CN113029975A - Method for identifying quality of freeze injury tea - Google Patents
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Abstract
The invention provides a method for identifying and evaluating frozen tea leaves, which is characterized by utilizing an intelligent sensory analysis instrument to determine the liquor color, aroma and taste of different varieties of tea tree frozen tea leaves, deducing a linear fitting equation based on a stepwise regression method through characteristic value screening, constructing a frozen tea leaf quality identification model and realizing identification and evaluation of the frozen tea leaf quality. The method is based on 16 indexes of an intelligent sensory instrument for comprehensive evaluation, is more accurate and objective compared with the method for identifying the frozen tea leaves by using single indexes or few indexes in the prior art, is suitable for the technical systems for identifying and evaluating the frozen tea leaves of different tea tree varieties, and is favorable for the standardization of tea leaf quality management in the tea leaf market.
Description
Technical Field
The invention belongs to the field of food quality detection, and relates to a method and a technology for intelligent sensory instrument analysis, sensory evaluation and statistical analysis.
Background
Tea tree (Camellia sinensis (L.) o.kuntze) is a perennial evergreen woody plant widely distributed in nearly 20 provinces of China. At present, the tea industry becomes an important support industry of rural economy in the main production area and an export-induced profit advantageous industry. Along with the rapid increase of the production scale of the tea industry in China, the problems of quality and safety of tea products, such as 'counterfeit and counterfeit tea', 'heavy metal of tea products exceeds standard', 'pesticide residue' of tea products and the like, are frequently seen, the occurrence frequency of uncontrollable weather conditions such as slight 'late spring coldness' is higher, and the extension of tea tips can be delayed to cause the reduction of the yield of the tea. The influence of low temperature causes huge loss to the tea industry every year, and seriously influences the stable and healthy development of the tea market. The quality safety of tea is the life line of tea, and a new technology related to tea quality evaluation is urgently needed.
The tea leaf evaluation technology is used as the core of tea leaf quality inspection, plays a role in guiding and promoting tea leaf production, and is always regarded as the center of tea leaf production. The tea leaves with different grades not only have great difference in flavor, but also are often far from each other in price. At present, the tea is mainly evaluated by traditional sensory evaluation, namely, human sense organs such as smell, taste, vision, touch and the like are utilized, five evaluation items such as appearance, aroma, liquor color, taste, leaf bottom and the like are divided into two aspects of dry-looking evaluation of appearance and open-soup evaluation of internal quality through a certain tea evaluation program, and each item also comprises a plurality of factors, so that the quality and the height of the tea are comprehensively evaluated. However, sensory analysis is easily interfered by a plurality of objective factors and subjective factors, has higher professional requirements on the appraisers and is greatly influenced by factors such as personal preference of the appraisers, and is easily interfered by external factors such as dry and wet environments and regional differences of the appraising places, which are all important factors causing inaccurate final appraising results, so that the appraising results are easy to cause disputes.
The physical and chemical evaluation instead of the sensory evaluation is a long-term wish of tea workers at home and abroad, and the development of scientific technology and the requirement of the market on the tea standard enable more emerging technologies to be applied to the evaluation process of tea. The intelligent sensory analysis technology is based on the simulation of the sensory perception process of human bodies, and can simulate the sensory perception of human bodies to a certain extent and give judgment results and fingerprint information related to the aroma, the taste and the foreign matters of tea leaves. And carrying out preprocessing, feature extraction, mode judgment and the like on the obtained intelligent sensory data by using a mode identification method so as to obtain the judgment on the tea quality. The intelligent sensory analysis technology has the advantages of short detection time, good repeatability, no need of complex sample pretreatment process, no occurrence of sensory fatigue, objective and reliable detection result and the like, is a hotspot and development trend of the current tea quality detection research, but has no relevant report on the aspect of identifying the quality of the frozen tea at present.
Disclosure of Invention
The invention aims to overcome the defects of the prior art and provide a method for identifying the quality of frozen tea.
