parameters of the amino acid sample solution in potatoes, the pH value of the amino acid sample solution in potatoes and the specific rotation of the amino acid sample solution in potatoes are input into the amino acid sample solution type classification model, and the amino acid sample solution type classification model is used to output the corresponding amino acid sample solution type in potatoes.

[0023] Preferably, in step 5, the process of obtaining the liquid chromatography column distribution result includes: For the hydrophobicity partition result of the amino acid sample solution, when the hydrophobicity partition result of the amino acid sample solution is a hydrophobic amino acid sample solution, a reverse phase liquid chromatography column is assigned; when the hydrophobicity partition result of the amino acid sample solution is a non-hydrophobic amino acid sample solution, a normal phase liquid chromatography column is assigned to obtain a hydrophobic liquid chromatography column assignment result; For the acid-base partition result of the amino acid sample solution, when the acid-base partition result of the amino acid sample solution is an acidic amino acid sample solution, a strong cation exchange liquid chromatography column is assigned; when the acid-base partition result of the amino acid sample solution is a neutral amino acid sample solution, a size exclusion liquid chromatography column is assigned; when the acid-base partition result of the amino acid sample solution is an alkaline amino acid sample solution, a strong anion exchange liquid chromatography column is assigned to obtain the acid-base liquid chromatography column assignment result; For the optical rotation partition result of the amino acid sample solution, when the optical rotation partition result of the amino acid sample solution is a chiral amino acid sample solution, a chiral liquid chromatography column is assigned; when the optical rotation partition result of the amino acid sample solution is an achiral amino acid sample solution, a reversed phase liquid chromatography column is assigned to obtain an optical rotation liquid chromatography column assignment result.

[0024] Preferably, in step 5, the process of constructing the liquid chromatography column distribution model includes: The hydrophobicity partition results of the amino acid sample solutions and their corresponding hydrophobicity liquid chromatography column distribution results, the acidity and alkalinity partition results of the amino acid sample solutions and their corresponding acidity and alkalinity liquid chromatography column distribution results, and the optical rotation partition results of the amino acid sample solutions and their corresponding optical rotation liquid chromatography column distribution results are used as the third data set, and divided into a training set and a test set in a ratio of 7:3; Combine the training set data with the neural network algorithm, take the hydrophobicity partition results of the amino acid sample solution, the acid-base partition results of the amino acid sample solution and the optical rotation partition results of the amino acid sample solution as input, take the hydrophobic liquid chromatography column distribution results, the acid-base liquid chromatography column distribution results and the optical rotation liquid chromatography column distribution results as output, combine forward propagation and back propagation, learn the nonlinear relationship between the hydrophobicity partition results of the amino acid sample solution and its corresponding hydrophobic liquid chromatography column distribution results, the nonlinear relationship between the acid-base partition results of the amino acid sample solution and its corresponding acid-base liquid chromatography column distribution results, and the nonlinear relationship between the optical rotation partition results of the amino acid sample solution and its corresponding optical rotation liquid chromatography column distribution results, and obtain a trained liquid chromatography column distribution model; The test set data is input into the trained liquid chromatography column allocation model. The MSE function is used to evaluate the error between the output result of the liquid chromatography column allocation model and the actual liquid chromatography column allocation result. According to the evaluation result, the parameters of the liquid chromatography column allocation model are adjusted to optimize the performance of the liquid chromatography column allocation model. The optimized liquid chromatography column allocation model is deployed into the system to obtain the liquid chromatography column allocation model.

[0025] Preferably, in step six, the process of obtaining the liquid chromatography column distribution result includes: Inputting the hydrophobicity partition result, the acidity and alkalinity partition result and the optical rotation partition result of the amino acid sample solution into the liquid chromatography column partition model, the liquid chromatography column partition model outputs the hydrophobicity liquid chromatography column partition result, the acidity and alkalinity liquid chromatography column partition result and the optical rotation liquid chromatography column partition result respectively; When the liquid chromatography column allocation model outputs a single liquid chromatography column allocation result, the liquid chromatography column corresponding to the single liquid chromatography column allocation result is allocated to the corresponding amino acid sample solution in potatoes; When the liquid chromatography column allocation model outputs more than one liquid chromatography column allocation result, it indicates that the amino acid sample solution contains more than one chemical property at the same time. According to the liquid chromatography column allocation results output by the liquid chromatography column allocation model, the corresponding liquid chromatography columns are allocated in sequence to separate the amino acid sample solution in potatoes.

