Discrimination of LongJing green-tea grade by electronic nose

2007 ◽  
Vol 122 (1) ◽  
pp. 134-140 ◽  
Author(s):  
Huichun Yu ◽  
Jun Wang
Keyword(s):  
Green Tea ◽  
Electronic Nose

scholarly journals Characterization of the effect of cis-3-hexen-1-ol on green tea aroma

2020 ◽  
Vol 10 (1) ◽  
Author(s):  
Cong-ning Nie ◽  
Yuan Gao ◽  
Xiao Du ◽  
Jin-lin Bian ◽  
Hui Li ◽  
...  
Keyword(s):  
Green Tea ◽  
Electronic Nose ◽  
Chemometric Analysis ◽  
Differential Threshold ◽  
Value Evaluation ◽  
Odor Activity Value ◽  
Aroma Components ◽  
Green Odor

Abstract cis-3-Hexen-1-ol has been regarded as the main source of green aroma (or green odor) in green tea. However, no clear findings on the composition of green aroma components in tea and the effect of cis-3-hexen-1-ol on other aroma components have been reported. In this study, the main green aroma components in green tea were characterized, especially the role of cis-3-hexen-1-ol in green aroma was analyzed and how it affected other aroma components in green tea was studied. Based on the GC–MS detection, odor activity value evaluation, and monomer sniffing, 12 green components were identified. Through the chemometric analysis, cis-3-hexen-1-ol was proven as the most influential component of green aroma. Moreover, through the electronic nose analysis of different concentrations of cis-3-hexen-1-ol with 25 other aroma components in green tea, we showed that the effect of cis-3-hexen-1-ol plays a profound effect on the overall aroma based on the experiments of reconstitution solution and natural tea samples. GC–MS and CG-FID confirmed that the concentration range of the differential threshold of green odor and green aroma of cis-3-hexen-1-ol was 0.04–0.52 mg kg−1.

Quality level identification of West Lake Longjing green tea using electronic nose

2019 ◽  
Vol 301 ◽  
pp. 127056 ◽  
Author(s):  
Xiaohui Lu ◽  
Jin Wang ◽  
Guodong Lu ◽  
Bo Lin ◽  
Meizhuo Chang ◽  
...  
Keyword(s):  
Green Tea ◽  
Electronic Nose ◽  
Quality Level ◽  
West Lake ◽  
Level Identification

scholarly journals Detection of bitterness and astringency of green tea with different taste by electronic nose and tongue

PLoS ONE ◽  
2018 ◽  
Vol 13 (12) ◽  
pp. e0206517 ◽  
Author(s):  
Guangyu Zou ◽  
Yanzhong Xiao ◽  
Miaosen Wang ◽  
Hongmei Zhang
Keyword(s):  
Green Tea ◽  
Electronic Nose

scholarly journals A Machine Learning Method for the Fine-Grained Classification of Green Tea with Geographical Indication Using a MOS-Based Electronic Nose

Foods ◽  
2021 ◽  
Vol 10 (4) ◽  
pp. 795
Author(s):  
Dongbing Yu ◽  
Yu Gu
Keyword(s):  
Machine Learning ◽  
Green Tea ◽  
Electronic Nose ◽  
Classification Performance ◽  
Small Sample ◽  
Support Vector ◽  
Svm Classifier ◽  
Discrimination Ability ◽  
Fine Grained

Chinese green tea is known for its health-functional properties. There are many green tea categories, which have sub-categories with geographical indications (GTSGI). Several high-quality GTSGI planted in specific areas are labeled as famous GTSGI (FGTSGI) and are expensive. However, the subtle differences between the categories complicate the fine-grained classification of the GTSGI. This study proposes a novel framework consisting of a convolutional neural network backbone (CNN backbone) and a support vector machine classifier (SVM classifier), namely, CNN-SVM for the classification of Maofeng green tea categories (six sub-categories) and Maojian green tea categories (six sub-categories) using electronic nose data. A multi-channel input matrix was constructed for the CNN backbone to extract deep features from different sensor signals. An SVM classifier was employed to improve the classification performance due to its high discrimination ability for small sample sizes. The effectiveness of this framework was verified by comparing it with four other machine learning models (SVM, CNN-Shi, CNN-SVM-Shi, and CNN). The proposed framework had the best performance for classifying the GTSGI and identifying the FGTSGI. The high accuracy and strong robustness of the CNN-SVM show its potential for the fine-grained classification of multiple highly similar teas.

