阅读说明：本技术 一种基于电子鼻的六堡茶陈香香味识别方法 (Electronic nose-based Liupao tea old fragrance identification method ) 是由 温立香 张芬 冯春梅 何梅珍 黄寿辉 彭靖茹 袁冬寅 陈家献 林家威 于 2020-07-21 设计创作，主要内容包括：本发明涉及茶叶检测技术领域,尤其是一种基于电子鼻的六堡茶陈香香味识别方法,本申请利用电子鼻进行六堡茶陈香香味识别,具体是选取合适的六堡茶样品,并通过特定方式采集香气,并对电子鼻各传感器所采集的响应特征值进行统计分析,并以此构建基于样本聚散程度的区分数据模型,通过LDA(线性判别函数分析法)利用标样建立区分数据模型,将待测样品的电子鼻响应信号与模型进行比较分析,完成识别和判断,操作简单,结果稳定可靠,本申请的方法能够较为准确的识别具有陈香的六堡茶,且能对尚未形成陈香的厂家茶和农家茶进行一定程度的区分。(The invention relates to the technical field of tea detection, in particular to a Liupao tea old fragrance and fragrance identification method based on an electronic nose.)

1. A Liupao tea old fragrance identification method based on an electronic nose is characterized by comprising the following steps:

(1) and (3) sensory evaluation of samples: taking Liupao tea, evaluating the samples according to a tea sensory evaluation standard method and classifying the samples into different groups according to evaluation results;

(2) sample odor data acquisition pretreatment: respectively weighing a dry tea sample, a tea soup and a tea bottom sample, processing, performing headspace enrichment, and selecting the same or various detections for judgment;

(3) collecting smell data: after the sample is processed according to the step (2), carrying out sample odor data acquisition by using an electronic nose to obtain a change curve and a response value of 10 sensors of the electronic nose responding to the volatile substances of the tea leaves along with the acquisition time;

(4) extracting aroma characteristic values: taking 3-5 response values of the sensor response curves in the stage of flattening and stabilizing as the aroma response characteristic values of the sample;

(5) Establishing a fragrance identification model: comparing with the classification result of sensory evaluation of a standard training sample, performing statistical analysis on the aroma response characteristic value extracted in the step (4) by utilizing LDA linear discriminant analysis, and establishing an aroma identification model to obtain a model distinguishing effect graph;

(6) detection of a target sample: taking a Liupao tea sample to be detected, processing according to the method in the step (2), collecting odor data according to the method in the step (3), then carrying out fragrance identification and judgment on the electronic nose response characteristic value through a DFA (distributed feed forward) judgment function analysis method, comparing with the model distinguishing effect diagram obtained in the step (5), and observing which region the odor characteristic curve of the target sample finally falls in the model, namely indicating that the odor characteristic of the sample is similar to that of the class to which the sample belongs, if the odor characteristic curve falls in a stale aroma region, judging that the sample belongs to a stale aroma type, if the odor characteristic curve falls in a stale aroma region, judging that the sample belongs to a non-stale aroma type.

2. The electronic nose based Liupu tea stale flavor identification method according to claim 1, wherein the classifying of the samples into different groups in step (1) is classifying the samples into two different groups, a stale flavor free group and a stale flavor group.

3. The Liupao tea stale flavor identification method based on the electronic nose as claimed in claim 1, wherein the sample odor data collection pretreatment in the step (2) is specifically as follows: a. dry tea sample: weighing 3g of dry tea sample, filling into a headspace enrichment sample injection bottle, standing for 15-20min, and waiting for detection; b. tea soup and tea bottom samples: weighing 3g of dry tea sample, placing the dry tea sample in a beaker, adding 50-100ml of boiling distilled water to quickly wash the tea for 5-10s, filtering out tea washing water, and mixing the dry tea sample with the distilled water according to the tea water ratio of 1: 50, adding boiling distilled water for brewing for 5min, filtering out tea soup, respectively filling the tea soup and the tea bottom into a headspace enrichment sample injection bottle, standing to 45-55 ℃, and detecting to obtain the dry tea sample, the tea soup sample and the tea bottom sample, wherein the dry tea sample, the tea soup sample and the tea bottom sample can be detected to be the same or various for judgment.