[0026] Preferably, in step six, the process of quantitatively analyzing potato amino acids by liquid chromatography tandem mass spectrometry comprises: S1. Using an amino acid sample extracted from a potato, an amino acid sample solution of different concentrations is prepared, wherein the amino acid sample solution includes a target amino acid and an internal standard, the concentration of the amino acid sample solution is collected using a fully automatic amino acid analyzer, the chemical properties of the amino acid sample solution are detected, and a liquid chromatography column is allocated to the amino acid sample solution according to the chemical properties of the amino acid sample solution, the temperature of the liquid chromatography column is set to 30 degrees Celsius, a gradient is set according to the separation requirements of the amino acids, and amino acids of different chemical properties in the amino acid sample solution are separated; S2. Using an electrospray ion source in a positive ion mode, respectively set the spray voltage, capillary temperature, sheath gas flow rate, and auxiliary gas flow rate, determine the parent ion and characteristic daughter ion of each amino acid with different chemical properties, set the corresponding monitoring ion pairs and collision energy, and convert the amino acid solutions with different chemical properties flowing out of the liquid chromatography column into gaseous ions; S3. Using a liquid chromatography tandem mass spectrometer to receive gaseous ions generated from an electrospray ion source, separating gaseous ions of different mass-to-charge ratios according to the motion characteristics of gaseous ions in electric and magnetic fields, detecting the separated gaseous ions, and recording the peak areas of the target amino acid and the internal standard at each amino acid sample solution concentration; S4. Draw a standard curve with the concentration of the amino acid sample solution as the horizontal axis and the peak area of ​​the target amino acid and the internal standard as the vertical axis, and obtain the equation and correlation coefficient of the standard curve through linear regression analysis; S5. Calculate the concentration of the amino acid in the amino acid sample solution based on the peak areas of the target amino acid and the internal standard and the equation of the standard curve.

[0027] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions. The sentence "includes an element defined by ... does not exclude the existence of other identical elements in the process, method, article or device including the element".

[0028] Although 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 present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A quantitative analysis method of potato amino acids by liquid chromatography-tandem mass spectrometry, characterized in that: The following steps are involved: Step 1, for obtaining amino acid samples and chemical property data in potatoes, and preprocessing the obtained data; Step 2: Based on the obtained optical rotation angle of the amino acid sample solution in potatoes, combined with a linear regression algorithm, the specific optical rotation of the amino acid sample solution in potatoes is obtained; Step 3, determining the classification standard of the amino acid sample solution types through the historical data of the amino acid samples in potatoes; Step 4: Based on the classification criteria of the amino acid sample solution types, combined with the convolutional neural network algorithm, a model for classifying the amino acid sample solution types is constructed to obtain the types of the amino acid sample solution in potatoes; Step 5: According to the type of the amino acid sample solution in the potato, a liquid chromatography column distribution result is obtained, and a liquid chromatography column distribution model is constructed using the liquid chromatography column distribution result; Step 6: Combine the output results of the liquid chromatography column distribution model to perform quantitative analysis on potato amino acids by liquid chromatography tandem mass spectrometry.

2. The quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry according to claim 1, characterized in that: In step 1, the process of obtaining the amino acid sample in potatoes and the data of its chemical properties includes: Deploy different collection equipment to obtain amino acid samples and their chemical property data in potatoes, including filter paper, centrifuge tubes, ultrasonic extractors, solid phase extraction columns, nitrogen blowers, sample tubes, high performance liquid chromatographs, pH meters and polarimeters; The amino acid sample chemical property data include water transfer parameters, pH value and optical rotation angle of the amino acid sample solution in potatoes.

