Nonlinear Feature Extraction Through Manifold Learning in an Electronic Tongue Classification Task.

Jersson X. Leon-Medina,Francesc Pozo,Diego Tibaduiza,Maribel Anaya

doi:10.3390/s20174834

Abstract

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 t-Distributed Stochastic Neighbor Embedding (t-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 () was obtained when the methodology uses Mean-Centered Group Scaling (MCGS) for data normalization, the t-SNE algorithm for feature extraction, and k-nearest neighbors (kNN) as classifier.

Highlights

Sensor arrays that are composed of electrochemical sensors can be used to discriminate different types of aqueous matrices, preserve flavor, detect anomalies, or quantify any analyte within an aqueous matrix [1]
The multiple frequency pulse signal used in the multifrequency large amplitude pulse voltammetry (MLAPV) electronic tongue is composed by three different frequencies, 0.2 Hz, 1 Hz, and 2 Hz, as well as five different pulse amplitudes 1 V, 0.8 V, 0.6 V, 0.4 V, and 0.2 V
The developed artificial taste recognition methodology allowed for correctly classifying aqueous matrices measured through a MLAPV electronic tongue

Summary

Introduction

Sensor arrays that are composed of electrochemical sensors can be used to discriminate different types of aqueous matrices, preserve flavor, detect anomalies, or quantify any analyte within an aqueous matrix [1]. The system known as the electronic tongue is composed of an array of non-selective sensors made of different materials that have the cross sensitivity property with which independent signals are captured by each sensor [2]. The data are sent to the pattern recognition system responsible for processing signals through multivariate data analysis and machine learning algorithms. Signal processing in electronic tongues sensor arrays aims to solve a classification or regression problem via machine learning algorithms [4].

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