%0 Conference Proceedings
%T Discrimination and Prediction of Pork Freshness by E-nose
%+ Zhejiang University
%A Hong, Xuezhen
%A Wang, Jun
%Z Part 1: Simulation, Optimization, Monitoring and Control Technology
%< avec comité de lecture
%( IFIP Advances in Information and Communication Technology
%B 5th Computer and Computing Technologies in Agriculture (CCTA)
%C Beijing, China
%Y Daoliang Li
%Y Yingyi Chen
%I Springer
%3 Computer and Computing Technologies in Agriculture V
%V AICT-370
%N Part III
%P 1-14
%8 2011-10-29
%D 2011
%R 10.1007/978-3-642-27275-2_1
%K Electronic nose
%K Pork freshness
%K Prediction
%K Back Propagation Neural Network
%K Multiple Linear Regression
%Z Computer Science [cs]Conference papers
%X 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.
%G English
%Z TC 5
%Z WG 5.14
%2 https://inria.hal.science/hal-01361112/document
%2 https://inria.hal.science/hal-01361112/file/978-3-642-27275-2_1_Chapter.pdf
%L hal-01361112
%U https://inria.hal.science/hal-01361112
%~ IFIP
%~ IFIP-AICT
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG5-14
%~ IFIP-CCTA
%~ IFIP-AICT-370