3. The quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry according to claim 2, characterized in that: In step 1, the process of preprocessing the acquired data includes: The water transmission parameters, pH value and optical rotation angle of the collected amino acid sample solutions in potatoes were cleaned, and the average values ​​of the water transmission parameters, pH value and optical rotation angle of the amino acid sample solutions in potatoes were calculated respectively. The corresponding calculated average values ​​were used to fill the missing values ​​in the water transmission parameters, pH value and optical rotation angle of the collected amino acid sample solutions.

4. The quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry according to claim 3, characterized in that: In the step 2, the process of obtaining the specific rotation of the amino acid sample solution in potatoes includes: The optical rotation angle of the amino acid sample solution in potato is taken as the first data set, and is divided into a training set and a test set in a ratio of 8:

2. The amino acid specific optical rotation model of the first data set is taken as input, and the specific optical rotation of the amino acid sample solution in potato is taken as output. The training set data and the linear regression algorithm are used to adjust the intercept term and the regression coefficient of the optical rotation angle of the amino acid sample solution in potato, learn the linear relationship between the optical rotation angle of the amino acid sample solution in potato and the specific optical rotation of the amino acid sample solution in potato, and train the amino acid specific optical rotation model; Input the test set data into the amino acid specific optical rotation model, evaluate the performance of the amino acid specific optical rotation model, adjust the parameters of the amino acid specific optical rotation model, optimize the amino acid specific optical rotation model, deploy the optimized amino acid specific optical rotation model into the system, and obtain the final amino acid specific optical rotation model; The optical rotation angle of the amino acid sample solution in potato is input into the amino acid specific optical rotation model to obtain the specific optical rotation of the amino acid sample solution in potato.

5. The quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry according to claim 4, characterized in that: In step 3, the process of determining the classification standard of the amino acid sample solution types includes: Obtaining historical data of amino acid samples in potatoes by searching PubMed database, wherein the historical data of amino acid samples in potatoes include historical water delivery parameters, historical pH values ​​and historical specific rotations of amino acid sample solutions in potatoes; Setting a water transfer parameter threshold of the amino acid sample solution in potatoes, classifying the amino acid sample solution in potatoes with a historical water transfer parameter higher than the water transfer parameter threshold as a hydrophobic amino acid sample solution, and classifying the amino acid sample solution in potatoes with a historical water transfer parameter lower than the water transfer parameter threshold as a non-hydrophobic amino acid sample solution, and obtaining a hydrophobicity classification result of the amino acid sample solution; The amino acid sample solution in potatoes with a historical pH value less than 5 is classified as an acidic amino acid sample solution, the amino acid sample solution in potatoes with a historical pH value between 5 and 7 is classified as a neutral amino acid sample solution, and the amino acid sample solution in potatoes with a historical pH value greater than 7 is classified as an alkaline amino acid sample solution, and the acidity and alkalinity classification results of the amino acid sample solution are obtained; The amino acid sample solution in potato with a historical specific optical rotation of 0 is divided into a non-chiral amino acid sample solution, and the amino acid sample solution in potato with a historical specific optical rotation not being 0 is divided into a chiral amino acid sample solution, and the optical rotation division result of the amino acid sample solution is obtained.

