Abstract
An electronic tongue (E-Tongue) is a bionic system that relies on an array of electrode sensors to realize taste perception. Large pulse voltammetry (LAPV) is an important E-Tongue type which generally generates a large amount of response data. Considering that high common-mode characteristics existing in sensor arrays largely depress the recognition performance, we propose an alternative feature extraction method for sensor specificity enhancement, which is called feature specificity enhancement (FSE). Specifically, the proposed FSE method measures sensor specificity on paired sensor responses and utilizes kernel function for nonlinear projection. Meanwhile, kernel extreme learning machine (KELM) is utilized to evaluate the overall performance of recognition. In experimental evaluation, we introduce several feature extraction methods and classifiers for comparison. The results indicate that the proposed feature extraction combined with KELM shows the highest recognition accuracy of 95% on our E-Tongue dataset, which is superior to other methods in both effectiveness and efficiency.
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Chen, Y., Liu, T., Chen, J., Li, D., Wu, M. (2020). A Novel Feature Specificity Enhancement for Taste Recognition by Electronic Tongue. In: Cao, J., Vong, C., Miche, Y., Lendasse, A. (eds) Proceedings of ELM 2018. ELM 2018. Proceedings in Adaptation, Learning and Optimization, vol 11. Springer, Cham. https://doi.org/10.1007/978-3-030-23307-5_2
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DOI: https://doi.org/10.1007/978-3-030-23307-5_2
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