Method for identifying age of tieguanyin aged tea

Abstract

This invention relates to a method for identifying the age of aged Tieguanyin tea. The method involves obtaining Tieguanyin tea from different years and detecting the content of key indicators. The content of these key indicators is then input into a storage year prediction model to predict the storage year. The storage year prediction model is: y = 6.702 - 17.142x1 - 0.021x2 - 1.242x3 + 51.878x4 - 8.012x5. The key indicators are dihydroactinolone, β-carotene, phenylacetaldehyde, tea aroma ketone, and 2,2,6-trimethylcyclohexanone. x1-x5 represent the content of the five key indicators in the Tieguanyin tea being tested, and a1-a5 represent the regression coefficients of these five key indicators. The prediction model established by this invention has high prediction accuracy and good stability, enabling effective prediction of the storage year of aged Tieguanyin tea, and providing a scientific basis for evaluating the aging quality of Tieguanyin and identifying its market age.

Description

Technical Field

[0001] This invention belongs to the field of food science and tea quality evaluation technology, specifically relating to a method and application for determining the age of Tieguanyin aged tea based on carotenoids and their volatile aroma components. Background Technology

[0002] As a typical representative of oolong tea, Tieguanyin's quality characteristics are closely related to its aging period. With prolonged storage, the aroma of Tieguanyin gradually shifts from a predominantly fresh floral and fruity fragrance in the new tea stage to a more mature flavor profile characterized by woody and aged aromas, exhibiting distinct differences based on the year of storage. Therefore, accurately determining the storage year is crucial for product pricing, quality grading, and market circulation.

[0003] Currently, the determination of the age of aged Tieguanyin tea mainly relies on sensory evaluation experience, or is supplemented by some conventional physicochemical indicators. However, sensory evaluation is easily affected by the experience level of the evaluators, subjective preferences, and external environmental factors, resulting in insufficient stability and repeatability; while conventional physicochemical indicators mostly focus on moisture, acid value, or other basic indicators, which are difficult to directly reflect the evolution of aroma components in tea during long-term storage, thus limiting the accuracy and consistency of the age determination results.

[0004] Existing research indicates that the composition and content of aroma components in Tieguanyin tea undergo systematic changes during storage. Some volatile substances show a trend of gradual accumulation or attenuation with prolonged storage time, which is closely related to the formation of aged flavor. However, current technologies mostly focus on component detection and analysis of change patterns, and have not yet established quantitative prediction models based on the combination characteristics of key aroma components, nor do they possess a mathematical model system capable of rapidly determining the vintage.

[0005] Therefore, there is an urgent need to construct a method for predicting the storage year based on the characteristics of objective aroma components. By screening characteristic indicators with significant year correlation, a quantifiable and repeatable prediction model can be established to achieve the scientific determination of the aging year of Tieguanyin tea. Summary of the Invention

[0006] In view of the problem that the determination of the aging age of Tieguanyin tea mainly relies on sensory experience and lacks an objective quantitative model in the existing technology, the purpose of this invention is to provide a method for predicting the storage age of Tieguanyin tea based on carotenoid components and some volatile components, so as to improve the accuracy, objectivity and repeatability of the age determination.

[0007] The objective of this invention is achieved through the following technical solution: A method for determining the age of aged Tieguanyin tea includes the following steps: (1) Obtain Tieguanyin tea from different years and test the content of its key indicators; (2) Input the content of key indicators into the storage year prediction model to predict the storage year; the storage year prediction model is as follows: y = K+a1 x1+a2 x2+a3 x3+a4 x4+a5 x5; Where y is the predicted storage year, K is a constant, x1-x5 are the contents of five key indicators in the Tieguanyin tea to be tested, and a1-a5 are the regression coefficients of the five key indicators. Specifically, the key indicators are dihydroactinolone, β-carotene, phenylacetaldehyde, tea ketone, and 2,2,6-trimethylcyclohexanone. The storage year prediction model is as follows: y = 6.702 - 17.142 x1-0.021 x2-1.242 x3+51.878 x4-8.012 x5 Where x1-x5 are the contents of five key indicators in the Tieguanyin tea to be tested: dihydroactinolone, β-carotene, phenylacetaldehyde, tea aroma ketone, and 2,2,6-trimethylcyclohexanone, respectively, and a1-a5 are the regression coefficients of these five key indicators.

