An investigation was conducted to evaluate the capacity of an electronic metal oxide
semiconductor （MOS）-type nose （e-nose） to classify pork samples with different storage
times （0–6 d）. The effects of the headspace-generation time and pork sample mass on
the response of the e-nose were studied using m*riate analysis of variance and oneway
analysis of variance, respectively. The results showed that the pork sample mass
had the most significant effect on the e-nose sensor response, followed by the headspacegeneration
time. The optimum parameters were 10 g of sample mass with 5 min of
headspace-generation time in a 500 mL vial. After either principal component analysis
or linear discriminant analysis, the results showed that the e-nose with the optimum
parameters can accuray classify the pork samples stored for 0–6 d. A method using a
back propagation neural network was also performed, and 91.43% of the prediction set （with
92.86% of the training set） was classified correctly using this model.