4. The Liupao tea stale flavor identification method based on the electronic nose as claimed in claim 3, wherein in the step (3), the electronic nose is used for sample odor data collection, and the electronic nose collection needle and the air supply needle are inserted into the headspace enrichment sample injection bottle, and the electronic nose is started for sample odor data collection.

5. The electronic nose-based Liupao tea stale flavor identification method according to any one of claims 3 and 4, wherein the headspace enrichment sample bottle is a sample bottle with a bottle cap provided with a silica gel gasket.

6. The Liupao tea stale flavor identification method based on the electronic nose as claimed in claim 4, wherein in step (3), the air replenishing needle is inserted into the collection bottle at a position deeper than the collection needle.

7. The Liupao tea stale flavor identification method based on the electronic nose as claimed in claim 1, wherein the electronic nose is of a metal oxide type, there are 10 groups of highly sensitive metal oxide gas sensors, each group of sensors has different sensitivity to different types of gas, specifically: W1C, benzene species; W5S, nitrogen oxides; W3C, ammonia species; W6S, hydrides; W5C, short chain alkanes; W1S, methyl-like; W1W, inorganic sulfides; W2S, alcohols, aldones; W2W, organic sulfides; W3S, long chain alkanes.

8. The Liupao tea stale flavor identification method based on the electronic nose as claimed in claim 7, wherein the analysis condition parameters of the electronic nose are as follows: sample preparation time: 5 s; automatic zero setting time: 10 s; sample introduction flow rate: 400 ml/min; data acquisition interval time 1s, cleaning time: 60s, data acquisition time: 80-120 s.

9. The Liupao tea stale flavor identification method based on the electronic nose according to claim 1, wherein the step (5) is to verify the flavor identification model after establishing the flavor identification model and obtaining a model distinguishing effect map.

10. The Liupao tea stale flavor identification method based on the electronic nose as claimed in claim 9, wherein the verification of the flavor identification model is carried out by verifying the accuracy of the model through DFA discriminant function analysis by using the electronic nose response characteristic value of a verification sample, statistically calculating the identification accuracy of the dry tea, tea soup and tea bottom identification models to the verification sample, and further verifying whether the model is correct or not so as to avoid error.

Technical Field

The invention relates to the technical field of tea detection, in particular to a Liupao tea old fragrance identification method based on an electronic nose.

Background

The Liupu tea is important export tea in Guangxi, is also main Qiaoyuan tea in China, is deeply loved by consumers by the characteristics of red, thick, old, mellow and unique quality, and is a tea product with national geographic marks and strong local characteristics in Guangxi. The Liupu tea is formed by gradually converting Liupu raw tea after pile fermentation and aging, although many people know that Liupu tea has the 'old fragrance' which is good tea, the Liupu tea does not know what the Liupu tea has the 'old fragrance', even many consumers fuzzily think that the 'old fragrance' of all black tea is the same for a long time, the aroma characteristics and the characteristic components of different black tea are different, and the expressed sensory perception is different.

From the consumption aspect, at present, no scientific judgment standard exists for the 'old fragrance' in the market, so that the phenomenon that the product name is disordered and is good enough in the market sometimes occurs, and more operators cheat consumers in order to obtain high profits and take the raw tea which does not form the 'old fragrance'; from the research aspect, the sensory evaluation adopted at present is lack of quantitative recognition, the accuracy is influenced by subjective factors such as sensory sensitivity and hobbies of an evaluation and auditor and a plurality of external factors such as environmental conditions and working procedures, the pretreatment of chemical detection is complex and low in efficiency, the aroma components of tea leaves are many and complex and volatile, various reactions are easy to occur in the extraction process, and the collection and extraction of aroma substances greatly influence the effectiveness of the chemical analysis method for the aroma of the tea leaves, so that a means which is standardized and easy to operate and can scientifically and objectively evaluate the old aroma of the Liupu tea is necessary to be explored.

