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Title: Taste recognition in E-Tongue using local discriminant preservation projection
Authors: Zhang, Lei
Wang, Xuehan
Huang, Guang-Bin
Liu, Tao
Tan, Xiaoheng
Keywords: Engineering::Electrical and electronic engineering
Issue Date: 2018
Source: Zhang, L., Wang, X., Huang, G.-B., Liu, T., & Tan, X. (2019). Taste recognition in E-Tongue using local discriminant preservation projection. IEEE Transactions on Cybernetics, 49(3), 947-960. doi:10.1109/TCYB.2018.2789889
Journal: IEEE Transactions on Cybernetics
Abstract: Electronic tongue (E-Tongue), as a novel taste analysis tool, shows a promising perspective for taste recognition. In this paper, we constructed a voltammetric E-Tongue system and measured 13 different kinds of liquid samples, such as tea, wine, beverage, functional materials, etc. Owing to the noise of system and a variety of environmental conditions, the acquired E-Tongue data shows inseparable patterns. To this end, from the viewpoint of algorithm, we propose a local discriminant preservation projection (LDPP) model, an under-studied subspace learning algorithm, that concerns the local discrimination and neighborhood structure preservation. In contrast with other conventional subspace projection methods, LDPP has two merits. On one hand, with local discrimination it has a higher tolerance to abnormal data or outliers. On the other hand, it can project the data to a more separable space with local structure preservation. Further, support vector machine, extreme learning machine (ELM), and kernelized ELM (KELM) have been used as classifiers for taste recognition in E-Tongue. Experimental results demonstrate that the proposed E-Tongue is effective for multiple tastes recognition in both efficiency and effectiveness. Particularly, the proposed LDPPbased KELM classifier model achieves the best taste recognition performance of 98%. The developed benchmark data sets and codes will be released and downloaded in tempcode.html.
ISSN: 2168-2267
DOI: 10.1109/TCYB.2018.2789889
Rights: © 2018 IEEE. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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