@inproceedings{bfa0b12e0f8a42ab8ac0e9ed46677cec,
title = "Non-destructive prediction of pork meat degradation using a stacked autoencoder classifier on hyperspectral images",
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.",
keywords = "Hyperspectral imaging, Machine learning, Meat quality assessment, Neural network",
author = "Gallo, \{B. B.\} and \{De Almeida\}, \{S. J.M.\} and Bermudez, \{J. C.M.\} and J. Chen and C. Richard",
note = "Publisher Copyright: {\textcopyright} 2019,IEEE; 27th European Signal Processing Conference, EUSIPCO 2019 ; Conference date: 02-09-2019 Through 06-09-2019",
year = "2019",
month = sep,
doi = "10.23919/EUSIPCO.2019.8903164",
language = "英语",
series = "European Signal Processing Conference",
publisher = "European Signal Processing Conference, EUSIPCO",
booktitle = "EUSIPCO 2019 - 27th European Signal Processing Conference",
}