[0008] The key indicators were determined using the following method: Samples of Tieguanyin tea from different storage years were obtained; and the following methods were used respectively: (1) Detection of carotenoid components by ultra-high performance liquid chromatography-atmospheric pressure chemical ionization mass spectrometry (UHPLC-APCI-MS); (2) Detection of volatile components by headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS); Key indicators were screened from the detected carotenoid and volatile components. Specifically, by analyzing the relationship between the content of the detected substances and the storage year, the Z-score data of the detected substances were standardized and range analysis was performed. R-type clustering was used to classify the variables, and the key indicators affecting Tieguanyin tea were determined by combining analysis of variance and correlation coefficient method.

[0009] Preferably, the key indicators of the Tieguanyin tea include: dihydroactinolone, β-carotene, phenylacetaldehyde, tea aroma ketone, and 2,2,6-trimethylcyclohexanone.

[0010] The corresponding storage year prediction model is as follows: y = 6.702 - 17.142 x1-0.021 x2-1.242 x3+51.878 x4-8.012 x5 Using the above technical solution, this invention uses liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry to detect carotenoid components and volatile components in Tieguanyin tea stored for different years, screens out key indicators that are significantly related to changes in the year, and constructs a mathematical prediction model to achieve rapid, objective, and quantitative prediction of the storage year of Tieguanyin tea.

[0011] The specific method for detecting carotenoid components using ultra-high performance liquid chromatography-atmospheric pressure chemical ionization mass spectrometry (UHPLC-APCI-MS) is as follows: Tea samples were taken and ground into powder using a ball mill; 50 mg of the ground sample was weighed and extracted with 0.5 mL of a hexane / acetone / ethanol mixture (1:1:1, v / v / v) containing 0.01% BHT (g / mL); the mixture was vortexed at room temperature for 20 min, then centrifuged at 12000 r / min for 5 min at 4 ℃, and the supernatant was collected. This extraction was repeated once, and the supernatants were combined after centrifugation; the resulting extract was concentrated, redissolved in 150 μL of dichloromethane, filtered through a 0.22 μm filter membrane, and stored in a brown vial for LC-MS / MS analysis. The liquid chromatography conditions were as follows: YMC C30 (3μm, 100mm×2.0mm id); mobile phase A was methanol / acetonitrile (1:3, v / v) with 0.01% BHT and 0.1% formic acid, and mobile phase B was methyl tert-butyl ether with 0.01% BHT; the gradient elution program was 0 min A / B 100:0 (V / V), 3 min 100:0 (V / V), 5 min 30:70 (V / V), 9 min 5:95 (V / V), 10 min 100:0 (V / V), and 11 min 100:0 (V / V); the flow rate was 0.8 mL / min; the column temperature was 28 ℃; the injection volume was 2 μL; and the mass spectrometry conditions were as follows: atmospheric pressure chemical ion source temperature 350 ℃, curtain gas 25 psi, and each ion pair was scanned and detected according to the optimized declustering voltage and collision energy. Qualitative and quantitative analysis: A MWDB database was constructed based on standards to perform qualitative analysis on the mass spectrometry data. Quantitative analysis was performed using the multiple reaction monitoring mode of triple quadrupole mass spectrometry to obtain mass spectrometry data for different samples. The chromatographic peaks of the target compounds (such as β-carotene and 21 other carotenoids) in the selected carotenoid components were integrated, and quantitative analysis was performed using a standard curve. Preparing concentrations of 0.001 μg / mL, 0.005 μg / mL, 0.01 μg / mL, 0.05 μg / mL, 0.1 μg / mL, 0.5 μg / mL, 1 μg / mL, 5 μg / mL, 10 μg / mL, and 25 μg / mL... We obtained the chromatographic peak intensity data of the corresponding quantitative signal of the standard solution of the target substance with a concentration of μg / mL. We plotted the standard curve of the target substance with the standard concentration as the abscissa and the standard peak area as the ordinate. We substituted the integral peak area of ​​all detected samples into the linear equation of the standard curve for calculation. After further substituting into the calculation formula, we finally obtained the content of the corresponding substance in the actual sample. The content (μg / g) of the target compound in a selected carotenoid component in the sample = c V / 1000 / m Where c is the concentration value obtained by substituting the integrated peak area in the sample into the standard curve; V is the volume (μL) of the solution used for reconstitution; and m is the mass of the sample taken.