6. The quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry according to claim 5, characterized in that: In step 4, the process of constructing a classification model of amino acid sample solutions and then obtaining the types of amino acid sample solutions in potatoes includes: The water transport parameters of the amino acid sample solutions in potatoes and the corresponding hydrophobicity classification standards of the amino acid sample solutions, the pH values ​​of the amino acid sample solutions in potatoes and the corresponding acidity and alkalinity classification standards of the amino acid sample solutions, and the specific optical rotation of the amino acid sample solutions in potatoes and the corresponding optical rotation classification standards of the amino acid sample solutions are used as the second data set, and divided into a training set and a test set in a ratio of 8:2; Using the training set data and the convolutional neural network algorithm, a convolutional neural network architecture is designed, the water delivery parameters of the amino acid sample solution in potatoes, the pH value of the amino acid sample solution in potatoes, and the specific optical rotation of the amino acid sample solution in potatoes are taken as input, and the hydrophobicity division results of the amino acid sample solution, the acid-base division results of the amino acid sample solution, and the optical rotation division results of the amino acid sample solution are taken as output, the convolutional neural network output value is calculated by forward propagation, the gradient is calculated by back propagation, and the convolutional neural network model parameters are updated according to the gradient, the nonlinear relationship between the water delivery parameters of the amino acid sample solution in potatoes and the hydrophobicity division results of the corresponding amino acid sample solution, the nonlinear relationship between the pH value of the amino acid sample solution in potatoes and the acid-base division results of the corresponding amino acid sample solution, and the nonlinear relationship between the specific optical rotation of the amino acid sample solution in potatoes and the optical rotation division results of the corresponding amino acid sample solution are learned, and the amino acid sample solution type division model is trained; Input the test set data into the amino acid sample solution type classification model, compare the output results of the amino acid sample solution type classification model with the types of amino acid sample solutions in actual potatoes, evaluate the performance of the amino acid sample solution type classification model, adjust the parameters of the amino acid sample solution type classification model, optimize the amino acid sample solution type classification model, deploy the optimized amino acid sample solution type classification model to the system, and obtain the final amino acid sample solution type classification model; The water delivery parameters of the amino acid sample solution in potatoes, the pH value of the amino acid sample solution in potatoes and the specific rotation of the amino acid sample solution in potatoes are input into the amino acid sample solution type classification model, and the amino acid sample solution type classification model is used to output the corresponding amino acid sample solution type in potatoes.

7. The quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry according to claim 6, characterized in that: In the step 5, the process of obtaining the liquid chromatography column distribution result includes: For the hydrophobicity partition result of the amino acid sample solution, when the hydrophobicity partition result of the amino acid sample solution is a hydrophobic amino acid sample solution, a reverse phase liquid chromatography column is assigned; when the hydrophobicity partition result of the amino acid sample solution is a non-hydrophobic amino acid sample solution, a normal phase liquid chromatography column is assigned to obtain a hydrophobic liquid chromatography column assignment result; For the acid-base partition result of the amino acid sample solution, when the acid-base partition result of the amino acid sample solution is an acidic amino acid sample solution, a strong cation exchange liquid chromatography column is assigned; when the acid-base partition result of the amino acid sample solution is a neutral amino acid sample solution, a size exclusion liquid chromatography column is assigned; when the acid-base partition result of the amino acid sample solution is an alkaline amino acid sample solution, a strong anion exchange liquid chromatography column is assigned to obtain the acid-base liquid chromatography column assignment result; For the optical rotation partition result of the amino acid sample solution, when the optical rotation partition result of the amino acid sample solution is a chiral amino acid sample solution, a chiral liquid chromatography column is assigned; when the optical rotation partition result of the amino acid sample solution is an achiral amino acid sample solution, a reversed phase liquid chromatography column is assigned to obtain an optical rotation liquid chromatography column assignment result.