A method for identifying the quality of freeze injury tea leaves is realized by the following steps:
1) selecting a plurality of tea tree varieties, collecting one bud of three leaves in current-year branches which are free from plant diseases and insect pests and have consistent growth vigour, washing the three leaves clean with purified water, drying surface water by using filter paper, setting N different experimental groups for low-temperature stress treatment for each tea tree variety, wherein the low-temperature stress treatment temperature is less than or equal to 10 ℃, and taking naturally-growing tea tree leaves as a reference; preparing tea samples of N experimental groups and a control group into steamed green samples, uniformly mixing each steamed green sample, dividing into 4 parts according to a quartering method, and taking 1 part of the steamed green samples for detection;
2) detecting the color of the tea soup by using a color difference meter, and measuring color parameters of brightness L, chroma a and chroma b;
3) detecting the aroma of the tea leaves by using an electronic nose, and measuring a response value of a sensor;
4) detecting the taste of the tea soup by using the electronic tongue, and measuring the response value of the sensor;
5) constructing a freezing injury tea quality identification model through the parameters of the color difference meter, the response value of the electronic nose sensor and the response value data of the electronic tongue sensor;
6) selecting tea leaves to be identified, making the tea leaves into steamed green samples, obtaining color difference meter parameters, response values of an electronic nose sensor and response values of an electronic tongue sensor through the steps 2) to 4), and identifying whether the tea leaves are frozen and/or identifying the quality of the frozen tea leaves by using the freezing injury tea leaf quality identification model established in the step 5).
In a preferred embodiment of the invention, 6 tea plant varieties are selected, namely Wuniuzao, Pingyangte-Taizao, Maolang, Zhongcha 102, Longjing 43 and Zhongcha 108 respectively, clone of the tea plant variety, age 6 a.
The time of the low-temperature stress treatment is 4 hours in a pre-selection mode, and further, the low-temperature stress treatment temperatures of the experimental groups are-16 ℃, 10 ℃, 5 ℃, 0 ℃, 4 ℃ and 10 ℃.
Preferably, the method needs to adopt a color difference meter with the model number of CM-3600A.
Preferably, the preparation method of the tea soup needs to refer to the national standard tea sensory evaluation method (GB/T/23776-.
Preferably, the electronic nose is GEMINI, and is provided with 6 metal oxide sensors (T70/2, PA/2, P30/1, P30/2, LY2/AA, LY 2/gCT).
Preferably, the preparation method of the sample for detecting the electronic nose is that 2g of the sample is placed in a 100mL beaker, sealed by a preservative film, placed in an oven at 50 ℃ for 10min and then taken out for detection.
Preferably, the electronic nose detection program is that the carrier gas is dry clean air, and the flow rate of the carrier gas is 150 mL-min-1. The data acquisition time is 90s, the delay time is 210s, and the maximum response value of each sensor is selected for statistical analysis.
Preferably, the electronic tongue is of an Astree type and is provided with 1 Ag/AgCl reference electrode and a 1# sensor (ZZ, JE, BB, CA, GA, HA and GB).
Preferably, the electronic tongue detection program sets the data acquisition time to be 120s, the stirring speed to be 1 time/s, and takes the average value of the last 20s measured values as the response value of the sensor. The sensor was washed 1 time into the wash solution for each 1 sample.
Preferably, the quality identification model of the frozen tea leaves is constructed by normalizing data, screening characteristic values and deducing a linear fitting equation based on a stepwise regression method.
Preferably, in the step 6), the quality identification model of the frozen tea leaves constructed in the step 5) is used for identifying whether the tea leaves are frozen, and specifically comprises the following steps: and inputting a colorimeter parameter, an electronic nose sensor response value and an electronic tongue sensor response value into the freezing injury tea quality identification model, outputting a predicted processing temperature as a model output result, and if the predicted processing temperature is less than or equal to 10 ℃, determining that the tea sample to be detected is frozen.
