###### Abstract  
Feature extraction is a key part of the electronic tongue system. Almost all of the existing features extraction methods are \"hand-crafted\", which are difficult in features selection and poor in stability. The lack of automatic, efficient and accurate features extraction methods has limited the application and development of electronic tongue systems. In this work, a convolutional neural network-based auto features extraction strategy (CNN-AFE) in an electronic tongue (e-tongue) system for tea classification was proposed. First, the sensor response of the e-tongue was converted to time-frequency maps by short-time Fourier transform (STFT). Second, features were extracted by convolutional neural network (CNN) with time-frequency maps as input. Finally, the features extraction and classification results were carried out under a general shallow CNN architecture. To evaluate the performance of the proposed strategy, experiments were held on a tea database containing 5100 samples for five kinds of tea. Compared with other features extraction methods including features of raw response, peak-inflection point, discrete cosine transform (DCT), discrete wavelet transform (DWT) and singular value decomposition (SVD), the proposed model showed superior performance. Nearly 99.9% classification accuracy was obtained and the proposed method is an approximate end-to-end features extraction and pattern recognition model, which reduces manual operation and improves efficiency. electronic tongue; tea classification; auto features extraction; convolutional neural network +
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and identification of tea provide a reference for healthy tea drinking. What's more, the classification and recognition of tea are more likely to provide the basis for the design of smart teapot in the future. Traditional tea quality assessment methods are based on different analytical instruments, such as high performance liquid chromatography [1], gas chromatography [2] and plasma atomic emission spectrometry [3]. However, these methods require a lot of technical personnel, material and financial support, which lead to low efficiency and larger overhead [4]. With the development of sensor technology, the advantages of methods based on sensor technology are more distinguished. The typical advantages are high accuracy, simple operation and fast detection, which improve the efficiency of tea quality inspection obviously. At the same time, the electronic nose for gas analysis has also made technological breakthroughs [5; 6]. The arrays of electrochemical sensors and devices have been designed for the analysis of complex liquid samples, such as taste sensor and electronic tongues [7; 8]. As a modern intelligent sensory instrument, electronic tongue is skillful in monitoring the production cycle of beverage, and has the advantages of simple, fast and low cost, which shows huge potential in beverage quality evaluation [9]. Figure 1 shows the basic flow of the electronic tongue system for beverage detection and quality evaluation. First, the response signals from the e-tongue hardware system are collected. Then, features of sampling raw data are extracted for pattern recognition. Pattern recognition and features extraction are the two most important parts of the electronic tongue system for liquid classification. Many scholars have contributed to tea pattern recognition. For instance, principal component analysis (PCA) [10], artificial neural network (ANN) [11], support vector machine (SVM) [9; 12], \(k\)-nearest neighbor algorithm (\(k\)-NN) [13], random forest (RF) [14] and autoregressive (AR) model [4] have been put forward for classification analysis in the e-tongue system. Features extraction is another critical step of the e-tongue as the quality of features selection will directly affect the quality of pattern recognition.