Detection of XinyangMaojian Green Tea Quality and Age by Electronic Nose

2011 ◽  
Vol 239-242 ◽  
pp. 2096-2100 ◽  
Author(s):  
Hong Mei Zhang ◽  
Ming Xun Chang ◽  
Yong Chang Yu ◽  
Hui Tian ◽  
Yu Qing He ◽  
...  
Keyword(s):  
Neural Network ◽  
Green Tea ◽  
Electronic Nose ◽  
Principal Component ◽  
Correct Identification ◽  
Tea Quality ◽  
Testing Data ◽  
Artificial Neural Network Ann ◽  
Correct Discrimination

In this work, the capacity of an electronic nose (E-nose, PEN2) to classify tea quality grades is investigated. Three tea groups with different quality grades were harvested at different times. Principal component analysis (PCA) and artificial neural network (ANN) were applied to identify the different tea samples. PCA provided perfect classification of tea quality grades. In the analysis of age, six groups of XinyangMaojian green tea were distinguished completely by PCA. The results of ANN analysis gave a high percentage of correct discrimination of green tea samples. The correct identification rates of the training and testing data were 98.6% and 83%, respectively, for three grades of green tea samples harvested in 2009. The correct identification rates of the training and testing data were 100% and 87.8%, respectively, for three grades of green tea samples harvested in 2010. In the analysis of age, the correct discrimination percentages for six groups of XinyangMaojian green tea were 99.4% and 88.9% for training and testing data, respectively. These results indicate that the electronic nose could be successfully used for the detection of teas of different quality grades and ages.

scholarly journals Optimization of Electronic Nose Sensor Array for Tea Aroma Detecting Based on Correlation Coefficient and Cluster Analysis

Chemosensors ◽  
2021 ◽  
Vol 9 (9) ◽  
pp. 266
Author(s):  
Jin Wang ◽  
Cheng Zhang ◽  
Meizhuo Chang ◽  
Wei He ◽  
Xiaohui Lu ◽  
...  
Keyword(s):  
Cluster Analysis ◽  
Correlation Coefficient ◽  
Green Tea ◽  
Electronic Nose ◽  
Sensor Array ◽  
Nearest Neighbor ◽  
Linear Discriminant ◽  
Average Value ◽  
And Cluster Analysis ◽  
Electronic Nose System

The electronic nose system is widely used in tea aroma detecting, and the sensor array plays a fundamental role for obtaining good results. Here, a sensor array optimization (SAO) method based on correlation coefficient and cluster analysis (CA) is proposed. First, correlation coefficient and distinguishing performance value (DPV) are calculated to eliminate redundant sensors. Then, the sensor independence is obtained through cluster analysis and the number of sensors is confirmed. Finally, the optimized sensor array is constructed. According to the results of the proposed method, sensor array for green tea (LG), fried green tea (LF) and baked green tea (LB) are constructed, and validation experiments are carried out. The classification accuracy using methods of linear discriminant analysis (LDA) based on the average value (LDA-ave) combined with nearest-neighbor classifier (NNC) can almost reach 94.44~100%. When the proposed method is used to discriminate between various grades of West Lake Longjing tea, LF can show comparable performance to that of the German PEN2 electronic nose. The electronic nose SAO method proposed in this paper can effectively eliminate redundant sensors and improve the quality of original tea aroma data. With fewer sensors, the optimized sensor array contributes to the miniaturization and cost reduction of the electronic nose system.

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