###### Abstract
Generally speaking, feature extraction is advantageous for three main purposes, namely: (1) Reduction of random noise, (2) reduction of unwanted systematic variations which are often due to experimental conditions, (3) data compression in order to capture the most relevant information from signals. There are differences in the implementation details of feature extraction methods in different types of electronic tongue systems. However, as far as we know, these methods are similar in principle, which can be divided into the following three categories: Features with physical meaning, features acquired by mathematical transformation, features in frequency domain. In terms of voltammetry e-tongue, in the early stage, the features extraction methods of samples were relatively simple, for example, raw data which were collected at a fix frequency from samples were treated as features [15]. Then, peak value and inflection point (the maximum value, the minimum value, and two inflection values in a circle from samples) were extracted as features [10; 16]. Unfortunately, these methods were low accuracy and features redundancy. Therefore, to extract features more effectively, many professional researchers tried to compress sampling data by various mathematical transformation methods. For instance, [PERSON] et al. used discrete wavelet transform (DWT) with sliding windows to extract energy in different frequency bands as features [17]. [PERSON] et al. used discrete cosine transform (DCT) to extract features, and selected some coefficients as eigenvalues for tea classification [18]. In addition, [PERSON] et al. transformed the sampling data into matrices, decomposed the matrices into singular values (SVD), and selected several singular values as features for tea classification [19]. In addition, SVD and DCT are fused for feature extraction and then applied in the prediction of theflavin and thearubigin in Tea [20]. Although these mathematical transformation methods made some improvements in features selection, they still need to be set  
Figure 1: The basic flow of the electronic tongue system for liquid detection. manually in the selection of features data. Specifically, for DWT, both the number of selected layers in wavelet transform and the strategy of characteristic coefficients (mean, variance, etc.) are determined according to experience. Moreover, the process of manually setting parameters in DCT is similar to DWT. In terms of SVD, diagonal values are selected based on experience. The way of features selection determines the limitations of these mathematical transformation methods. In addition, scholars have tried to fuse the electronic tongue with the electronic nose sensor data to enhance the tea quality prediction accuracies. For example, [PERSON] et al. adopted PCA to compress electronic tongue and electronic nose samples to assess a bio-inspired herbal tea flavor [21]. Wavelet energy feature (WEF) has been extracted from the responses of e-nose and e-tongue for the classification of different grades of Indian black tea [22]. Furthermore, [PERSON] et al. proposed a framework for a multi-level fusion strategy of electronic nose and electronic tongue. The time-domain based feature (mean value and max value of sensor response) and frequency-domain based feature (the energy of DWT) were fused for classification [23]. [PERSON] et al. combined electronic tongue data with electronic nose data and then fused DWT with Bayesian statistical analysis to evaluate the artificial flavor of black tea [24]. [PERSON] et al. proposed a cross-perception model (fused of electronic nose and electronic tongue samples by PCA and multiple linear regression) for the detection of aroma and taste of black tea [25]. In general, the manual features-based methods are difficult to adapt to changing scenes and have poor stability. The reason is that the methods adopt empirical parameters and the empirical value is usually no longer effective when the external environment changes.