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Freshness is a key value for access fish quality; fish freshness could be determined by volatile compounds during storage. An electronic nose was constructed with virtual instrument and four commercial tin dioxide-based gas sensors array. Fresh water fish silver carp was stored at 4degC and tested by headspace method. A weighted piece of fish is introduced into a sensor's chamber and the signals of the sensors due to the fish emissions are recorded as function of time. Unsupervised method PCA (principal component analysis) was employed to discriminate among fresh, semi-fresh and deteriorate fish. PCR (principal component regression), PLS (partial least square) and BP-ANN (artificial neural network) method were used to build mathematic model to predict TVB-N (total volatile basic nitrogen). The results indicated that PCA method can discriminate fresh, semi-fresh and deteriorate fish, which indicated the electronic nose response correlated with the loss of fish freshness. The correlation coefficients of predicted and measured TVB-N value of PCR, PLS and BP-ANN model were 0.898, 0.922 and 0.976, which indicated that the BP-ANN method has the highest predicted accuracy for electronic nose.