Departamento de Ingeniería Mecánica y Mecatrónica, Universidad Nacional de Colombia, Cra 45 No. 26-85, Bogotá 111321, Colombia.
MEM (Modelling-Electronics and Monitoring Research Group), Faculty of Electronics Engineering, Universidad Santo Tomás, Bogotá 110231, Colombia.
Sensors (Basel). 2020 Aug 27;20(17):4834. doi: 10.3390/s20174834.
A nonlinear feature extraction-based approach using manifold learning algorithms is developed in order to improve the classification accuracy in an electronic tongue sensor array. The developed signal processing methodology is composed of four stages: data unfolding, scaling, feature extraction, and classification. This study aims to compare seven manifold learning algorithms: Isomap, Laplacian Eigenmaps, Locally Linear Embedding (LLE), modified LLE, Hessian LLE, Local Tangent Space Alignment (LTSA), and -Distributed Stochastic Neighbor Embedding (-SNE) to find the best classification accuracy in a multifrequency large-amplitude pulse voltammetry electronic tongue. A sensitivity study of the parameters of each manifold learning algorithm is also included. A data set of seven different aqueous matrices is used to validate the proposed data processing methodology. A leave-one-out cross validation was employed in 63 samples. The best accuracy (96.83%) was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the -SNE algorithm for feature extraction, and -nearest neighbors (NN) as classifier.
为了提高电子舌传感器阵列的分类准确性，开发了一种基于流形学习算法的非线性特征提取方法。所开发的信号处理方法由四个阶段组成：数据展开、缩放、特征提取和分类。本研究旨在比较七种流形学习算法：等距映射（Isomap）、拉普拉斯特征映射（Laplacian Eigenmaps）、局部线性嵌入（LLE）、改进的 LLE、Hessian LLE、局部切空间对齐（LTSA）和分布式随机邻居嵌入（-SNE），以在多频大幅脉冲伏安电子舌中找到最佳的分类准确性。还包括对每种流形学习算法的参数进行灵敏度研究。使用七种不同的水溶液矩阵数据集来验证所提出的数据处理方法。在 63 个样本中采用了留一法交叉验证。当方法使用均值中心化组缩放（MCGS）进行数据归一化、-SNE 算法进行特征提取和 -最近邻（NN）作为分类器时，获得了最佳的准确性（96.83%）。
