Grade identification of rice eating quality via a novel flow-injection voltammetric electronic tongue combined with SFFS-BO-SVM

Abstract

Given the importance and complexity of grade identification for rice eating quality, this study aimed to use a self-established flow-injection voltammetric electronic tongue (FIE-tongue) combined with SFFS-BO-SVM to replace human sensory evaluation. A novel FIE-tongue was firstly designed and established in this work, and the optimal rice pretreatment conditions were determined by fractional factorial design (FFD) which used for FIE-tongue detection. Based on optimized detection data, the optimized features and hyperparameters of 16 samples in three eating quality grades for SVM model were obtained by Sequential Floating Forward Selection (SFFS) algorithm and Bayesian Optimization (BO) algorithm, respectively. Compared to the original SVM model established by all features, the accuracy of SFFS-BO-SVM model for different grades were all higher than 92%, and have the increasement of 5.5%, 18.8%, and 11.4%, respectively. Therefore, the FIE-tongue combined with SFFS-BO-SVM method should be promising for rice eating quality grading and other food quality control.