The bionic Electronic Nose (EN) is a new flavor analysis technology, can simulate the human olfactory system to directly carry out quantitative analysis on olfactory sensory indexes of a sample, is different from a common chemical instrument, obtains qualitative and quantitative results of components of the sample to be detected, and objectively evaluates the whole condition of the sample according to the whole information of volatile components in the sample, so the bionic electronic nose is called as an odor fingerprint analyzer. At present, the application of an electronic nose of a bionic instrument to tea quality analysis is the main direction of current research, for example, patent CN201610186784.0 (a method for judging aroma grade of pekoe silver needle based on electronic nose detection information) judges the aroma grade of pekoe silver needle by using the electronic nose, but because the old aroma of Liupu tea is different from other black tea, the Liupu tea is complicated and volatile due to a plurality of aroma components, and cannot be easily analyzed by the electronic nose, and at present, a method for scientifically and objectively evaluating the old aroma of Liupu tea does not exist.

In view of the above situation, it is urgent to establish a Liupu tea recognition method based on an electronic nose, which can accurately recognize Liupu tea with old fragrance, can distinguish factory tea and farmhouse tea which do not form old fragrance to a certain extent, is standardized and easy to operate, and can scientifically and objectively evaluate Liupu tea old fragrance recognition method.

Disclosure of Invention

In order to solve the technical problems in the prior art, the invention provides a Liupao tea old fragrance identification method based on an electronic nose, which is realized by the following technical scheme:

a Liupao tea old fragrance identification method based on an electronic nose comprises the following steps:

(1) and (3) sensory evaluation of samples: taking Liupao tea, evaluating the samples according to a tea sensory evaluation standard method, and classifying the samples into different groups according to evaluation results, wherein the evaluation method specifically comprises the following steps: taking Liupao tea which is formed and does not form old fragrance, and evaluating a sample by an evaluating person with tea evaluation occupational qualification according to a tea sensory evaluation standard method and classifying the sample into a non-old fragrance group and a old fragrance group according to evaluation results;

(2) sample odor data acquisition pretreatment: respectively weighing a dry tea sample, a tea soup and a tea bottom sample, processing, performing headspace enrichment, and selecting the same or various detections for judgment; the method comprises the following steps: a. dry tea sample: weighing 3g of dry tea sample, filling into a headspace enrichment sample injection bottle, standing for 15-20min, and waiting for detection; b. tea soup and tea bottom samples: weighing 3g of dry tea sample, placing in a beaker, adding 50-100ml of boiling distilled water to quickly wash the tea for 5-10s (removing the foreign odor generated due to long-term aging), filtering out tea washing water, and mixing the tea washing water with the water-soluble organic solvent according to the tea water ratio of 1: 50, adding boiling distilled water for brewing for 5min, filtering out tea soup, respectively filling the tea soup and the tea bottom into a headspace enrichment sample injection bottle, standing to 45-55 ℃, detecting to be detected, detecting the same or various dry tea samples, tea soup samples and tea bottom samples for judgment, so that the tea soup sample can be suitable for various environments, such as inconvenient brewing identification, only the tea bottom can be used for identification and the like;

(3) Collecting smell data: after the sample is processed according to the step (2), carrying out sample odor data acquisition by using an electronic nose, specifically, inserting an electronic nose acquisition needle and an air supply needle into a headspace enrichment sample introduction bottle, starting the electronic nose to carry out sample odor data acquisition, and obtaining a change curve and a response value of 10 sensors of the electronic nose responding to the volatile substances of the tea leaves along with the acquisition time, as shown in fig. 1;

(4) extracting aroma characteristic values: taking 3-5 response values of the sensor response curves in the stage of flattening and stabilizing as the aroma response characteristic values of the sample;

(5) establishing a fragrance identification model: and (3) comparing with the classification result of sensory evaluation of the standard training sample, and carrying out statistical analysis on the aroma response characteristic value extracted in the step (4) by utilizing LDA linear discriminant analysis, wherein the statistical analysis result is as follows: the first discriminant LDA1 of the dry tea, the tea soup and the tea bottom respectively reach 85.24%, 97.14% and 90.53%, the second discriminant LDA2 respectively reaches 1.85%, 0.36% and 1.18%, the total contribution rates of the two discriminant distinguishing samples respectively reach 87.09%, 97.50% and 91.72% of the dry tea, the tea soup and the tea bottom, the main information characteristics of the samples are already covered, and the fact that Liupao tea with stale aroma and the stale aroma are feasible to construct a stale aroma and aroma identification model based on the different characteristic sample aggregation degrees by an LDA statistical analysis method, the aroma identification model is established, and a model distinguishing effect diagram is obtained, particularly as shown in FIGS. 2-4;

