Green tea freshness quality evaluation method based on near infrared spectrum and component association

Abstract

The invention discloses a green tea freshness quality evaluation method based on near infrared spectrum and component correlation, and belongs to the field of green tea freshness quality evaluation.The method comprises the steps that the freshness quality grades of a plurality of green tea samples are obtained; determining a freshness component set, determining the freshness component grade of each green tea sample, and determining a final freshness component-freshness quality grade relational expression according to the freshness component grade and the freshness quality grade of each green tea sample; extracting a near infrared spectrum of each green tea sample, and determining wavelength characteristics of each freshness component under each grade; obtaining the near infrared spectrum of the green tea sample to be detected, obtaining the grade of each freshness component of the green tea sample to be detected according to the near infrared spectrum of the green tea sample to be detected and the wavelength characteristics of each freshness component under each grade, and obtaining the freshness quality grade of the green tea sample to be detected according to the final freshness component-freshness quality grade relational expression. The problem that an existing freshness quality grade measuring method is not high in applicability and efficiency is solved.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of fresh quality evaluation of green tea, and particularly relates to a fresh quality evaluation method of green tea based on near-infrared spectroscopy and ingredient correlation. BACKGROUND

[0002] As one of the widely consumed beverages in the world, the flavor quality of tea has an important influence on consumers. Among the many flavor characteristics of tea, umami, as one of the basic tastes, plays a significant role in improving the overall taste and quality of tea soup. Green tea, as an important variety of tea, is favored by consumers for its unique flavor. Among them, umami, as a signature taste characteristic of green tea, is characterized by freshness, liveliness, freshness and vitality. Its presentation not only depends on umami substances such as amino acids, but also requires the joint action of polyphenols at an appropriate concentration to form a refreshing and harmonious sensory experience. Therefore, the freshness quality of green tea is considered as a core indicator for evaluating the quality of green tea, and is also the most easily recognized and most representative product matching degree indicator for consumers. At present, the quality labels of green tea products mostly use comprehensive grades, which deviate from the actual perception of consumers. If the freshness is used as a directional indicator, it will help to improve the recognition and market acceptance of the product. In terms of evaluation methods, although traditional sensory evaluation includes umami elements, it is highly subjective and difficult to accurately quantify freshness.

[0003] In green tea, the presentation of umami is mainly contributed by various secondary metabolites such as amino acids, catechins, free sugars and other substances. The content, proportion and threshold of these substances will affect the presentation of tea soup umami. Therefore, it is feasible to identify the freshness of green tea through chemical composition.

[0004] Existing quantitative descriptive analysis (QDA) or high-performance liquid chromatography (HPLC) chemical detection methods can achieve a certain degree of evaluation, but generally have limitations such as complex process, long time-consuming, expensive equipment, and high requirements for operators. Therefore, it is of great significance to establish an efficient, rapid and objective green tea freshness quality evaluation technology for accurate identification and quality control of tea. SUMMARY

[0005] In view of the above shortcomings in the prior art, the green tea freshness quality evaluation method based on near-infrared spectroscopy and ingredient correlation provided by the present application solves the problem of low applicability and efficiency of existing freshness quality grade measurement methods.

[0006] In order to achieve the above-mentioned purpose of the application, the technical scheme adopted by the present application is as follows: a green tea freshness quality evaluation method based on near-infrared spectroscopy and ingredient correlation, comprising: obtaining a plurality of green tea samples and the freshness quality grades of the green tea samples; determine a freshness component set, and set a grade threshold of each freshness component in the freshness component set; According to the grade threshold of each freshness component, determine the freshness component grade of each green tea sample, and according to the freshness component grade and the freshness quality grade of each green tea sample, determine a final freshness component-freshness quality grade relationship formula; the expression of the final freshness component-freshness quality grade relationship formula is:

[0007] wherein, is the corresponding freshness quality grade with the maximum posterior probability; is the value of the maximum ; is the freshness quality grade parameter; is the freshness component vector; is the freshness quality grade number; is the final model parameter vector; is the freshness component weight vector corresponding to the first grade; is the bias term corresponding to the first grade; is the total number of freshness quality grades; extract the near-infrared spectrum of each green tea sample, and determine the wavelength characteristics under each grade of each freshness component; obtain the near-infrared spectrum of the green tea sample to be tested, and according to the near-infrared spectrum of the green tea sample to be tested and the wavelength characteristics under each grade of each freshness component, obtain the grade of each freshness component of the green tea sample to be tested, and according to the final freshness component-freshness quality grade relationship formula, obtain the freshness quality grade of the green tea sample to be tested.