[0012] The method for detecting volatile components using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) is as follows: The tea sample was pulverized into powder. 0.1 g of tea powder and 1.5 g of NaCl were accurately weighed into an 18 mL headspace vial, followed by the addition of 20 μL of 2-octanol internal standard (10 mg / L). Then, 5 mL of boiling water was added to the headspace vial, and the tea was steeped for 5 min. The headspace vial was sealed with a silicone diaphragm. The vial was then placed in a 50 ℃ water bath for 10 minutes. Next, an SPME extraction fiber was inserted into the headspace of the vial, and volatile compounds were extracted in a 50 ℃ water bath for 50 min. Finally, the SPME extraction fiber was inserted into the gas chromatograph inlet, and thermal desorption was performed at 250 ℃ for 5 min, and the program was started. GC-MS parameter settings: Column: HP-INNOWAX (30.0 m × 0.25 mm × 0.25 μm); Temperature program: Initial temperature 40℃, hold for 5 min, increase to 120℃ at 3℃ / min, then increase to 240℃ at 6℃ / min, hold for 5 min; Injector temperature set to 250℃; Splitless injection; Carrier gas: High purity He (purity >99.999%); Flow rate: 1.0 mL / min; Mass spectrometry conditions: ion source temperature was 230 °C; quadrupole temperature was set to 150 °C; electron impact ion energy was 70 eV; mass spectrometry information of 35-450 amu was obtained in full scan mode. Qualitative and quantitative analysis of volatile substances: Total ion chromatograms of volatile substances were obtained by GC-MS analysis, and qualitative analysis was performed using the NIST11 spectral library and the retention index (RI). The retention index (RI) was calculated according to the following formula: In the formula: t X t represents the retention time of the target compound, n and n+1 represent the number of carbon atoms in the n-alkane before and after the target elution, respectively. n and t n+1 For the corresponding n-alkanes, t represents the retention time, where t is the retention time. n <t x <t n+1 The content of volatile substances was calculated using a semi-quantitative method: content of volatile substances (μg / g) = (peak area of ​​volatile substances / peak area of ​​internal standard) × 0.05.

[0013] Compared with the prior art, the advantages of the present invention are as follows: The present invention establishes a system for predicting the age of Tieguanyin tea based on the changing patterns of carotenoid components and some volatile components, avoiding the subjectivity of traditional sensory judgment, improving the stability and repeatability of the judgment results, and providing a new technical approach for the traceability and quality control of aged Tieguanyin tea.

[0014] This invention systematically detects and statistically analyzes the carotenoid and volatile aroma components of Tieguanyin tea during storage, identifies key indicators significantly correlated with storage years, and establishes a multiple regression prediction model to achieve rapid, objective, and quantitative prediction of the storage years of Tieguanyin tea. The prediction model established in this invention has high prediction accuracy and good stability, and can effectively predict the storage years of aged Tieguanyin tea.

[0015] This invention overcomes the shortcomings of traditional sensory judgment, such as strong subjectivity and poor stability, and has the advantages of high detection efficiency, good repeatability, and reliable results. It provides a scientific basis for evaluating the aging quality of Tieguanyin tea and identifying its market age. It can achieve rapid, objective, and quantitative prediction of the storage age of Tieguanyin tea, providing technical support for the traceability, quality control, and market supervision of aged tea. Attached Figure Description

[0016] Figure 1 A flowchart of a method for predicting the storage age of aged Tieguanyin tea provided in an embodiment of the present invention; Figure 2 PCoA score graphs for Tieguanyin tea samples from different storage years; Figure 3 Heatmap showing the content distribution of five key indicators in Tieguanyin tea from different aging years; Figure 4 Verification diagram for the model of predicting the storage age of Tieguanyin aged tea. Detailed Implementation

[0017] The present invention will now be described in detail with reference to the accompanying drawings and embodiments: Please refer to Figure 1 As shown, this invention proposes a method for identifying the age of aged Tieguanyin tea, which includes the following steps: S1: Sample Collection and Composition Analysis Samples of Tieguanyin tea from different storage years were obtained, and the contents of carotenoids and volatile components in the samples were detected.

[0018] Specifically, 32 Tieguanyin tea samples were collected, and the carotenoid and volatile components were detected by liquid chromatography-mass spectrometry and gas chromatography-mass spectrometry, respectively.

