Employment of an electronic tongue combined with deep learning and transfer learning for discriminating the storage time of Pu-erh tea. (March 2021)

• Record Type: Journal Article
• Authors: Yang, Zhengwei; Miao, Nan; Zhang, Xin; Li, Qingsheng; Wang, Zhiqiang; Li, Caihong; Sun, Xia; Lan, Yubin
• Is Part Of: Food control. Volume 121(2021)
• Publication Date: 2021-03
• Subjects: Voltammetric electronic tongue -- 1-D convolutional neural network -- Transfer learning -- Pu-erh tea -- Storage time; Food -- Quality -- Periodicals; Food -- Analysis -- Periodicals; Food handling -- Periodicals; Food industry and trade -- Quality control -- Periodicals; Aliments -- Industrie et commerce -- Qualité -- Contrôle -- Périodiques; Aliments -- Qualité -- Périodiques; Aliments -- Analyse -- Périodiques; Hygiène alimentaire -- Périodiques; Food -- Analysis; Food handling; Food -- Quality; Periodicals; Electronic journals; 664.07
• DOI: 10.1016/j.foodcont.2020.107608
• Languages: English
• ISSNs: 0956-7135

Abstract

Abstract: Pu-erh tea is a famous Chinese fermented tea, and its quality and flavor are closely related to the storage time used for its fermentation. This paper puts forward one method to discriminate the age of Pu-erh tea by employing a voltammetric electronic tongue (VE-Tongue) combined with deep learning and transfer learning techniques. To make the deep learning model suitable for processing VE-Tongue signals, a one-dimensional convolutional neural network (1-D CNN) was developed to automatically perform feature extraction and classification. Transfer learning (TL) was introduced to train the model to reduce the training complexity and enhance the generalization capability of the CNN. The performance of the proposed model was further compared with that of traditional machine learning methods such as the backpropagation neural network, support vector machine and extreme learning machine. The results showed that the proposed model exhibited better performance in classifying Pu-erh tea than other methods. Its accuracy for the test set, precision, recall and F1 score was 98.80%, 98.2%, 98%, and 0.98, respectively. This study found that the VE-Tongue combined with deep learning and TL algorithms could be a sensitive, reliable and effective detection method for identifying the amount of storage time of Pu-erh tea, which could further expand its applications to other related fields. Graphical abstract: Image 1 Highlights: Classification of Pu-erh tea is achieved by voltammetric electronic tongue system. Pattern recognition method based on deep learning and transfer learning is proposed. 1-D CNN model is design and optimized for analyzing electronic tongue signals. The proposed model is superior to conventional pattern recognition methods.