局部线性嵌入作为非线性特征提取的循环伏安电子舌鉴别液体

Jersson X. Leon-Medina, Maribel Anaya, D. Tibaduiza
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引用次数: 0

摘要

电子舌是用于分析水性基质的分类或定量任务的装置。这些系统由几个不同材料的传感器、一个数据采集单元和一个模式识别系统组成。伏安传感器已应用于循环伏安法的电子舌中。通过使用这种方法,每个传感器产生伏安图,该伏安图将电流响应与施加到工作电极上的电压变化联系起来。在实验过程中获得了大量的数据,可以将分析作为模式识别应用进行处理;然而,开发高效的基于机器学习的方法仍然是一个开放的研究兴趣话题。作为贡献,这项工作提出了一种新的数据处理方法来分类由循环伏安电子舌获得的信号。该方法包括通过群尺度法对数据进行归一化和利用局部线性嵌入(LLE)技术进行非线性特征提取等步骤。将缩小尺寸的特征向量输入到k-近邻(k-NN)监督分类器算法中。执行留一交叉验证(LOOCV)过程以获得最终的分类精度。用五种不同果汁作为液体物质的数据集验证了该方法。电子舌采用了两个丝网印刷电极电压传感器。具体来说,它们的工作电极材料是铂和石墨。应用所开发的方法后,结果达到80%的分类准确率。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Locally Linear Embedding as Nonlinear Feature Extraction to Discriminate Liquids with a Cyclic Voltammetric Electronic Tongue
Electronic tongues are devices used in the analysis of aqueous matrices for classification or quantification tasks. These systems are composed of several sensors of different materials, a data acquisition unit, and a pattern recognition system. Voltammetric sensors have been used in electronic tongues using the cyclic voltammetry method. By using this method, each sensor yields a voltammogram that relates the response in current to the change in voltage applied to the working electrode. A great amount of data is obtained in the experimental procedure which allows handling the analysis as a pattern recognition application; however, the development of efficient machine-learning-based methodologies is still an open research interest topic. As a contribution, this work presents a novel data processing methodology to classify signals acquired by a cyclic voltammetric electronic tongue. This methodology is composed of several stages such as data normalization through group scaling method and a nonlinear feature extraction step with locally linear embedding (LLE) technique. The reduced-size feature vector input to a k-Nearest Neighbors (k-NN) supervised classifier algorithm. A leave-one-out cross-validation (LOOCV) procedure is performed to obtain the final classification accuracy. The methodology is validated with a data set of five different juices as liquid substances.Two screen-printed electrodes voltametric sensors were used in the electronic tongue. Specifically the materials of their working electrodes were platinum and graphite. The results reached an 80% classification accuracy after applying the developed methodology.
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