[0019] (1) Detection of carotenoid components: Carotenoids were detected using ultra-high performance liquid chromatography-atmospheric pressure chemical ionization mass spectrometry (UHPLC-APCI-MS). Tea samples were ground into powder using a ball mill (30 Hz, 1 min). 50 mg of the ground sample was weighed and extracted with 0.5 mL of a hexane / acetone / ethanol mixture (1:1:1, v / v / v) containing 0.01% BHT (g / mL). The mixture was vortexed at room temperature for 20 min, then centrifuged at 12000 r / min for 5 min at 4 ℃. The supernatant was collected, and the extraction was repeated once, followed by centrifugation and combining the supernatants. The extract was concentrated, redissolved in 150 μL of dichloromethane, filtered through a 0.22 μm filter, and stored in a brown vial for LC-MS / MS analysis.

[0020] The liquid chromatography conditions were as follows: YMC C30 (3 μm, 100 mm × 2.0 mm id); mobile phase A was methanol / acetonitrile (1:3, v / v) with 0.01% BHT and 0.1% formic acid, and mobile phase B was methyl tert-butyl ether with 0.01% BHT; the gradient elution program was: 0 min A / B 100:0 (V / V), 3 min 100:0 (V / V), 5 min 30:70 (V / V), 9 min 5:95 (V / V), 10 min 100:0 (V / V), and 11 min 100:0 (V / V); the flow rate was 0.8 mL / min; the column temperature was 28 ℃; and the injection volume was 2 μL. The mass spectrometry conditions were as follows: atmospheric pressure chemical ion source temperature 350 ℃, curtain gas 25 psi, and each ion pair was scanned and detected based on optimized declustering voltage and collision energy.

[0021] Qualitative and quantitative analysis: The MWDB database was constructed based on standards to perform qualitative analysis on the mass spectrometry data; quantification was performed using the multiple reaction monitoring mode of triple quadrupole mass spectrometry to obtain mass spectrometry analysis data of different samples. The chromatographic peaks of the target compounds (such as 21 types of carotenoids such as β-carotene) in the selected carotenoid components were integrated and quantitative analysis was performed through standard curves. Standard solutions of the target analyte with concentrations of 0.001 μg / mL, 0.005 μg / mL, 0.01 μg / mL, 0.05 μg / mL, 0.1 μg / mL, 0.5 μg / mL, 1 μg / mL, 5 μg / mL, 10 μg / mL, and 25 μg / mL were prepared. Chromatographic peak intensity data of the corresponding quantitative signals of each concentration of standard were obtained. Standard curves of the target analyte were plotted with the standard concentration as the abscissa and the standard peak area as the ordinate. The integral peak areas of all detected samples were substituted into the linear equation of the standard curve for calculation. After further substituting into the calculation formula, the content of the corresponding substance in the actual sample was finally obtained.

[0022] The content of the target compound in the carotenoid component of the sample (μg / g) = c V / 1000 / m Where c is the concentration value obtained by substituting the integrated peak area in the sample into the standard curve; V is the volume (μL) of the solution used for reconstitution; and m is the mass of the sample taken.

[0023] (2) Detection of volatile components: Volatile components were detected using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS). The sample was pulverized into powder, and 0.1 g of tea powder and 1.5 g of NaCl were accurately weighed into an 18 mL headspace vial. Then, 20 μL of 2-octanol internal standard (10 mg / L) was added. Next, 5 mL of boiling water was added to the headspace vial, and the tea was steeped for 5 min. The headspace vial was sealed with a silicone diaphragm. The vial was then placed in a 50 ℃ water bath for 10 min. The SPME extraction fiber was then inserted into the headspace of the vial, and volatile compounds were extracted in a 50 ℃ water bath for 50 min. Finally, the SPME extraction fiber was inserted into the gas chromatograph inlet, and thermal desorption was performed at 250 ℃ for 5 min, and the program was started.

[0024] GC-MS parameter settings: Column: HP-INNOWAX (30.0 m × 0.25 mm × 0.25 μm); Temperature program: Initial temperature 40 ℃, hold for 5 min, increase to 120 ℃ at 3 ℃ / min, then increase to 240 ℃ at 6 ℃ / min, hold for 5 min. Injector temperature set to 250 ℃; splitless injection; carrier gas: high-purity He (purity >99.999%); flow rate: 1.0 mL / min. Mass spectrometry conditions: Ion source temperature 230 ℃; quadrupole temperature set to 150 ℃. Electron impact ion energy was 70 eV, obtaining mass spectrometry information of 35-450 amu in full scan mode.

