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Communication Dans Un Congrès Année : 2019

Non-Destructive Prediction of Pork Meat Degradation using a Stacked Autoencoder Classifier on Hyperspectral Images

Résumé

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.
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Dates et versions

hal-03634157 , version 1 (07-04-2022)

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B. Gallo, Sergio de Almeida, José-Carlos M Bermudez, Chen Jie, Cédric Richard. Non-Destructive Prediction of Pork Meat Degradation using a Stacked Autoencoder Classifier on Hyperspectral Images. 2019 27th European Signal Processing Conference (EUSIPCO), Sep 2019, A Coruna, France. pp.1-5, ⟨10.23919/EUSIPCO.2019.8903164⟩. ⟨hal-03634157⟩
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