T-Distributed Stochastic Neighbor Embedding to Improve the Discrimination of Yogurt Using a Multistep Amperometry Electronic Tongue

Abstract

In this work, a classification system of eight different yogurts produced in the Boyacá region of Colombia is developed. The system is based on a sensor array used as an electronic tongue. Following the concept of the IUPAC technical report [1], an “electronic tongue” is an analytical instrument including an array of non-selective chemical sensors with partial specificity to different solution components and an appropriate pattern recognition instrument, capable of recognizing quantitative and qualitative compositions of simple and complex solutions. First, the electronic tongue developed is described with its parts, its characteristics, and operation. Subsequently, the electrochemical techniques used in the developed experiments, that is, multi-step amperometry, are detailed, as well as the configuration parameters. The electronic tongue described in this work is composed of an array of Sensors of the Screen-Printed Electrode (SPE) type from the BVT technologies brand. The selected potentiostat is the Palmsens4 offered by the Palmsens company, in addition to a MUX8R2 multiplexer equipment also from the Palmsens company. This multiplexer allows handling up to 8 sensors. The schematic diagram in Figure 1 illustrates the components of the electronic tongue system. The different signals coming from the 8 sensors in each experiment require a signal processing and pattern recognition methodology for the correct classification of the yogurts. Our team developed a new pattern recognition methodology composed of the following stages. First, the data is arranged to build a two-dimensional matrix following a data unfolding process. Next, the raw data is normalized using the mean-centered group scaling method, in order to consider the different magnitudes of the signals coming from the sensors. Then, a dimensionality reduction and feature extraction stage is performed using the t-distributed stochastic neighbor embedding t-SNE method [2]. The feature matrix serves as input of a supervised machine learning classification algorithm, in this case a k-NN method is used [3]. The total number of experiments yields few samples per yogurt, therefore, in total, an imbalanced dataset of 151 samples of 8 different types of yogurt is used for the validation of the proposed methodology. This validation is executed through a Leave One Out Cross Validation method [4]. A final classification accuracy of 99. 33% was obtained by applying the developed methodology. Only one sample mistake in the 151 samples was obtained.The authors thank FONDO DE CIENCIA TECNOLOGÍA E INNOVACION FCTeI DEL SISTEMA GENERAL DE REGALÍAS SGR. The authors express their gratitude to the Administrative Department of Science, Technology and Innovation—Colciencias with the grant 779—“Convocatoria para la Formación de Capital Humano de Alto Nivel para el Departamento de Boyacá 2017” for sponsoring the research presented herein. Jersson X. Leon-Medina is grateful with Colciencias and Gobernación de Boyacá for the PhD fellowship.