[0025] Qualitative and quantitative analysis of volatile substances: Total ion chromatograms of volatile substances were obtained by GC-MS analysis. The NIST11 spectral library was searched, and the retention index (RI) was used in conjunction with references to qualitatively identify key aromas (i.e., key storage indicators). The retention index (RI) was calculated using the following formula: In the formula: t X t represents the retention time of the target compound, n and n+1 represent the number of carbon atoms in the n-alkane before and after the target elution, respectively. n and t n+1 For the corresponding n-alkanes, t represents the retention time, where t is the retention time. n <t x <t n+1 The content of volatile substances was calculated using a semi-quantitative method: content of volatile substances (μg / g) = (peak area of ​​volatile substances / peak area of ​​internal standard) × 0.05.

[0026] S2: Analysis of the differences in chemical composition of Tieguanyin tea from different years To analyze the changes in the overall chemical composition of Tieguanyin tea samples during storage, over 110 variables, including 21 types of carotenoids and over 90 types of volatile components, were used as basic data. After Z-score normalization, the distance matrix between samples was calculated, and principal component analysis (PCoA) was performed using R Studio 4.3.1 software. The analytical results are as follows: Figure 2 As shown, the first principal component, PCoA1, explained 61.05% of the differences, and the second principal component, PCoA2, explained 15.58%. These two principal components collectively explained 76.63% of the variance information. The Tieguanyin samples from different storage years exhibited significant distribution differences in the principal coordinate space, indicating that the chemical composition of the Tieguanyin samples changed significantly with prolonged storage. This result further demonstrates the feasibility of using chemical composition information to determine the storage year of aged Tieguanyin tea.

[0027] S3: Screening and Characteristic Analysis of Key Indicators for Determining Storage Years Based on the above analysis results, in order to further determine the key indicators that can effectively determine the storage year of Tieguanyin tea, SPSS 27 software was used to conduct range analysis, R-type cluster analysis, variance analysis and correlation analysis on more than 110 variables. Finally, five key indicators for determining the storage year were selected: dihydroactinolone, β-carotene, phenylacetaldehyde, tea aroma ketone, and 2,2,6-trimethylcyclohexanone.

[0028] Based on this, 32 samples of Tieguanyin tea were obtained, and the contents of the above key indicators in each sample were determined, namely, dihydroactinolone (x1), β-carotene (x2), phenylacetaldehyde (x3), tea aroma ketone (x4), and 2,2,6-trimethylcyclohexanone (x5).

[0029] Based on the key indicator content data obtained from the detection, a heatmap of the key indicator content distribution was drawn using R Studio 4.3.1 software. Figure 3 This study aimed to demonstrate the changing trends of five key indicators in Tieguanyin tea samples from different aging years. The results showed significant differences in the content of these five key indicators across samples from different years. The contents of β-carotene and phenylacetaldehyde decreased significantly with prolonged storage time, while the contents of dihydroactinolone, tea aroma ketone, and 2,2,6-trimethylcyclohexanone gradually increased with increasing storage years. These results indicate a high correlation between these indicators and changes in storage years, making them important indicators for characterizing the storage years of Tieguanyin tea and providing a basis for establishing subsequent storage year prediction models.

[0030] S4: Establishment of a model for predicting storage years Based on the content of five key indicators and storage time (y), a multiple linear regression analysis was conducted to establish a prediction model for the storage year of Tieguanyin tea. The storage year prediction model obtained in this embodiment is as follows: y = 6.702 - 17.142 x1-0.021 x2-1.242 x3+51.878 x4-8.012 x 5。

[0031] The model validation results are shown in Table 1. The analysis of variance results show that the model has a significance p < 0.001 and a goodness of fit R = 0.966, indicating that the storage year prediction model has a good linear relationship and strong predictive ability.

[0032] Table 1: Statistical test of the model for predicting the storage years of Tieguanyin tea

[0033] S5: Validation of the storage year prediction model To verify the effectiveness of the Tieguanyin tea storage year prediction model established in this invention, the model prediction results were compared and analyzed with the actual storage years of the samples. For example... Figure 4 As shown, predictions were validated on 32 Tieguanyin tea samples. The results showed a good linear relationship between the predicted year and the actual year, with a linear regression determination coefficient R0. 2 The root mean square error (RMSE) was 1.133, with a value of 0.933. As shown in Table 2, the prediction error for most samples was controlled within ±2 years, indicating a small overall deviation. These results demonstrate that the prediction model established in this invention possesses high prediction accuracy and good stability, enabling effective prediction of the storage years of aged Tieguanyin tea.

