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
