Classification of Rice by Combining Electronic Tongue and Nose

Abstract

One hundred twenty indica rice samples were determined by electronic tongue and electronic nose. The potential of the combinational approaches of electronic tongue and nose for rice analysis, with the aim of differentiating conventional and hybrid rice, was investigated. Principal component analysis (PCA) and locally linear embedding (LLE) were used to preprocess data from electronic systems. Support vector machine (SVM) model and K-nearest neighbors (KNN) model were established with the values from PCA and LLE algorithms as attributes. For the combination of electronic tongue and nose, the prediction accuracies of PCA-SVM, PCA-KNN, LLE-SVM, and LLE-KNN models were 55, 55, 85, and 80 %. The LLE-based models achieved better prediction accuracies than PCA-based models. These results demonstrated that LLE algorithm coupled with SVM or KNN for the combined electronic signals was effective in extracting and analyzing features for detecting rice. The LLE-SVM model achieved a little higher accuracy than the LLE-KNN model. It can be concluded that the combination of electronic systems coupled with LLE-based model have a great potential in the prediction of rice types.