[0008] Further, the determination of the importance coefficient of each freshness component is specifically: measure the content of each freshness component in each green tea sample, and based on the grade threshold of each freshness component, obtain the grade of each freshness component in each green tea sample, and integrate the green tea sample information to obtain a green tea sample dataset:

[0009] wherein, is the green tea sample dataset; is the first green tea sample; is the freshness quality grade of the first green tea sample; is the content of the first freshness component; is the grade of the first freshness component; is the green tea sample index; a green tea sample set is collected; a freshness component index is generated; a freshness component set is generated; a plurality of green tea sample data subsets are generated according to the green tea sample data set based on the freshness quality grades; each green tea sample data subset is sorted from the best to the worst based on the freshness quality grades; for the rth green tea sample data subset, a freshness component-freshness quality grade relationship formula of the rth green tea sample data subset is constructed based on the freshness component-freshness quality grade relationship formula of the (r-1)th green tea sample data subset, until the freshness component-freshness quality grade relationship formula of the last green tea sample data subset is obtained as the final freshness component-freshness quality grade relationship formula.

[0010] Further, a logistic regression model with parameter initialization is selected as the classification model to fit the freshness component-freshness quality grade relationship formula.

[0011] Further, when r=1, the expression of the freshness component-freshness quality grade relationship formula is:

[0012] wherein, is a binary variable for indicating whether it is the freshness quality grade corresponding to the 1st green tea sample data subset, and is 1, indicating that it is, and is 0, indicating that it is not; is the posterior probability of determining that =1 when r=1; is a determination threshold; is a freshness quality grade parameter; is a freshness component vector; is the model parameter when r=1; is a sigmoid activation function; is the freshness component weight vector when r=1; is a transpose; is the bias term when r=1.

[0013] Further, when r is not equal to 1, the logistic regression model is expanded into a multi-classification model; the number of classifications is equal to the value of r.

[0014] Further, when r is not equal to 1, the expression of the initial parameters of the logistic regression model is:

[0015] wherein, is the initial freshness component weight vector of the logistic regression model when r is not equal to 1; is the influence weight of the freshness component-freshness quality grade relationship formula when r-1. is the freshness component weight vector when r-1; is the random freshness component weight vector generated when training on the rth green tea sample data subset; is the initial bias term of the logistic regression model when r is not equal to 1; is the bias term when r-1; is the random bias term generated when training on the rth green tea sample data subset.

[0016] The present application has the advantages that the present application solves the grade boundary deviation caused by the difference in the component distribution of different grades of samples in the traditional method; the present application performs the training of the current grade while retaining the migration grade characteristics through the transmission of the preceding subset parameters, ensures that the determination standards of each grade gradually link from “good to poor”, and avoids the fragmentation of the grade boundary; the freshness component quantity / grade is determined through the near-infrared spectrum in combination with the near-infrared spectrum technology. The overall technical process does not cause damage to the tea itself, and retains the original quality and appearance of the tea. BRIEF DESCRIPTION OF DRAWINGS

[0017] Figure 1 is the flowchart of the method of the present application.

[0018] Figure 2 is the average content diagram of aspartic acid of different freshness samples in the embodiment of the present application.

[0019] Figure 3 is the average content diagram of glutamic acid of different freshness samples in the embodiment of the present application.

[0020] Figure 4 is the average content diagram of asparagine of different freshness samples in the embodiment of the present application.

[0021] Figure 5 is the average content diagram of serine of different freshness samples in the embodiment of the present application.

