Application of Electronic Nose and Neural Network in Beef Freshness Detection
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Abstract:
Fresh beef under 0~12 day cold storage were detected by sensory evaluation, semi-micro determination of nitrogen detection, microbiological testing and electronic nose (e-nose) detection. For the e-nose detection, the 50th s sensor signals were extracted for analysis. Mahalanobis Distance (MD) between fresh and stored samples enlarged with the increase of storage time, and the quality of beef significantly changed after stored for 5 days. Principle component analysis (PCA) and Linear Discriminant Analysis (LDA) results indicated that all the beef samples could be well distinguished except a little overlap between the samples stored for 0~5 days. Stepwise Linear Discriminant Analysis (Step-LDA) was applied to predict the storage time and got an accuracy of 96.19%. Back Propagation Neural Network (BPNN）and Generalized Regression Neural Network (GRNN) were also used to build a correlation model between the storage time and the physicochemical indicators, which showed that GRNN was better than BPNN. The prediction error based on the GRNN model for the storage time, total volatile basic nitrogen, microbial population and sensory scores were 1.36 days, 4.64?10-2 mg/g, 1.61×106 cfu/g and 1.31, respectively.
