Classification of wolfberry from different geographical origins by using electronic tongue and deep learning algorithm

Abstract

Wolfberry is a traditional Chinese food. Its price and function are closely related to its geographical origin. Illegal labeling driven by commercial interests has brought serious food safety problems and damaged consumer confidence. In this study, a voltammetric electronic tongue (VE-tongue) combined with deep learning algorithm was developed to perform recognize of different origins of wolfberry samples. Training of deep learning model (Convolutional Neural Network, CNN) was performed with 260 wolfberry samples which were from 4 different geographical origins samples. To find the best performance CNN model, learning rate, optimizer and minibatch size were modified. The best classification accuracy of CNN was further compared with traditional machine learning method— BPNN with discrete wavelet transform (DWT) as feature extraction method. The classification accuracy of CNN, DWT-BPNN and BPNN are 98.27%, 88.46% and 48.08% respectively. This study provides a novel method for recognition and classification of wolfberry from different geographical origins, which holds great promise for its wide applications in geographical origin traceability for agricultural products.