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DOI 10.1111/1750-3841.14917

Медицинская библиотека · Journal of food science · 2019

Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis.

Yuan Haibo, Chen Xiaoqiang, Shao Yundong, Cheng Yong, Yang Yanqin, Zhang Mingming, Hua Jinjie, Li Jia, Deng Yuliang, Wang Jinjin, Dong Chunwang, Jiang Yongwen, Xie Zhongwen, Wu Zhengqi

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Аннотация

Aroma assessment remains difficult and uncertain in the present sensory assessment system. It is highly desirable to develop a new assessment method to discriminate the quality of various teas in the tea market. In the present work, based on linear discriminant analysis and principal component analysis, the aroma of dry and wet samples of different Xi-hu Longjing and Pu-erh teas were tested and differentiated by electronic noses (e-nose). The results confirm that e-nose can discriminate different priced Xi-hu Longjing tea samples in the range of 80-800 RMB/500 g and varying storage years of Pu-erh tea samples. Furthermore, for the detection of both dry and wet samples of Longjing and Pu-erh teas, the results reveal that all samples have specific aroma characteristics that e-nose can recognize. More importantly, contribution analysis in sensors indicates that nitrogen oxides, methane and alcohols are the characteristic components that contribute to the fragrances of different priced Xi-hu Longjing teas, while nitrogen oxides, aromatic benzene and amines make the fragrances of Pu-erh teas with different storage years disparate. PRACTICAL APPLICATION: This work demonstrates that e-nose can rapidly distinguish tea products with different price levels and varying storage years. With the advantages of ease of use, high portability and flexibility, e-nose will be widely expanded and applied in refined processing and the development of flavored foods.

Для цитирования: Yuan Haibo, Chen Xiaoqiang, Shao Yundong, Cheng Yong, Yang Yanqin, Zhang Mingming, Hua Jinjie, Li Jia, Deng Yuliang, Wang Jinjin, Dong Chunwang, Jiang Yongwen, Xie Zhongwen, Wu Zhengqi. Quality Evaluation of Green and Dark Tea Grade Using Electronic Nose and Multivariate Statistical Analysis.. Journal of food science. 2019. DOI: 10.1111/1750-3841.14917.

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