Further, if the predicted processing temperature obtained by the model is lower, the higher the freezing damage degree of the tea to be measured is, the lower the quality is.
Drawings
FIG. 1 shows the difference of color values of tea soup colors;
FIG. 2a shows the difference in the values of the derived indices b/a of the tea sample broths
FIG. 2b shows the difference of the saturation C of the tea soup;
FIG. 3 is a graph of color difference score analysis data cluster analysis of various tea samples;
note: A-Pingyang Tezao, B-Wunizao, C-maoLu, D-Longjing 43, E-Zhongcha 102, F-Zhongcha 108
FIG. 4 is an electronic nose detection radar chart of various tea-like tea soups;
FIG. 5a is an electronic nose principal component analysis of various tea-like aromas;
FIG. 5b is an electronic nose discriminant factor analysis of various tea sample aromas;
note: round-Pingyang super early, cross-Wuniao, diamond-maoLu, star-Longjing 43, square-Zhongcha 102, straight-Zhongcha 108.
FIG. 6 is a graph of cluster analysis of electronic nose analysis data for various tea samples;
FIG. 7 is an electronic tongue detection radar chart for various tea-like tea soups;
FIG. 8 is an electronic tongue principal component analysis (left) and discriminant factor analysis (right) of various tea sample aromas;
FIG. 9 is a graph of cluster analysis of electronic tongue analysis data for various tea samples.
Detailed Description
The invention is further described in the following with reference to the figures and examples of the specification.
The preparation process of the steamed green tea sample in each embodiment of the invention is as follows:
selecting 6 tea tree varieties (Wuniao, Pingyang super-early, Maoyun, Zhongcha 102, Longjing 43 and Zhongcha 108), clone and tree age 6a, collecting three leaves of one bud in current-year branches without diseases and insect pests and with consistent growth vigor, washing with purified water, sucking surface water by filter paper, and carrying out low-temperature stress treatment for 4h at the treatment temperature of-16 ℃, 10 ℃, 5 ℃, 0 ℃, 4 ℃ and 10 ℃, wherein the leaves of naturally-growing tea trees are used as a reference. Samples of 7 different treatment temperatures for each tea variety were made into steamed samples. Each part of the steamed green sample is uniformly mixed and divided into 4 parts according to a quartering method, and 1 part of the mixture is taken for detection.
In the invention, the preparation method of the tea soup for detecting the color and the taste by using the color difference meter and the electronic tongue refers to the tea sensory evaluation method of the national standard (GB/T23776-.
Example 1 color analysis of the liquor color of steamed tea samples at different temperatures
Taking 3g of the steamed tea samples of each tea variety experimental group and the control group respectively, putting the steamed tea samples into conical bottles with covers, adding 150mL of boiling ultrapure water, and filtering after 5 min. After the filtrate was cooled to room temperature, 10mL of the filtrate was used for color difference meter analysis. A CM-3600A type color difference meter manufactured by Konica Minolta, Japan was used. During the test, the tea soup to be tested is poured into a colorimetric vessel special for a color difference meter, and the brightness L and the chroma a and b are detected. The color space is measured in absolute terms (laxa b color difference system). The value range of the brightness L is 0-100, and the larger the value is, the higher the brightness is. For a, "+" indicates a red component and "-" indicates a green component; for b, "+" indicates a yellow component and "-" indicates a blue component. Each sample was tested in 10 replicates.
Analysis of tea soup color parameters L, a, b shows that the tea soup brightness L of each variety shows an overall rising trend along with temperature reduction (fig. 1). Except for the medium tea 102 and the Longjing tea 43, the brightness of the rest 4 varieties of tea soup is highest at the temperature of minus 16 ℃, and the brightness of the two varieties is highest at the temperature of minus 5 ℃; the brightness of Pingyang super-early and middle tea 102 in the control samples of different varieties is similar, the brightness of Luo-Luo and Longjing 43 is similar, and the brightness of middle tea 108 is highest. The tea soup chroma a of each variety at different temperatures is negative, and green chroma is represented; except for the medium tea 102, the green chroma a of each variety is obviously deepened along with the reduction of the temperature; the green shade of tea 102 was highest in the control of the different varieties. Except for the medium tea 102, the liquor colors of the other 5 varieties are obviously deepened along with the reduction of the temperature and the yellow chroma b; the yellow chroma of tea 102 in the control samples of different varieties is the highest, while the yellow chroma of Pingyang super early, Wuniu early and Longjing 43 is lower. The visible chromaticity value can be reflected sensitively to the change of the temperature.
