Discrimination of cocoa beans according to geographical origin by electronic tongue and multivariate algorithms

Abstract

Electronic tongue as an advanced and novel emerging technology has been successfully utilized for the rapid identification of cocoa beans according to their geographical locations. Seven categories of cocoa beans from Ghana were used in this experiment. Electronic tongue system was used for data acquisition while three patterns recognition methods were applied comparatively to build discrimination model. The performances of the models were cross-validated to ensure its stability. Experimental results revealed that Fisher’s discriminant analysis (FDA) is better than principal component analysis (PCA) for visualizing the cluster trends. K-nearest neighbour (KNN) model was slightly better than FDA model at an optimal performance of 100 % in the training set and 98.8 % in prediction set. Overall, support vector machine (SVM) was superior to both FDA and KNN with 100 % discrimination rate in both the training and prediction set at five PCs. This finding proves that electronic tongue technology coupled with SVM technique can rapidly, accurately, and reliably discriminate cocoa beans for quality assurance management