[0022] Figure 6 is the average content diagram of theanine of different freshness samples in the embodiment of the present application.

[0023] Figure 7 is the average content diagram of cysteine of different freshness samples in the embodiment of the present application.

[0024] Figure 8 is the average content diagram of methionine of different freshness samples in the embodiment of the present application.

[0025] Figure 9 is the average content diagram of L-leucine of different freshness samples in the embodiment of the present application.

[0026] Figure 10Figure for average content of L-phenylalanine in different freshness samples in the embodiment of the present application.

[0027] Figure 11 Figure for average content of histidine in different freshness samples in the embodiment of the present application.

[0028] Figure 12 Figure for average content of ornithine in different freshness samples in the embodiment of the present application.

[0029] Figure 13 Figure for average content of lysine in different freshness samples in the embodiment of the present application.

[0030] Figure 14 Figure for average content of tyrosine in different freshness samples in the embodiment of the present application.

[0031] Figure 15 Figure for average content of gallic acid in different freshness samples in the embodiment of the present application.

[0032] Figure 16 Figure for average content of gallocatechin gallate in different freshness samples in the embodiment of the present application.

[0033] Figure 17 Figure for average content of gallocatechin in different freshness samples in the embodiment of the present application.

[0034] Figure 18 Figure for average content of catechin in different freshness samples in the embodiment of the present application.

[0035] Figure 19 Figure for average content of caffeine in different freshness samples in the embodiment of the present application.

[0036] Figure 20 Figure for average content of epicatechin in different freshness samples in the embodiment of the present application.

[0037] Figure 21 Figure for average content of epigallocatechin gallate in different freshness samples in the embodiment of the present application.

[0038] Figure 22 Figure for average content of gallocatechin gallate in different freshness samples in the embodiment of the present application.

[0039] Figure 23 Figure for average content of epigallocatechin in different freshness samples in the embodiment of the present application.

[0040] Figure 24 Figure for average content of amber acid in different freshness samples in the embodiment of the present application. DETAILED DESCRIPTION

[0041] The specific embodiments of the present application are described below to facilitate the understanding of the present application for those skilled in the art, but it should be clear that the present application is not limited to the scope of the specific embodiments, and for those skilled in the art, it is obvious that various changes are within the spirit and scope of the present application defined and determined by the appended claims, and all the inventions utilizing the concept of the present application are within the scope of protection.

[0042] As shown in the figure, in one embodiment of the present application, a green tea freshness quality evaluation method based on near-infrared spectrum and component correlation includes: Figure 1 Obtaining a plurality of green tea samples and the freshness quality grade of each green tea sample; Determining the freshness component set and setting the grade threshold of each freshness component in the freshness component set; According to the grade threshold of each freshness component, determining the freshness component grade of each green tea sample, and according to the freshness component grade and the freshness quality grade of each green tea sample, determining the final freshness component-freshness quality grade relationship formula; the expression of the final freshness component-freshness quality grade relationship formula is:

[0043] Among them, is the corresponding freshness quality grade with the maximum posterior probability; is the value of the maximum ; is the freshness quality grade parameter; is the freshness component vector; is the freshness quality grade number; is the final model parameter vector; is the freshness component weight vector corresponding to the th grade; is the bias term corresponding to the th grade; is the total number of freshness quality grades; Extracting the near-infrared spectrum of each green tea sample, determining the wavelength characteristics of each freshness component under each grade; Obtaining the near-infrared spectrum of the green tea sample to be tested, according to the near-infrared spectrum of the green tea sample to be tested and the wavelength characteristics of each freshness component under each grade, obtaining the grade of each freshness component of the green tea sample to be tested, and according to the final freshness component-freshness quality grade relationship formula, obtaining the freshness quality grade of the green tea sample to be tested.

[0044] ​In this embodiment, by seeking the relationship between each freshness component and freshness quality grade, a relationship formula for determining freshness quality grade through freshness component is established; on this basis, in order to avoid the determination of freshness component from destroying tea leaves, combined with near-infrared spectroscopy technology, the determination of freshness component content / grade (freshness component grade is based on content division) is carried out through near-infrared spectroscopy. The overall technical process does not destroy the tea itself, and the original quality and appearance of the tea are retained.

