Discrimination and prediction of pork freshness by E-nose

Abstract

An electronic nose (e-nose) was used to establish a freshness evaluation model for pork. A pre-experiment was performed to acquire optimum parameters (10 g sample mass with 5 min headspace-generation time in 500 mL vial) for later e-nose detection of pork. Responding signals of the e-nose were extracted and analyzed. Linear Discriminant Analysis (LDA) results showed that the e-nose could classify pork with different storage time (ST) well. Back Propagation Neural Network (BPNN) was performed to predict the ST, and the results showed that 97.14% of the predicting set (with 95.71% of the training set) was classified correctly; and Multiple Linear Regression (MLR) was used to predict the sensory scores, with the results showing that the correlation coefficients (R 2 = 0.9848) between the e-nose signals and the sensory scores was high. These results prove that e-nose has the potential of assessing pork freshness. © 2012 IFIP Federation for Information Processing.