Microstructural analysis in foods of vegetal origin: An approach with convolutional neural networks [Análisis microestructural en alimentos de origen vegetal: Una aproximación con redes neuronales convolucionales]
Date
2020-04-30Author(s)
Castro, Wilson
Yoshida, Hideaki
Seguí Gil, Lucia
Mayor López, Luis
Oblitas Cruz, Jimy
De la Torre Gomora, Miguel
Avila George, Himer
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ABSTRACT
The microstructure is a factor in the knowledge and prediction of properties in food and the associated changes during processing. The objective of this work was to evaluate the feasibility of using a convolution neural network (CNN) for the discrimination of structures in foods of vegetable origin. Micrographs of pumpkin were processed digitally to improve the detection of structures (cells and intercellular spaces). Later the found elements were classified in two sets, using a trained operator. The implementation made use of a pre-trained network AlexNet, performing cross-validation, and one hundred repetitions randomizing the information delivered to the training and validation processes. The statistics obtained were accuracy and F-measure. Therefore, the use of convolutional neural networks shows potential for the discrimination of structures in foods of vegetal origin.
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Bibliographic citation
Castro, W. ...[et al]. (2020). Microstructural analysis in foods of vegetal origin: An approach with convolutional neural networks [Análisis microestructural en alimentos de origen vegetal: Una aproximación con redes neuronales convolucionales]. 8th International Conference On Software Process Improvement (CIMPS), 1-5. https://doi.org/10.1109/CIMPS49236.2019.9082421
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El texto completo de este trabajo no está disponible en el Repositorio Académico UPN por restricciones de la casa editorial donde ha sido publicado.
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