[0045] In this embodiment, the process of obtaining the freshness quality grade of each green tea sample is as follows: The QDA method is used to evaluate the umami intensity of green tea samples. A sensory evaluation team is established to complete the QDA evaluation of umami intensity in this study. The evaluation team members are trained multiple times before the formal evaluation begins. The discrimination ability, stability, and consistency of the evaluation team have all met the standards using Panel check software.

[0046] In the formal experiment, sodium glutamate is used as the umami standard reference, and a 10-point scale method is used for descriptive analysis of the umami intensity of green tea samples. The evaluation scale of equivalent umami intensity of sodium glutamate is shown in Table 1. The tea infusion conditions are tea-to-water ratio of 1:50, infusion time of 4 min, and the brewed tea is placed in a 50℃ water bath for incubation to ensure that the tea temperature is between 45±2℃ when tasted by the evaluators. 50mL of tea is removed and labeled with a random three-digit code and distributed to the evaluation team. The evaluation team determines the umami intensity of the tea based on the standard reference solution. The interval between two sample evaluations should be 5min and the evaluators should rinse their mouths with water to reduce the interaction between flavors and taste fatigue.

[0047] Table 1 Umami intensity and its corresponding standard reference sample concentration

[0048] The QDA evaluation results of the freshness of 1000 green tea samples show that the umami intensity ranges from 2.0 to 4.25. According to the evaluation results, the samples can be divided into three different umami intensity sample groups: X1 is the high freshness group, with 270 samples having umami intensity above 3.6; X2 is the medium freshness group, with 513 samples having umami intensity between 2.8 and 3.6; and X3 is the low freshness group, with 217 samples having umami intensity below 2.8. It can be seen that the umami intensity of this batch of green tea covers most of the range from good to poor, with good discrimination.

[0049] The free amino acid content of different freshness tea samples was detected. The results are as follows Figures 2-14As shown, in green tea samples of different freshness, theanine was the highest content of amino acid, followed by Asp (aspartic acid), Asn (asparagine), Glu (glutamic acid) and Ser (serine), among which theanine, Asp (aspartic acid), Asn (asparagine), Glu (glutamic acid) were umami amino acids, and Ser (serine) was a sweet amino acid. In addition, Glu (glutamic acid), Ala (alanine) and Thr (threonine) were sweet; Tyr (tyrosine), Leu (L-leucine) and theanine had lower content. With the decrease of green tea umami intensity, the content of theanine, Asp (aspartic acid), Glu (glutamic acid), Asn (asparagine), Cys (cysteine), Met (methionine), Leu (L-leucine), His (histidine), Orn (ornithine) and Lys (lysine) gradually decreased. Free amino acids in tea, especially theanine, were the main components of green tea freshness. When the content of free amino acids such as theanine decreased, the freshness of green tea would be significantly weakened, and the taste of tea soup would become relatively flat. At the same time, the taste of tea is the result of the joint action of many components, and different free amino acids may exhibit different taste characteristics. When the content of free amino acids decreases, the freshness of green tea decreases, which may also be accompanied by changes in bitter, astringent, sour, sweet and other taste qualities, leading to poor taste coordination. By comparing the differences in the content of free amino acids between different freshness, theanine, Glu (glutamic acid) and Ser (serine) showed significant differences between different freshness samples, indicating that they might be the key substances affecting the freshness of green tea. Phe is L-phenylalanine.

