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- W2914853827 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." @default.
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- W2914853827 date "2014-01-01" @default.
- W2914853827 modified "2023-09-24" @default.
- W2914853827 title "Quantitative and Qualitative Analysis of Fish Freshness based on Electronic Tongue Technique" @default.
- W2914853827 hasPublicationYear "2014" @default.
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