Abstract:In order to quantitatively and qualitatively evaluate the fish freshness , an electronic tongue was employed to detect the pomfret stored at 4 ℃ for different days. The total volatile basic nitrogen (TVB-N) and total viable count (TVC) of the fish samples were detected concurrently. K-nearest neighbor（KNN）model and back-propagation artificial neural network ( BP-ANN ) model were built to assess the freshness of the fish. Results showed that identification rate of training set and prediction set of KNN model were 99.11% and 98.21% respectively. While, the identification rate of training set and prediction set of BP-ANN model were 92.81% and 91.07% respectively. Support vector machine regression (SVR) model was established between the electronic tongue data and TVB-N as well as TVC for quantitative determination. The correlation coefficients between SVR predicted and measured TVB-N and TVC values were respectively 0.9727 and 0.9457, and root mean square error of prediction were 2.8×10-4 mg/g and 0.052 log(CFU/g), respectively. The overall results sufficiently demonstrate that the electronic tongue technique combined with appropriate pattern recognition method has a great potential to quantitative and qualitative evaluation of fish freshness rapidly.
