Computer image analysis for intramuscular fat segmentation in dry-cured ham slices using convolutional neural networks
Visualitza/Obre
Autor/a
Muñoz, I.
Gou, P.
Fulladosa, E.
Data de publicació
2019-06-12ISSN
0956-7135
Resum
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.
Tipus de document
Article
Versió del document
Versió acceptada
Llengua
English
Matèries (CDU)
663/664 - Aliments i nutrició. Enologia. Olis. Greixos
Pàgines
29
Publicat per
Elsevier
Publicat a
Food Control
Citació
Muñoz, I., Gou, P. and Fulladosa, E. 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.
Número de l'acord de la subvenció
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/
Programa
Qualitat i Tecnologia Alimentària
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