[0050] The results of comparison of catechin components and caffeine content between different grades are as follows Figures 15-23As shown in the figure, comparing the test results of tea samples with different freshness levels reveals that as the umami intensity decreases, the contents of GA (gallic acid), GC (gallocatechin), C (catechin), and GCG (gallocatechin gallate) decrease, while the contents of EGC (epigallocatechin), EC (epigallocatechin), and EGCG (epigallocatechin gallate) increase. The contents of EGC (epigallocatechin) and CAF (caffeine) remain relatively stable. Analyzing the differences in catechin and caffeine contents among green tea samples with different freshness levels, significant differences were found in the contents of GA (gallic acid), EGC (epigallocatechin), C (catechin), EC (epigallocatechin), EGCG (epigallocatechin gallate), and GCG (glucocatechin gallate). Among these, EGCG (epigallocatechin gallate) showed significant differences among all three groups of samples. EGCG (epigallocatechin gallate) is one of the main bitter and astringent components in green tea. Because it has a low bitter and astringent threshold, as the content of EGCG increases, the bitterness and astringency of green tea will become more obvious, which will have a significant impact on the overall taste of the tea soup and may further weaken the umami intensity of green tea.

[0051] The comparison results of succinic acid content among different grades are as follows: Figure 24 As shown, the succinic acid content decreases with the decrease in umami intensity, and the succinic acid content in sample X1 differs significantly from that in samples X2 and X3. Succinic acid itself contributes primarily to the umami flavor, but as a component of the tea infusion, its content changes may indirectly affect the overall flavor of the tea. When the succinic acid content decreases, its synergistic effect with amino acids in the tea infusion may weaken, leading to a decrease in the umami intensity of the tea infusion.

[0052] The determination of the importance coefficients of each freshness component is specifically as follows: The content of each freshness component in each green tea sample was measured, and the grade of each freshness component in each green tea sample was obtained based on the grade threshold of each freshness component. The green tea sample information was then integrated to obtain a green tea sample dataset.

[0053] in, For green tea sample datasets; For the first One green tea sample; For the first The freshness quality grade of each green tea sample; For the first Content of each freshness component; For the first Each freshness component grade; Index for green tea samples; A collection of green tea samples; freshness component index; freshness component set; According to the green tea sample data set, classification is carried out based on the freshness quality grade, and a plurality of green tea sample data subsets are obtained; For each green tea sample data subset, the freshness quality grade is sorted from good to bad; For the rth green tea sample data subset, the freshness component-freshness quality grade relationship formula of the rth green tea sample data subset is constructed based on the freshness component-freshness quality grade relationship formula of the r-1th green tea sample data subset, until the freshness component-freshness quality grade relationship formula of the last green tea sample data subset is obtained as the final freshness component-freshness quality grade relationship formula.

[0054] In this embodiment, the value of the freshness component is converted into a grade description, and the joint influence relationship of each freshness component grade on the freshness quality grade is sought; the complex numerical calculation is converted into a simple grade relationship, which reduces the difficulty and complexity of relationship seeking.

[0055] The logistic regression model with parameter initialization is selected as the classification model to fit the freshness component-freshness quality grade relationship formula.

[0056] When r=1, the expression of the freshness component-freshness quality grade relationship formula is:

[0057] wherein, is a binary variable for indicating whether it is the freshness quality grade corresponding to the first green tea sample data subset, and is 1, indicating that it is, and is 0, indicating that it is not; is the posterior probability of determining that =1; is the determination threshold; is the freshness quality grade parameter; is the freshness component vector; is the model parameter when r=1; is the sigmoid activation function; is the freshness component weight vector when r=1; is the transpose; is the bias term when r=1.

[0058] When r is not equal to 1, the logistic regression model is expanded into a multi-classification model; the number of classifications is equal to the value of r.

[0059] When r is not equal to 1, the expression of the initial parameter of the logistic regression model is:

[0060] wherein, is the initial freshness component weight vector of the logistic regression model when r is not equal to 1; is the influence weight of the freshness component-freshness quality grade relationship when r is equal to 1; is the freshness component weight vector when r is equal to 1; is the random freshness component weight vector generated when training the rth green tea sample data subset; is the initial bias term of the logistic regression model when r is not equal to 1; is the bias term when r is equal to 1; is the random bias term generated when training the rth green tea sample data subset.

[0061] In this embodiment, the relationship is first calculated from the data set with the highest freshness quality grade, and the classification number of the logistic regression model increases as the grade decreases. The model parameters under the previous freshness quality grade are retained, and the model parameters calculated under the current freshness quality grade are corrected under the influence of the model parameters under the previous freshness quality grade. In this way, it is ensured that the final relationship is applicable to tea leaves of each freshness quality grade.

