A data-driven methodology for the classification of different liquids in artificial taste recognition applications with a pulse voltammetric electronic tongue

Abstract

Electronic tongue-type sensor arrays are devices used to determine the quality of substances and seek to imitate the main components of the human sense of taste. For this purpose, an electronic tongue-based system makes use of sensors, data acquisition systems, and a pattern recognition system. Particularly, in the latter, machine learning techniques are useful in data analysis and have been used to solve classification and regression problems. However, one of the problems in the use of this kind of device is associated with the development of reliable pattern recognition algorithms and robust data analysis. In this sense, this work introduces a taste recognition methodology, which is composed of several steps including unfolding data, data normalization, principal component analysis for compressing the data, and classification through different machine learning models. The proposed methodology is tested using data from an electronic tongue with 13 different liquid substances; this electronic tongue uses multifrequency large amplitude pulse signal voltammetry. Results show that the methodology is able to perform the classification accurately and the best results are obtained when it includes the use of K-nearest neighbor machine in terms of accuracy compared with other kinds of machine learning approaches. Besides, the comparison to evaluate the methodology is made with different classification performance measures that show the behavior of the process in a single number.

Editorial

“…The experimental results were statistically treated for a difference in population means to prove the accuracy of the model. Leon-Medina et al 5 propose a data-driven methodology for the classification of liquids using an electronic tongue. The electronic tongue uses multifrequency large amplitude pulse signal voltammetry.…”

“…Leon-Medina et al 5 propose a data-driven methodology for the classification of liquids using an electronic tongue. The electronic tongue uses multi-frequency large amplitude pulse signal voltammetry.…”

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

“…Thereby, a measure is obtained by each one of the electrodes that composed the electronic tongue. Following the formulation proposed by Pozo et al [ 39 ], the discretized measurements of the sensor can be arranged to form a matrix, as follows [ 1 ]: …”

“…Group scaling is frequently used when the data have several blocks of equal variables. In this case, the blocks are the number of sensors J and each block comprises variables in some given units of measure, but different sensors use different units [ 1 ]. In MCGS, the mean of all measurements of the sensor in the same column is considered in the normalization, as follows [ 39 ]: where and are the mean and the standard deviation of all the elements in matrix , respectively.…”

“…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 ].…”