Discrimination of preserved licorice apricot using electronic tongue

Fast recognition and characterization of preserved licorice apricot were studied by electronic tongue. The E-tongue signals were analyzed by pattern recognition techniques. Five brands of preserved licorice apricot were discriminated with strong convergence by pattern recognition techniques, Principal Component Analysis, Canonical discriminant Analysis and Cluster Analysis. The characterization of the samples obtained by Back-Propagation Neural Network (BPNN) and Partial Least Squares regression (PLSR) were 100% accurate both for training and test set, and the highest correlation between observed and predicted values was obtained for aerobic plate count, (0.9943, 0.9951) followed by total sugar content (0.9941, 0.9853), content of sodium chloride (0.9926, 0.9902), sulfur dioxide residues (0.9894, 0.9928) with BPNN method. All pattern recognition methods performed for the characterization and classification showed the potential of E-tongue as a rapid tool in the analysis and characterization of preserved fruits.

► The taste of preserved licorice apricot was studied by an electronic tongue.
► Pattern recognition methods were employed to classify the groups of samples.
► Physicochemical parameters were well predicted by E-tongue responses.
► A rapid quality analysis method was reported for preserved fruit based on E-tongue.