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.

中文翻译：

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使用局部判别保存投影的电子舌味觉识别


电子舌（E-Tongue）作为一种新颖的味觉分析工具，为味觉识别展现了广阔的前景。本文构建了伏安法E-Tongue系统，对茶、酒、饮料、功能材料等13种不同的液体样品进行了测量。由于系统的噪声和各种环境条件，获得的E -舌头数据显示出不可分割的模式。为此，从算法的角度来看，我们提出了一种局部判别保留投影（LDPP）模型，这是一种正在研究的子空间学习算法，涉及局部判别和邻域结构保留。与其他传统的子空间投影方法相比，LDPP 有两个优点。一方面，通过局部歧视，它对异常数据或异常值具有更高的容忍度。另一方面，它可以将数据投影到更可分离的空间并保留局部结构。此外，支持向量机、极限学习机（ELM）和核化ELM（KELM）已被用作电子舌中味道识别的分类器。实验结果表明，所提出的电子舌在效率和有效性方面对于多种口味识别都是有效的。特别是，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发布和下载。