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Article

A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue

1
Department of School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China
2
Electric Power Research Institute of Guizhou Power Grid Co., Ltd., Guizhou 550007, China
*
Author to whom correspondence should be addressed.
Appl. Sci. 2019, 9(12), 2518; https://doi.org/10.3390/app9122518
Received: 11 May 2019 / Revised: 9 June 2019 / Accepted: 18 June 2019 / Published: 20 June 2019
(This article belongs to the Special Issue Non-destructive Sensors in Food Authentication)
Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are “hand-crafted”, which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited the application and development of electronic tongue systems. In this work, a convolutional neural network-based auto features extraction strategy (CNN-AFE) in an electronic tongue (e-tongue) system for tea classification was proposed. First, the sensor response of the e-tongue was converted to time-frequency maps by short-time Fourier transform (STFT). Second, features were extracted by convolutional neural network (CNN) with time-frequency maps as input. Finally, the features extraction and classification results were carried out under a general shallow CNN architecture. To evaluate the performance of the proposed strategy, experiments were held on a tea database containing 5100 samples for five kinds of tea. Compared with other features extraction methods including features of raw response, peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD), the proposed model showed superior performance. Nearly 99.9% classification accuracy was obtained and the proposed method is an approximate end-to-end features extraction and pattern recognition model, which reduces manual operation and improves efficiency. View Full-Text
Keywords: electronic tongue; tea classification; auto features extraction; convolutional neural network electronic tongue; tea classification; auto features extraction; convolutional neural network
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MDPI and ACS Style

Zhong, Y.h.; Zhang, S.; He, R.; Zhang, J.; Zhou, Z.; Cheng, X.; Huang, G.; Zhang, J. A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue. Appl. Sci. 2019, 9, 2518. https://doi.org/10.3390/app9122518

AMA Style

Zhong Yh, Zhang S, He R, Zhang J, Zhou Z, Cheng X, Huang G, Zhang J. A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue. Applied Sciences. 2019; 9(12):2518. https://doi.org/10.3390/app9122518

Chicago/Turabian Style

Zhong, Yuan h., Shun Zhang, Rongbu He, Jingyi Zhang, Zhaokun Zhou, Xinyu Cheng, Guan Huang, and Jing Zhang. 2019. "A Convolutional Neural Network Based Auto Features Extraction Method for Tea Classification with Electronic Tongue" Applied Sciences 9, no. 12: 2518. https://doi.org/10.3390/app9122518

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