(6) Detection of a target sample: taking a Liupao tea sample to be detected, processing according to the method in the step (2), collecting odor data according to the method in the step (3), then carrying out fragrance identification and judgment on the electronic nose response characteristic value through a DFA (distributed feed forward) judgment function analysis method, comparing with the model distinguishing effect diagram obtained in the step (5), and observing which region the odor characteristic curve of the target sample finally falls in the model, namely indicating that the odor characteristic of the sample is similar to that of the class to which the sample belongs, if the odor characteristic curve falls in a stale region, judging that the sample belongs to a stale type, if the odor characteristic curve falls in a stale region, judging that the sample belongs to a non-stale type.

Further, the set without old aroma is new tea with old aroma not formed yet.

Furthermore, the headspace enrichment sample injection bottle is a sample bottle with a silica gel gasket on the bottle cap, so that the collection needle and the air supply needle can be directly inserted into the sample bottle for fragrance data collection.

Furthermore, in the step (3), the position of the air replenishing needle inserted into the collection bottle is deeper than that of the collection needle so as to ensure that gas circulation is formed and more sample odor information is collected during testing.

Further, the electron nose is the metal oxide type, has 10 high sensitive metal oxide gas sensor of group, and every group sensor has different sensitivity to different grade type gas, specifically does: W1C, benzene species; W5S, nitrogen oxides; W3C, ammonia species; W6S, hydrides; W5C, short chain alkanes; W1S, methyl-like; W1W, inorganic sulfides; W2S, alcohols, aldones; W2W, organic sulfides; W3S, long-chain alkanes, wherein the analysis condition parameters of the electronic nose are as follows: sample preparation time: 5 s; automatic zero setting time: 10 s; sample introduction flow rate: 400 ml/min; data acquisition interval time 1s, cleaning time: 60s, data acquisition time: 70-100 s. When the sample injection flow is too large or too small (less than 300ml/min or more than 500 ml/min), the response degree of each sensor is weak (the response value is small), the response degree is strong along with the increase of the air inflow when the air inflow is 400-500ml/min, but the time for reaching balance is longer, the selection of 400ml/min in the experiment is comprehensively considered, the selection of each parameter integrates the factors of the response sensitivity, the peak value, the time for reaching balance and the like of the sensor, the aroma enrichment effect is good under the condition of the parameter, and the aroma response curve of the tea sample reaches balance in the stage of 70-100 s.

And (5) after a fragrance identification model is established and a model distinguishing effect graph is obtained, verifying the fragrance identification model, specifically verifying the accuracy of the model by using the electronic nose response characteristic value of a verification sample through a DFA discriminant function analysis method, counting and calculating the identification accuracy of the dry tea, tea soup and tea bottom identification model to a verification sample, and further verifying whether the model is correct or not so as to avoid error conditions, wherein the verification sample is obtained by randomly extracting a part of samples from two different groups of the non-old fragrance group and the old fragrance group in the step (1) and recoding the samples to serve as the verification sample. Further, after a part of samples are randomly extracted from the two different groups of the non-aging-fragrance group and the aging-fragrance group in the step (1) respectively to be recoded as verification samples, and the rest samples are continued to be processed downwards according to the step (2).

Furthermore, the method can identify whether the Liupu tea has the old fragrance or not, and can also construct an identification model of the rich degree of the old fragrance according to the method to judge the rich degree of the old fragrance of the Liupu tea.

