Yogurt classification using an electronic tongue system and machine learning techniques

Electronic tongue sensor arrays allow the analysis of liquid substances and provide information for classification tasks to artificially emulate a taste recognition system. These kinds of devices are needed for the alimentary industry because they ensure their products' quality; however, they are still in the development phase because of the components required for their robustness. This paper presents an electronic tongue system for yogurt classification to contribute to developing automatic systems for this industry. The developed system uses a sensor array with Screen-Printed Electrode sensors, a potentiostat, and a multiplexer to handle eight different sensors. A pattern recognition methodology for the processing of multi-step amperometry signals is defined. It comprises the following stages: First, a data unfolding process to build a two-dimensional matrix with the information from the sensors is applied. Next, raw data is normalized using the mean-centered group scaling method. Then, a dimensionality reduction stage is applied. Two methods are evaluated, the t-distributed stochastic neighbor embedding (t-SNE) and Principal Component Analysis (PCA). Subsequently, a supervised machine learning classification algorithm is used. Five methods were evaluated to determine the best classifier: LDA, classification trees(C4.5), naive Bayes, k-NN, and SVM. A dataset of 151 samples of 8 different yogurts is used for the methodology validation. This validation is executed through a Leave One Out Cross Validation (LOOCV) method. Final classification accuracy of 100% was obtained When PCA and LDA or t-SNE and k-NN were used; however, from the computational point of view, a shorter execution time was evidenced for PCA and LDA, showing the convenience in its use in the methodology.