According to the analysis of the tea soup color difference derivative index b/a (fig. 2a), the color difference derivative indexes of various varieties show a descending trend along with the reduction of the temperature. Except Pingyang super-early and Luo-mao, the other 4 varieties all rise after a sudden drop at 4 ℃; the derivative indexes of the Longjing 43 and the Wuniu early color difference are reduced along with the temperature, the overall change trend is more consistent, and the phenomena of reduction, increase and reduction are presented; the extreme early Pingyang and the overall change trend of the mao green are consistent, the change is gentle in the temperature range above 0 ℃, and the sudden drop occurs below 0 ℃; both middle tea 102 and middle tea 108 have a tendency to suddenly rise at 10 ℃ and suddenly drop at 4 ℃ and then slowly fall.
According to the analysis of the tea soup saturation C (figure 2b), the overall change trend of the saturations of the other varieties except the Longjing 43 is increased along with the reduction of the temperature, wherein the Pingyang super-early and the Malachite green change trends are consistent; the saturation difference of the same variety at different temperatures is large, and the difference between different varieties is obvious, so that the colorimeter can show the slight difference between the soup colors, the soup colors can be effectively distinguished, and the color change of the tea soup processed at different temperatures can be effectively and accurately reflected.
DPS software is adopted to perform cluster analysis on the 3 index data of the color colorimeter of each variety at different temperatures, Euclidean distance is taken as a cluster distance, and a dispersion square sum method is adopted to perform cluster analysis on the index data (figure 3). The result shows that the control tea samples of 6 varieties are obviously distinguished from the low-temperature treated tea samples, particularly the Pingyang is extremely early, and compared with other samples, the control samples are independently gathered into one type, so that the distinguishing is very obvious. The Longjing tea 43 and the Zhongcha tea 108 are classified at 0 deg.C, at 0 deg.C or higher, and at 0 deg.C or lower.
Example 2 aroma analysis of steamed tea samples at different temperatures
And (3) taking 2g of the steamed green tea sample into a 100mL beaker, sealing the beaker by using a preservative film, standing the beaker in a 50 ℃ oven for 10min, and taking out the beaker for detection. GEMINI electron developed by French Alpha MOS corporationThe nose is provided with 6 metal oxide sensors (T70/2, PA/2, P30/1, P30/2, LY2/AA, LY 2/gCT). The machine is started up 48 hours before data acquisition, the gas-carrying generator is opened to introduce airflow, and the sensor is slowly promoted to reach a balance state. The carrier gas is dry clean air, and the flow rate of the carrier gas is 150 mL/min-1. The data acquisition time is 90s, the delay time is 210s, and the maximum response value of each sensor is selected for statistical analysis. Each sample was tested in 5 replicates.
The response values of various tea sample aroma sensors at different temperatures have obvious difference along with the temperature change through an electronic nose sensor radar chart. The 6 sensors responded to a variety of tea-like aromas to varying degrees, with P40/1 being the highest, followed by PA/2 and P30/1, and the lowest being LY2/AA and LY 2/gCT. The dispersion of the response value of the sensor P30/1 is large, and the difference between samples is obvious. The LY2 series of sensors responds very intensively.
