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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 (R2 = 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.
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Xuezhen Hong, Jun Wang. Discrimination and Prediction of Pork Freshness by E-nose. 5th Computer and Computing Technologies in Agriculture (CCTA), Oct 2011, Beijing, China. pp.1-14, ⟨10.1007/978-3-642-27275-2_1⟩. ⟨hal-01361112⟩



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