Establishing a fragrance recognition model in the step (5), wherein the specific data analysis operation process is as follows:

1. Entering Winmaster data processing software for template editing, specifically referring to FIG. 11;

2. adding the collected signal data according to the classification result of sensory evaluation of the standard training sample, specifically referring to fig. 12;

firstly, confirming the collection time range selected by the characteristic value and the classification result of sensory evaluation of the standard training sample, and particularly referring to fig. 13;

adding a data file for establishing a model, specifically shown in FIG. 14;

thirdly, importing data for establishing the model, specifically as shown in FIG. 15;

setting the characteristics of imported data, particularly as shown in FIGS. 16-19;

3. the LDA analysis method in the software was chosen for data modeling, see fig. 20 in particular.

The step (5) of verifying the fragrance recognition model comprises the following specific operation processes:

1. open validation sample data, see fig. 21 in particular;

2. decision Function Analysis (DFA) is selected for result determination, see fig. 22 in particular;

3. and obtaining a result judgment visual chart, and referring to fig. 23 for a specific example.

Drawings

FIG. 1 is an EN response curve, response value and radar plot of dried tea, tea soup, leaf base;

FIG. 2 is a LDA distinguishing effect graph established by the response characteristic value of the dry tea electronic nose;

FIG. 3 is a LDA distinguishing effect graph established by the response characteristic value of the tea soup electronic nose;

FIG. 4 is an LDA distinguishing effect graph established by the response characteristic value of the electronic nose at the tea bottom;

FIG. 5 is a sample verification example with stale scent;

FIG. 6 is a graph of sample validation without stale aroma;

FIG. 7 is a schematic view of a biomimetic nasal structure;

FIG. 8 is a graph showing the effect of PCA analysis of Liupao tea dry tea, tea soup and leaf bottom;

FIG. 9 is a Liupao tea soup load (loads) graph;

FIG. 10 is a graph showing the results of a farmer's tea aged for 2 years without showing stale aroma;

FIG. 11 is a schematic diagram of a template editing operation into Winmaster data processing software;

FIG. 12 is a schematic representation of the operation of adding collected signal data according to the classification results of sensory evaluation of standard training samples;

FIG. 13 is an operational view of the classification results of sensory evaluation of standard training samples for a selected time range of collection of confirmation feature values;

FIG. 14 is a schematic view of the operation of adding modeled data files;

FIG. 15 is a data manipulation diagram of an import build model;

FIG. 16 is a schematic diagram of the characteristic operations of setting import data;

FIG. 17 is a schematic diagram of a characteristic operation of setting import data;

FIG. 18 is a schematic diagram of the characteristic operations of setting import data;

FIG. 19 is a schematic diagram of the characteristic operations of setting import data;

FIG. 20 is a schematic diagram of an LDA analysis method in selected software for data modeling operations;

FIG. 21 is a schematic view of the operation of opening validation sample data;

FIG. 22 is a schematic diagram of (2) a decision operation for selecting a Discriminant Function Analysis (DFA) result;

fig. 23 is a view illustrating an example of result determination.

Compared with the prior art, the invention has the technical effects that:

the method utilizes the electronic nose to recognize the old fragrance and the flavor of Liupu tea, particularly selects a proper Liupu tea sample, collects the fragrance in a specific mode, statistically analyzes response characteristic values collected by sensors of the electronic nose, constructs a distinguishing data model based on the sample gathering and scattering degree, utilizes a standard sample to establish the distinguishing data model through LDA (linear discriminant function analysis), compares and analyzes the electronic nose response signal of a sample to be detected and the model, completes recognition and judgment, has simple operation and stable and reliable results, has the recognition accuracy rates of unknown samples of 84.62%, 92.31% and 82.05%, wherein the recognition accuracy rate of tea soup is more than 90%, and shows that the method can more accurately recognize Liupu tea with old fragrance and can distinguish factory tea and farmhouse tea which does not form old fragrance to a certain degree, the Liupao tea identifying method has the advantages that Liupao tea with old fragrance can be identified accurately, factory tea and farmhouse tea which do not form old fragrance can be distinguished to a certain degree, standardization and easy operation are realized, and the Liupao tea identifying method can be used for scientifically and objectively evaluating the old fragrance of Liupao tea.

Detailed Description

The technical solution of the present invention is further defined below with reference to the specific embodiments, but the scope of the claims is not limited to the description.