caa a22 4500 606243097 CHVBK 20210128101322.0 cr unu---uuuuu 210128e20150301xx s 000 0 eng 10.1007/s11694-014-9209-0 doi (NATIONALLICENCE)springer-10.1007/s11694-014-9209-0 Study of herbal tea beverage discrimination method using electronic nose [Elektronische Daten] [Jiaojiao Jin, Shanggui Deng, Xiaoguo Ying, Xiao Ye, Tingting Lu, Guohua Hui] Herbal tea, a kind of traditional Chinese beverage, is widely used in China for its health benefits. This kind of tea beverage usually consists of several traditional Chinese medicines. So the taste of different herbal tea species varies a lot for their diverse components. In this paper, herbal tea discrimination method based on electronic nose (EN) is investigated. A laboratory developed EN, consisting of eight metal oxide semiconductor (MOS) gas sensors, is used in this study. EN responses to herbal tea samples at three temperatures' environment (20, 30, and 50°C) are collected by a desktop. Principal component analysis (PCA) and stochastic resonance (SR) is used for EN measurement data processing. PCA results indicate that herbal tea samples are discriminated more clearly with the increase of measurement temperature. The SR output signal-to-noise ratio (SNR) parameter is used for herbal tea discrimination. PCA method can not discriminate all herbal tea samples under different temperature. SR SNR method successfully discriminates all herbal tea samples under 20, 30, and 50°C. Denglao herbal tea SNR eigenvalues have much difference from other tea samples. Higher measurement temperature leads to better herbal tea discrimination based on EN. Springer Science+Business Media New York, 2014 Herbal tea nationallicence Temperature nationallicence Electronic nose nationallicence Stochastic resonance nationallicence Signal-to-noise ratio nationallicence Jin Jiaojiao School of Computer Science and Information Engineering, Zhejiang Gongshang University, 310035, Hangzhou, China aut Deng Shanggui College of Food and Pharmacy, Zhejiang Ocean University, 316000, Zhoushan, China aut Ying Xiaoguo College of Food and Pharmacy, Zhejiang Ocean University, 316000, Zhoushan, China aut Ye Xiao School of Computer Science and Information Engineering, Zhejiang Gongshang University, 310035, Hangzhou, China aut Lu Tingting School of Computer Science and Information Engineering, Zhejiang Gongshang University, 310035, Hangzhou, China aut Hui Guohua School of Computer Science and Information Engineering, Zhejiang Gongshang University, 310035, Hangzhou, China aut Journal of Food Measurement and Characterization Springer US; http://www.springer-ny.com 9/1(2015-03-01), 52-60 2193-4126 9:1<52 2015 9 11694 https://doi.org/10.1007/s11694-014-9209-0 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11694-014-9209-0 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Jin Jiaojiao School of Computer Science and Information Engineering, Zhejiang Gongshang University, 310035, Hangzhou, China aut NATIONALLICENCE 700 1- Deng Shanggui College of Food and Pharmacy, Zhejiang Ocean University, 316000, Zhoushan, China aut NATIONALLICENCE 700 1- Ying Xiaoguo College of Food and Pharmacy, Zhejiang Ocean University, 316000, Zhoushan, China aut NATIONALLICENCE 700 1- Ye Xiao School of Computer Science and Information Engineering, Zhejiang Gongshang University, 310035, Hangzhou, China aut NATIONALLICENCE 700 1- Lu Tingting School of Computer Science and Information Engineering, Zhejiang Gongshang University, 310035, Hangzhou, China aut NATIONALLICENCE 700 1- Hui Guohua School of Computer Science and Information Engineering, Zhejiang Gongshang University, 310035, Hangzhou, China aut NATIONALLICENCE 773 0- Journal of Food Measurement and Characterization Springer US; http://www.springer-ny.com 9/1(2015-03-01), 52-60 2193-4126 9:1<52 2015 9 11694