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

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.

Automated summary

This study proposed a method to discriminate the age of Pu-erh tea using a voltammetric electronic tongue combined with deep learning and transfer learning techniques, showing better performance than traditional machine learning methods. The accuracy of the proposed model for classifying Pu-erh tea was 98.80%, with precision of 98.2%, recall of 98%, and F1 score of 0.98.