Rapid identification method of wolfberry geographical origin based on voltammetry electronic tongue

Authors

YIN Tingjia, School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong 255049, China
YANG Zhengwei, School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong 255049, China
GUO Tingting, School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong 255049, China
WANG Zhiqiang, School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong 255049, China
SUN Xia, School of Agricultural Engineering and Food Science, Shandong University of Technology, Zibo, Shandong 255049, China
LI Caihong, School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong 255049, China
YUAN Wenhao, School of Computer Science and Technology, Shandong University of Technology, Zibo, Shandong 255049, China

Abstract

In order to achieve rapid identification of Wolfberry from different geographical origin, an electronic tongue identification method based on Hilbert-Huang transform (HHT)-Linear Discriminant Analysis (LDA) was proposed. Taking the four geographical origins (Ningxia, Xinjiang, Gansu and Qinghai) of wolfberry as experimental materials, the voltammetry electronic tongue was used to collect the “fingerprint” information of different geographical origins, and then the Ensemble empirical modal decomposition (EEMD) was used to carry out the original signal of the electronic tongue. The scale decomposition obtained a set of intrinsic mode functions (IMF), and finally its singular spectral entropy and Hilbert marginal spectrum were collected as feature vectors. On this basis, LDA was used to establish a nonlinear combination prediction model for the production area. The experimental results showed that HHT-LDA was better than the algorithm of Feature Point Extraction (FPE), Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT). The overall classification accuracy and kappa coefficient of Wolfberry from unknown origin reached 98% and 0.973, respectively, indicating that the model had a good identification performance.

Publication Date

5-28-2019

First Page

116

Last Page

122

DOI

10.13652/j.issn.1003-5788.2019.05.021

Recommended Citation

Tingjia, YIN; Zhengwei, YANG; Tingting, GUO; Zhiqiang, WANG; Xia, SUN; Caihong, LI; and Wenhao, YUAN (2019) "Rapid identification method of wolfberry geographical origin based on voltammetry electronic tongue," Food and Machinery: Vol. 35: Iss. 5, Article 21.
DOI: 10.13652/j.issn.1003-5788.2019.05.021
Available at: https://www.ifoodmm.cn/journal/vol35/iss5/21

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