Taste Recognition in E-Tongue Using Local Discriminant Preservation Projection
Taste Recognition in E-Tongue Using Local Discriminant Preservation Projection
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DOI：
10.1109/tcyb.2018.2789889
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发表时间：
2019-03
影响因子：
11.8
通讯作者：
Lei Zhang;Xue Wang;G. Huang;Tao Liu;Xiaoheng Tan
中科院分区：
文献类型：
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作者：
Lei Zhang;Xue Wang;G. Huang;Tao Liu;Xiaoheng Tan
电子舌作为一种新型的味觉分析工具，在味觉识别方面具有广阔的应用前景。本文构建了一套伏安法电子舌系统，对茶叶、葡萄酒、饮料、功能材料等13种不同的液体样品进行了测量。由于系统的噪声和环境条件的多样性，获得的电子舌数据呈现出不可分割的模式。为此，从算法的角度来看，我们提出了一个局部判别保留投影（LDPP）模型，一个正在研究的子空间学习算法，涉及到局部判别和邻域结构的保护。与传统的子空间投影方法相比，LDPP具有两个优点。一方面，具有局部判别能力，它对异常数据或离群值具有更高的容忍度。另一方面，它可以将数据投影到一个更可分离的空间，并保持局部结构。此外，支持向量机，极端学习机（ELM）和核ELM（KELM）已被用作分类器的味道识别在电子舌。实验结果表明，所提出的电子舌是有效的多口味识别的效率和效果。特别地，所提出的基于LDPP的KELM分类器模型实现了98%的最佳味道识别性能。开发的基准数据集和代码将在http://www.leizhang.tk/ tempcode.html上发布和下载。
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 LDPP-based KELM classifier model achieves the best taste recognition performance of 98%. The developed benchmark data sets and codes will be released and downloaded in http://www.leizhang.tk/ tempcode.html.