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]

Abstract

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.