8. The quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry according to claim 7, characterized in that: In the step 5, the process of constructing the liquid chromatography column distribution model includes: The hydrophobicity partition results of the amino acid sample solutions and their corresponding hydrophobicity liquid chromatography column distribution results, the acidity and alkalinity partition results of the amino acid sample solutions and their corresponding acidity and alkalinity liquid chromatography column distribution results, and the optical rotation partition results of the amino acid sample solutions and their corresponding optical rotation liquid chromatography column distribution results are used as the third data set, and divided into a training set and a test set in a ratio of 7:3; Combine the training set data with the neural network algorithm, take the hydrophobicity partition results of the amino acid sample solution, the acid-base partition results of the amino acid sample solution and the optical rotation partition results of the amino acid sample solution as input, take the hydrophobic liquid chromatography column distribution results, the acid-base liquid chromatography column distribution results and the optical rotation liquid chromatography column distribution results as output, combine forward propagation and back propagation, learn the nonlinear relationship between the hydrophobicity partition results of the amino acid sample solution and its corresponding hydrophobic liquid chromatography column distribution results, the nonlinear relationship between the acid-base partition results of the amino acid sample solution and its corresponding acid-base liquid chromatography column distribution results, and the nonlinear relationship between the optical rotation partition results of the amino acid sample solution and its corresponding optical rotation liquid chromatography column distribution results, and then train the liquid chromatography column distribution model; The test set data is input into the liquid chromatography column allocation model. The MSE function is used to evaluate the error between the output result of the liquid chromatography column allocation model and the actual liquid chromatography column allocation result. According to the evaluation result, the parameters of the liquid chromatography column allocation model are adjusted to optimize the performance of the liquid chromatography column allocation model. The optimized liquid chromatography column allocation model is deployed into the system to obtain the liquid chromatography column allocation model.

9. The quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry according to claim 8, characterized in that: In step 6, the process of obtaining the liquid chromatography column distribution result includes: Inputting the hydrophobicity partition result, the acidity and alkalinity partition result and the optical rotation partition result of the amino acid sample solution into the liquid chromatography column partition model, the liquid chromatography column partition model outputs the hydrophobicity liquid chromatography column partition result, the acidity and alkalinity liquid chromatography column partition result and the optical rotation liquid chromatography column partition result respectively; When the liquid chromatography column allocation model outputs a single liquid chromatography column allocation result, the liquid chromatography column corresponding to the single liquid chromatography column allocation result is allocated to the corresponding potato amino acid sample solution; When the liquid chromatography column allocation model outputs more than one liquid chromatography column allocation result, it indicates that the amino acid sample solution contains more than one chemical property at the same time. According to the liquid chromatography column allocation results output by the liquid chromatography column allocation model, the corresponding liquid chromatography columns are allocated in sequence to separate the amino acid sample solution in potatoes.

10. The quantitative analysis method of potato amino acids by liquid chromatography tandem mass spectrometry according to claim 9, characterized in that: In step 6, the process of quantitatively analyzing potato amino acids by liquid chromatography tandem mass spectrometry includes: S1. Using an amino acid sample extracted from a potato, an amino acid sample solution of different concentrations is prepared, wherein the amino acid sample solution includes a target amino acid and an internal standard, the concentration of the amino acid sample solution is collected using a fully automatic amino acid analyzer, the chemical properties of the amino acid sample solution are detected, and a liquid chromatography column is allocated to the amino acid sample solution according to the chemical properties of the amino acid sample solution, the temperature of the liquid chromatography column is set to 30 degrees Celsius, a gradient is set according to the separation requirements of the amino acids, and amino acids of different chemical properties in the amino acid sample solution are separated; S2. Using an electrospray ion source in a positive ion mode, respectively set the spray voltage, capillary temperature, sheath gas flow rate, and auxiliary gas flow rate, determine the parent ion and characteristic daughter ion of each amino acid with different chemical properties, set the corresponding monitoring ion pairs and collision energy, and convert the amino acid solutions with different chemical properties flowing out of the liquid chromatography column into gaseous ions; S3. Using a liquid chromatography tandem mass spectrometer to receive gaseous ions generated from an electrospray ion source, separating gaseous ions of different mass-to-charge ratios according to the motion characteristics of gaseous ions in electric and magnetic fields, detecting the separated gaseous ions, and recording the peak areas of the target amino acid and the internal standard at each amino acid sample solution concentration; S4. Draw a standard curve with the concentration of the amino acid sample solution as the horizontal axis and the peak area of ​​the target amino acid and the internal standard as the vertical axis, and obtain the equation and correlation coefficient of the standard curve through linear regression analysis; S5. Calculate the concentration of the amino acid in the amino acid sample solution based on the peak areas of the target amino acid and the internal standard and the equation of the standard curve.

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