Claims

1. A method for evaluating the freshness quality of green tea based on near infrared spectroscopy and ingredient correlation, characterized by, The method comprises the following steps: Obtain a plurality of green tea samples and freshness quality grades of the green tea samples; Determine a freshness component set and set grade thresholds of the freshness components in the freshness component set; Determine freshness component grades of the green tea samples according to the grade thresholds of the freshness components, and determine a final freshness component-freshness quality grade relationship according to the freshness component grades of the green tea samples and the freshness quality grades; the expression of the final freshness component-freshness quality grade relationship is: wherein, is the corresponding freshness quality class having the largest posterior probability; is the value of the freshness quality class having the largest ; is the freshness quality class parameter; is the freshness component vector; is the freshness quality class number; is the final model parameter vector; is the freshness component weight vector corresponding to the th class; is the bias term corresponding to the th class; is the total number of freshness quality classes; Extract near-infrared spectra of the green tea samples, and determine wavelength characteristics of each grade of each freshness component; Obtain near-infrared spectra of a green tea sample to be tested, and obtain freshness component grades of the green tea sample to be tested according to the near-infrared spectra of the green tea sample to be tested and the wavelength characteristics of each grade of each freshness component, and obtain a freshness quality grade of the green tea sample to be tested according to the final freshness component-freshness quality grade relationship.

2. The method according to claim 1, wherein the method is characterized by, The determination of the importance coefficients of the freshness components is specifically: Measure the contents of the freshness components in the green tea samples, obtain freshness component grades of the green tea samples based on the grade thresholds of the freshness components, and integrate green tea sample information to obtain a green tea sample data set: wherein, is a green tea sample data set; is a fresh quality rating for the th green tea sample; is a fresh quality rating for the th green tea sample; is a freshness constituent content for the th green tea sample; is a freshness constituent rating for the th green tea sample; is a green tea sample index; is a green tea sample set; is a freshness constituent index; is a freshness constituent set; Classify the green tea sample data set based on freshness quality grades to obtain a plurality of green tea sample data subsets; Sort the green tea sample data subsets from good to bad based on freshness quality grades; For the rth green tea sample data subset, construct a freshness component-freshness quality grade relationship of the rth green tea sample data subset based on a freshness component-freshness quality grade relationship of an (r-1) th green tea sample data subset, until a freshness component-freshness quality grade relationship of a last green tea sample data subset is obtained as the final freshness component-freshness quality grade relationship.

3. The method according to claim 2, wherein the method is characterized by, Select a logistic regression model with parameter initialization as a classification model to fit the freshness component-freshness quality grade relationship.

4. The method according to claim 3, wherein the method is characterized by, When r=1, the expression of the freshness component-freshness quality grade relationship is: wherein, is a binary variable for indicating whether the freshness quality grade corresponds to the 1st green tea sample data subset, is 1, indicating yes, and 0, indicating no; is the posterior probability of =1 when r = 1; is the posterior probability of =1 when r = 1; is the decision threshold; is the freshness quality grade parameter; is the freshness component vector; is the model parameter when r = 1; is the sigmoid activation function; is the freshness component weight vector when r = 1; is the transpose; is the bias term when r = 1.

5. The method according to claim 3, wherein the method is characterized by: When r is not equal to 1, the logistic regression model is expanded into a multi-classification model; the number of classifications is equal to the value of r.

6. The method according to claim 5, wherein the method is characterized by, When r is not equal to 1, the expression of the initial parameters of the logistic regression model is: wherein, is an initial freshness component weight vector for the logistic regression model when r is not equal to 1 ; is an influence weight for the freshness component-freshness quality grade relationship when r is equal to 1 ; is a freshness component weight vector when r is equal to 1 ; is a random freshness component weight vector generated when training on the rth green tea sample data subset; is an initial bias term for the logistic regression model when r is not equal to 1 ; is a bias term when r is equal to 1 ; is a random bias term generated when training on the rth green tea sample data subset.

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