In Principal Component Analysis (PCA) and Discriminant Factor Analysis (DFA) (FIGS. 5a and 5b), the control sample data of each variety analyzed by PCA are relatively concentrated, the span is small, and the stability is good; at-16 ℃, the data span of each variety is large, and then the data span is-5 ℃; on the contrary, the data span of each variety is small at 0 ℃, and the data among the varieties and in the varieties are concentrated; the conditions of stability and large difference between varieties under the same variety span are shown at 10 ℃ and 4 ℃; the difference between varieties is large at minus 10 ℃. The fragrance difference between the control sample and the 0 ℃ treatment sample is not obvious. DFA analysis shows that more data are concentrated, the overlapping degree is high, and the data are difficult to distinguish. Overall, -16 ℃ and-5 ℃ have a greater impact on the aroma of the various tea samples, whereas 10 ℃ and 4 ℃ only have an impact on the aroma of mugwort morning, mao green and midge tea 102, the three tea-like aromas being slightly less stable to temperature changes.
The DPS software is adopted to perform cluster analysis on the response values of the 6 sensors of the electronic nose with different temperatures of each variety, the Euclidean distance is taken as the clustering distance, and the clustering analysis is performed by adopting the sum of squared deviations method (figure 6). The result shows that the control tea samples of 6 varieties are obviously distinguished from the low-temperature treatment tea samples, and particularly, the control samples of the Wuniao, the Longjing tea 43, the Zhongcha tea 102 and the Zhongcha tea 108 are independently gathered into one type, so that the distinguishing is very obvious. While the brilliant green varieties are classified into 0 ℃ as a boundary, 0 ℃ and above are classified into one type, and below 0 ℃ is classified into one type, wherein the control samples are classified into a small type independently in the 0 ℃ and above clusters. Pingyang was early on, and was classified into those at 4 ℃ and below.
Example 3 taste analysis of steamed tea-like soup at different temperatures
Taking 3g of the steamed green tea sample, putting the steamed green tea sample into a conical flask with a cover, adding 150mL of boiling ultrapure water, and filtering after 5 min. The filtrate was cooled to room temperature and 100mL of the filtrate was used for electronic tongue analysis. An Astree type electronic tongue developed by French Alpha MOS company is adopted and is provided with a 1# sensor (ZZ, JE, BB, CA, GA, HA and GB) and 1 Ag/AgCl reference electrode. Before data acquisition, the system needs to be initialized, calibrated, diagnosed and the like to ensure the reliability and stability of the response signals of the sensors. The data acquisition time is set to be 120s, the stirring speed is set to be 1 time/s, and the average value of the last 20s measured values is taken as the response value of the sensor. And the sensor enters the cleaning solution to be cleaned for 1 time every time the sample is sampled for 1 time, so that the influence on the response signal of the next sample is avoided. Each sample was repeated 10 times.
To visually analyze the response of the electronic tongue sensors to different tea soup samples, the data mean of each sensor was evenly arranged on the circumference (fig. 7). The response value distribution dispersion of 7 sensors is large. The JE sensors are relatively discrete. Compared with BB and HA, the response values of the CA sensor, the JE sensor, the JB sensor, the GA sensor and the ZZ sensor to the sample are greatly different, which indicates that the 5 sensors have higher sensitivity to certain substances in the sample, and indirectly indicates that the sensors have better distinguishing effect on the taste of the tea soup.
Principal component analysis and discriminant factor analysis are multivariate statistical methods for researching information in all aspects of the whole by integrating a plurality of variables into a few comprehensive variables on the premise of less loss of information. The closer the sample distance, the more similar the quality characteristics. The electronic tongue can accurately and effectively detect the tea soup flavor difference of 42 samples in total at 7 different treatment temperatures of 6 tea plant varieties (figure 8). PCA analysis shows that intraspecies differences are mainly distinguished according to PC2, interspecies differences are mainly distinguished according to PC1, and the differences caused by low-temperature treatment on the flavors of different varieties of tea soup are obvious. The taste of the control sample of each variety is obviously different from the taste of the tea soup processed at the temperature in the variety, and the taste of the tea soup in the variety is linearly distributed; the distance between the temperatures of the Longjing tea 43 is far, which shows that the tea soup taste has obvious difference at different temperatures; the samples of the maoLu and Zhongcha 108 treated at the temperature of 10 ℃ and below are relatively concentrated and are far away from each control sample, which shows that the taste quality of the 2 varieties is obviously deteriorated once the varieties are subjected to low temperature; the taste of Pingyang super-early and Wuniu early are similar, especially in the range of 0-10 ℃, the 2 varieties have overlapping phenomenon, which shows that in the temperature range, the taste of Pingyang super-early and Wuniu early is consistent. DFA analysis showed that within-group differences decreased and between-group differences increased. The difference of each variety is more consistent with the PCA result.
