Analysis of E-tongue data for tea classification based on semi-supervised learning of generative adversarial network

The identification of tea quality provides the protection of rights and interests to consumers. This paper proposes a semi-supervised learning model based on a generative adversarial network (GAN-SSL), to model and predict the electronic tongue data of tea samples. The GAN-SSL consists of generative model G and discriminative model D. We demonstrated the generated sample as a new class. The model G was inputted into a random noise. The discriminator acquires a series of potential features of the inputted data. Through the adversarial learning between the D and G, more realistic samples were generated, and D predicted the category and authenticity of the real data and generated data. The analysis of collected E-tongue data set of tea showed that GAN-SSL effectively improved the accuracy of classification compared to multi-class support vector machine (multi-class SVM), partial least square regression analysis (PLS-DA) and Decision Tree.