Discrimination and prediction of multiple beef freshness indexes based on electronic nose

Abstract

An electronic nose (e-nose) instrument combined with chemometrics was used to predict the physical–chemical indexes (sensory scores, total volatile basic nitrogen (TVBN) and microbial population) for beef. The e-nose data generated were analyzed by chemometrics methods and pattern recognition. Mahalanobis Distance (MD) analysis, Principle Component Analysis (PCA) and Linear Discriminant Analysis (LDA) confirmed the difference in volatile profiles of beef samples of 7 different storage times (ST). The Back Propagation Neural Network (BPNN) and Generalized Regression Neural Network (GRNN) were used to build prediction models for ST, TVBN content, microbial population and sensory scores. The result of GRNN was better than that of BPNN, and the standard error (SE) of GRNN prediction model for ST, TVBN, microbial population, sensory scores were 1.36 days, 4.64 × 10 −2 mg g −1, 1.612 × 10 6 cfu g −1 and 1.31 respectively. This research indicates that it is of feasibility to use e-nose to predict multiple freshness indexes for beef.