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- W2984557753 abstract "This work presents initial results on a multitemporal hyperspectral image analysis method to evaluate the time degradation of pork meat. The proposed method is inexpensive and practically non-destructive. The hyperspectral data is analyzed and the relevant information is reduced to the information in only three wavelengths. The analysis is performed by a binary classifier composed by two stacked autoencoders and a softmax output layer. The use of autoencoders reduces tenfold the dimension of the input space. The proposed classifier has led to 97.2% of correct decisions, which indicates the great potential of the methodology to monitor the safety of meat." @default.
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- W2984557753 date "2019-09-01" @default.
- W2984557753 modified "2023-10-17" @default.
- W2984557753 title "Non-Destructive Prediction of Pork Meat Degradation using a Stacked Autoencoder Classifier on Hyperspectral Images" @default.
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- W2984557753 doi "https://doi.org/10.23919/eusipco.2019.8903164" @default.
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