Rapid identification of oolong tea category by synergetic application of E-nose and E-tongue combined with a modified GAN - TCN composite model

The adulteration and counterfeiting of tea products lead to economic losses and poses health concerns for consumers. This study proposed a novel method to rapidly discriminate the category of oolong tea, a famous Chinese semifermented tea, by applying an electronic nose (E-nose) and electronic tongue (E-tongue) system combined with a modified generative adversarial network (GAN) - temporal convolutional network (TCN) composite model. Specifically, the olfactory and gustatory fingerprint signals of tea samples are first collected by E-nose and E-tongue sensory systems, respectively. Considering the unbalanced scarcity and small-sample nature of data collection, a Wasserstein GAN model is employed to learn the data representations of E-nose and E-tongue signals and generate highly realistic training samples. Two improved temporal convolutional networks (TCNs) integrated with a squeeze-and-excitation attention module are proposed to learn the significant features and discover the internal patterns from the signals of the E-nose and E-tongue. Then, a dynamic fusion module (DFM) is developed to fuse the features of the E-nose and E-tongue to achieve information complementarity and enhancement. Finally, the fused information is sent to a classifier to predict the class label. The proposed approach is assessed using different performance metrics, and the experimental results reveal that an accuracy of 99.33% was achieved. The above research will provide a new approach for the rapid identification of different categories of oolong tea, which has a wide range of application prospects in the rapid classification and detection of other agricultural products.