[0034] Table 2: Validation data of the storage period prediction model for Tieguanyin tea

[0035] Application of this invention in predicting storage years: For example, the contents of dihydroactinolone (x1=0), β-carotene (x2=135.34), phenylacetaldehyde (x3=1.61), tea aroma ketone (x4=0), and 2,2,6-trimethylcyclohexanone (x5=0.16) in Tieguanyin tea samples are obtained from carotenoid components and GC-MS results, and then substituted into the regression equation y=6.702-17.142 for predicting the storage years of Tieguanyin tea. x1-0.021 x2-1.242 x3+51.878 x4-8.012 x5 yields a storage year of 0.56 years, which is highly consistent with the actual year of 0.

[0036] In summary, this invention, through systematic detection and statistical analysis of carotenoid and volatile aroma components during the storage of Tieguanyin tea, screened out key indicators significantly correlated with the storage year and established a multiple regression prediction model, achieving rapid, objective, and quantitative prediction of the storage year of Tieguanyin. This invention overcomes the shortcomings of traditional sensory judgment, which is highly subjective and unstable, and has advantages such as high detection efficiency, good repeatability, and reliable results, providing a scientific basis for evaluating the aging quality of Tieguanyin and identifying its market age.

[0037] The above description is merely a preferred embodiment of the present invention and is not intended to limit the scope of protection of the present invention. All equivalent substitutions or improvements made within the spirit and principles of the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for identifying the age of aged Tieguanyin tea, characterized in that: Includes the following steps: (1) Obtain Tieguanyin tea from different years and test the content of its key indicators; (2) Input the content of key indicators into the storage year prediction model to predict the storage year; The key indicators are dihydroactinolone, β-carotene, phenylacetaldehyde, tea ketone, and 2,2,6-trimethylcyclohexanone. The storage year prediction model is as follows: y = 6.702 - 17.142 x1-0.021 x2-1.242 x3+51.878 x4-8.012 x5 Where x1-x5 are the contents of five key indicators in the Tieguanyin tea to be tested: dihydroactinolone, β-carotene, phenylacetaldehyde, tea aroma ketone, and 2,2,6-trimethylcyclohexanone, respectively, and a1-a5 are the regression coefficients of these five key indicators.

2. The method for predicting the storage year of Tieguanyin tea according to claim 1, characterized in that: The key indicators were determined using the following method: Samples of Tieguanyin tea from different storage years were obtained; and the following methods were used respectively: (1) Detection of carotenoid components by ultra-high performance liquid chromatography-atmospheric pressure chemical ionization mass spectrometry (UHPLC-APCI-MS); (2) Detection of volatile components by headspace solid phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS); Key indicators were screened from the detected carotenoid and volatile components. Specifically, by analyzing the relationship between the content of the detected substances and the storage year, the Z-score data of the detected substances were standardized and range analysis was performed. R-type clustering was used to classify the variables, and the key indicators affecting Tieguanyin tea were determined by combining analysis of variance and correlation coefficient method.

