Discrimination and bitter flavor characteristics assessments of soybean peptide by intelligent electronic tongue

In this paper,the discrimination and bitter flavor characteristics assessments of five different samples was studied. The taste information of soybean peptide solution was collected by the French ASTRESS electronic tongue sensor and the method of DFA was applied to do qualitative analysis of the solution. The relationship between the sensor response value and bitter taste score was analyzed and two qualitative prediction modes was established based respectively on the methods of RBF and partial least squares. The results showed that the method of DFA had a good discrimination of different soybean peptide samples with100 distinction index and accurately predicted the taste of unknown samples with 100% recognition rate. The RMSE based on partial least squares of modeling and prediction sets were 2.47% and 6.81% respectively while the model based on the methods of RBF were 0.81% and 3.37%. It showed that the model based on RBF did better in the prediction than the model based on partial least squares and the results might provide a way to the follow-up study of the flavor characteristics of soybean peptide product.