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.
Highlights
Tea is one of the most prevailing beverages across the world
Inspired by the successful application of convolutional neural network (CNN) in image processing, we proposed an auto features extraction strategy based on CNN (CNN-AFE)
Counter electrode and the an reference electrode processes, the working electrode is placed in distilled water and with electrochemical for minmethod andwith dried with cloth
Summary
Tea is one of the most prevailing beverages across the world. The practice of drinking tea has been a long history in China. Pattern recognition and features extraction are the two most important parts of the electronic tongue system for liquid classification. Scholars have tried to fuse the electronic tongue with the electronic nose sensor data to enhance the tea quality prediction accuracies. 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]. The significant contribution of the paper is to put forward a deep learning-based auto features extraction strategy in the e-tongue system for tea classification. In the rest of this paper, we arrange the content as follows: Section 2 provides a brief description this study, we adopt introduces tea databaseexperiment for classification, samples areofcollected by an e-tongue aboutInthe e-tongue system, settingsteaand details the proposed method.
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