3. The method for predicting the storage year of Tieguanyin tea according to claim 2, characterized in that: The specific method for detecting carotenoid components using ultra-high performance liquid chromatography-atmospheric pressure chemical ionization mass spectrometry (UHPLC-APCI-MS) is as follows: Tea samples were taken and ground into powder using a ball mill; 50 mg of the ground sample was weighed and extracted with 0.5 mL of a hexane / acetone / ethanol mixture (1:1:1, v / v / v) containing 0.01% BHT (g / mL); the mixture was vortexed at room temperature for 20 min, then centrifuged at 12000 r / min for 5 min at 4 ℃, and the supernatant was collected. This extraction was repeated once, and the supernatants were combined after centrifugation; the resulting extract was concentrated, redissolved in 150 μL of dichloromethane, filtered through a 0.22 μm filter membrane, and stored in a brown vial for LC-MS / MS analysis. The liquid chromatography conditions were as follows: YMC C30 (3μm, 100mm×2.0mm id); mobile phase A was methanol / acetonitrile (1:3, v / v) with 0.01% BHT and 0.1% formic acid, and mobile phase B was methyl tert-butyl ether with 0.01% BHT; the gradient elution program was 0 min A / B 100:0 (V / V), 3 min 100:0 (V / V), 5 min 30:70 (V / V), 9 min 5:95 (V / V), 10 min 100:0 (V / V), and 11 min 100:0 (V / V); the flow rate was 0.8 mL / min; the column temperature was 28 ℃; the injection volume was 2 μL; and the mass spectrometry conditions were as follows: atmospheric pressure chemical ion source temperature 350 ℃, curtain gas 25 psi, and each ion pair was scanned and detected according to the optimized declustering voltage and collision energy. Qualitative and quantitative analysis: A MWDB database was constructed based on standards to perform qualitative analysis on the mass spectrometry data. Quantitative analysis was performed using the multiple reaction monitoring mode of triple quadrupole mass spectrometry to obtain mass spectrometry analysis data for different samples. The chromatographic peaks of the target compounds in the selected carotenoid components were integrated, and quantitative analysis was performed using a standard curve. Standard solutions of the target compounds with concentrations of 0.001 μg / mL, 0.005 μg / mL, 0.01 μg / mL, 0.05 μg / mL, 0.1 μg / mL, 0.5 μg / mL, 1 μg / mL, 5 μg / mL, 10 μg / mL, and 25 μg / mL were prepared, and the chromatographic peak intensity data of the corresponding quantitative signals of the standards at each concentration were obtained. A standard curve for the corresponding target compounds was plotted with the standard concentration as the x-axis and the standard peak area as the y-axis. The integrated peak areas of all detected samples were substituted into the linear equation of the standard curve for calculation. After further substituting into the calculation formula, the content of the corresponding substances in the actual samples was finally obtained. The content of the target compound in the carotenoid component of the sample (μg / g) = c V / 1000 / m Where c is the concentration value obtained by substituting the integrated peak area in the sample into the standard curve; V is the volume (μL) of the solution used for reconstitution; and m is the mass of the sample taken.

4. The method for predicting the storage year of Tieguanyin tea according to claim 2, characterized in that: The method for detecting volatile components using headspace solid-phase microextraction-gas chromatography-mass spectrometry (HS-SPME-GC-MS) is as follows: The tea sample was pulverized into powder. 0.1 g of tea powder and 1.5 g of NaCl were accurately weighed into an 18 mL headspace vial, followed by the addition of 20 μL of 2-octanol internal standard (10 mg / L). Then, 5 mL of boiling water was added to the headspace vial, and the tea was steeped for 5 min. The headspace vial was sealed with a silicone diaphragm. The vial was then placed in a 50 ℃ water bath for 10 minutes. Next, an SPME extraction fiber was inserted into the headspace of the vial, and volatile compounds were extracted in a 50 ℃ water bath for 50 min. Finally, the SPME extraction fiber was inserted into the gas chromatograph inlet, and thermal desorption was performed at 250 ℃ for 5 min, and the program was started. GC-MS parameter settings: Column: HP-INNOWAX (30.0 m × 0.25 mm × 0.25 μm); Temperature program: Initial temperature 40 ℃, hold for 5 min, increase to 120 ℃ at 3 ℃ / min, then increase to 240 ℃ at 6 ℃ / min, hold for 5 min; Injector temperature set to 250 ℃; Splitless injection; Carrier gas: High purity He (purity > 99.999%); Flow rate: 1.0 mL / min; Mass spectrometry conditions: ion source temperature was 230 °C; quadrupole temperature was set to 150 °C; electron impact ion energy was 70 eV; mass spectrometry information of 35-450 amu was obtained in full scan mode. Qualitative and quantitative analysis of volatile substances: The total ion chromatogram of volatile substances was obtained by GC-MS analysis, and qualitative analysis was performed by searching the NIST11 spectral library and combining the retention index RI. The retention index RI is calculated using the following formula:

5. In the formula: t X t represents the retention time of the target compound, n and n+1 represent the number of carbon atoms in the n-alkane before and after the target elution, respectively. n and t n+1 For the corresponding n-alkanes, the retention times are: t n <t x <t n+1 The content of volatile substances was calculated using a semi-quantitative method: content of volatile substances (μg / g) = (peak area of ​​volatile substances / peak area of ​​internal standard) × 0.05.

Images