Computer image analysis for intramuscular fat segmentation in dry-cured ham slices using convolutional neural networks
Determination of intramuscular fat (IMF) content in dry cured meats is critical because it affects the sensory quality and consumer's acceptability. Recently, deep learning has become one of the most promising techniques in machine learning for image analysis. However, few applications in food products are found in the literature. This study presents the application of deep learning for the detection of intramuscular fat (IMF) in images of slices of dry cured ham. 8 convolutional neural networks (CNNs) have been studied and compared using segmented images (252 for training, 61 for validation and 62 for testing). The performance was compared to other simple CNNs. CNNs were able to segment IMF with an overall pixel accuracy of 0.99 and a recall and precision rates for fat near 0.82 and 0.84, respectively, using a limited number of training images. However, performance is affected by the quality of the ground truth due to the difficulty of labelling correctly pixels.
633 - Cultius i produccions
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Muñoz, I., P. Gou, and E. Fulladosa. 2019. "Computer Image Analysis For Intramuscular Fat Segmentation In Dry-Cured Ham Slices Using Convolutional Neural Networks". Food Control 106: 106693. Elsevier BV. doi:10.1016/j.foodcont.2019.06.019.
Grant agreement number
INIA/Programa Estatal de promoción del talento y su empleabilidad en I+D+I/RTA2013-00030-C03-01/ES/Caracterización y detección objetiva de defectos de textura en jamón curado mediante tecnologías no destructivas. Desarrollo y evaluación de medidas correctoras/
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