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.