And (3) performing cluster analysis on the response values of the 7 sensors of the electronic tongue with different temperatures of each variety by adopting DPS software, taking the Euclidean distance as a clustering distance, and performing cluster analysis on the response values by adopting a deviation square sum method (figure 9). The results show that various steamed green samples are obviously divided into two categories, wherein the comparison samples of the maoLu, Longjing 43 and the Chinese tea 102 are obviously distinguished from the samples stressed by low temperature, and the comparison samples of other categories are gathered into one category with the samples above 0 ℃.
Example 4 sensory evaluation of steamed tea sample quality at different temperatures and correlation analysis with an Intelligent Instrument
A total of 42 samples of 6 varieties at 7 temperatures were subjected to sensory evaluation according to the national standards for sensory evaluation (GB/T23776-2018). The sensory evaluation score and the data of the electronic tongue, the electronic nose and the chromatic aberration meter are found through Spearman correlation analysis (table 1), and the electronic tongue, the electronic nose and the chromatic aberration meter have strong positive correlation with the sensory evaluation. In terms of taste, the 5 sensors other than JE and HA were significantly or very significantly related to the electronic tongue. In terms of aroma, 4 sensors other than the LY2 series of sensors showed very significant correlation with the electronic nose. In terms of the soup color, it appears to be significantly or very significantly correlated with L and a.
Because the electronic tongue, the electronic nose and the color difference meter have strong positive correlation with sensory evaluation, the method for constructing the tea quality recognition model under low temperature stress by adopting the detection signal values of the intelligent instruments such as the electronic tongue, the electronic nose and the color difference meter is scientific and accurate.
TABLE 1 correlation of sensory evaluation scores with electronic tongue, electronic nose and colorimeter
Example 5 Integrated analysis
According to the obtained results, the electronic tongue, the electronic nose and the chromatic aberration meter can detect 6 samples of 7 kinds and 7 temperatures. And (3) carrying out normalization processing on the data, screening characteristic values, deriving a linear fitting equation based on a stepwise regression method, and constructing a tea quality identification model under low temperature stress.
The tea quality recognition model under low temperature stress finally constructed in the embodiment is as follows:
T＝7.478X1-14.207X2+0.075X3+0.067X4-0.107X5+0.073X6+0.056X7+0.088X8-353.209 notes: temperature T (. degree. C.), X1Is the color difference parameter a, X2Is an electronic nose sensor P30/1, X3-X8Respectively, electronic tongue sensors ZZ, BB, CA, GA, HA and JB.
The model has extremely high identification accuracy on the freeze injury tea leaves and the normal tea leaves, can accurately and respectively identify the freeze injury tea leaves and the normal tea leaves, can also roughly identify the freeze injury degree of the tea leaves, and is worthy of attention, the model and the method can be used for identifying the green tea leaves except Wuniao, Pingyangte super-early, MaoLuo, Chinese tea 102, Longjing 43 and Chinese tea 108, and can also accurately identify the freeze injury tea leaves and the normal tea leaves. The model has certain universality and can be popularized to the identification of the freezing injury of the green tea, which also shows that the modeling characteristic parameters selected by the method better include the sensory difference of the freezing injury tea compared with the common tea. The model identification method also has the advantages of good repeatability, no need of complex sample pretreatment process, no occurrence of sensory fatigue, objective and reliable detection result and the like.
