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詳細(xì)介紹

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.

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