The above description is only a few embodiments of the present invention, and is not intended to limit the present invention in any way, and the modifications, optimizations, equivalent changes and modifications of the above embodiments according to the technical spirit of the present invention are all within the technical scope of the present invention.
Claims (10)
1. A method for identifying the quality of freeze injury tea is characterized by comprising the following steps:
1) selecting a plurality of tea tree varieties, collecting one bud of three leaves in current-year branches which are free from plant diseases and insect pests and have consistent growth vigour, washing the three leaves clean with purified water, drying surface water by using filter paper, setting N different experimental groups for low-temperature stress treatment for each tea tree variety, wherein the low-temperature stress treatment temperature is less than or equal to 10 ℃, and taking naturally-growing tea tree leaves as a reference; preparing tea samples of N experimental groups and a control group into steamed green samples, uniformly mixing each steamed green sample, dividing into 4 parts according to a quartering method, and taking 1 part of the steamed green samples for detection;
2) detecting the color of the tea soup by using a color difference meter, and measuring color parameters of brightness L, chroma a and chroma b;
3) detecting the aroma of the tea leaves by using an electronic nose, and measuring a response value of a sensor;
4) detecting the taste of the tea soup by using the electronic tongue, and measuring the response value of the sensor;
5) constructing a freezing injury tea quality identification model through the parameters of the color difference meter, the response value of the electronic nose sensor and the response value data of the electronic tongue sensor;
6) selecting tea leaves to be identified, making the tea leaves into steamed green samples, obtaining color difference meter parameters, response values of an electronic nose sensor and response values of an electronic tongue sensor through the steps 2) to 4) as model inputs, and identifying whether the tea leaves are frozen and/or identifying the quality of the frozen tea leaves by using the frozen tea leaf quality identification model established in the step 5).
2. The method of claim 1, wherein the colorimeter has a model number CM-3600A.
3. The method as claimed in claim 1, wherein the tea soup in step 2) is prepared by GB/T23776 2018.
4. The method of claim 1, wherein the electronic nose is GEMINI, and comprises 6 metal oxide sensors T70/2, PA/2, P30/1, P30/2, LY2/AA, LY 2/gCT.
5. The method according to claim 1, wherein in the step 3), the sample for the electronic nose test is prepared by taking 2g of steamed sample into a 100mL beaker, sealing the beaker with a preservative film, standing the beaker in an oven at 50 ℃ for 10min, and taking out the beaker for test.
6. The method according to claim 1 or 5, wherein the detection method of the electronic nose is: dry clean air is used as carrier gas, and the flow rate of the carrier gas is 150 mL/min-1(ii) a The data acquisition time is 90s, the delay time is 210s, and the maximum response value of each sensor is selected for statistical analysis.
7. The method according to claim 1, characterized in that the electronic tongue is of the Astree type, equipped with 1 Ag/AgCl reference electrode and 1# sensors ZZ, JE, BB, CA, GA, HA, GB.
8. The method according to claim 1, wherein the electronic tongue is detected by setting the data acquisition time to 120s, the stirring rate to 1 time/s, and taking the average of the last 20s measurements as the response value of the sensor; the sensor was washed 1 time into the wash solution for each 1 sample.
9. The method according to claim 1, wherein the freezing injury tea quality identification model is constructed by normalizing data, screening characteristic values and deriving a linear fitting equation based on a stepwise regression method.
10. The method according to claim 1, wherein in the step 6), the freezing injury tea leaf quality identification model constructed in the step 5) is used for identifying whether the tea leaves are frozen or not, and specifically comprises the following steps: and inputting a colorimeter parameter, an electronic nose sensor response value and an electronic tongue sensor response value into the freezing injury tea quality identification model, outputting a predicted processing temperature as a model output result, and if the predicted processing temperature is less than or equal to 10 ℃, determining that the tea sample to be detected is frozen.
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