Estimating bulk density in leguminous grains with different traits using color parameters from digital images combined with artificial neural networks
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Data
2020-01-01
Autores
Floriam, Bruna Gava [UNESP]
Pereira, Fabíola Manhas Verbi [UNESP]
Filletti, Érica Regina [UNESP]
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Resumo
Dry grains from leguminous species, such as soybeans (Glycine max L.), common beans (Phaseolus vulgaris L.), chickpeas (Cicer arietinum L.) and corn (Zea mays L.), are regularly consumed for human nutrition. This paper showed the possibility of estimating bulk density as quality parameter of 4 different dry grains (soybeans, common beans, chickpeas and corn) in a same model using the average values of color descriptors from digital images combined with an artificial neural network, with low computational costs. These food products are good sources of carbohydrates, protein and dietary fiber, and they possess significant amounts of vitamins and minerals and a high energetic value. Estimation of the physicochemical properties of grains is challenging due to variations in shape, texture, and size and because the grain colors appear similar to the naked eye. In this work, an analytical method was developed based on digital images converted into ten color scale descriptors combined with a neural model to provide an accurate parameter for grain quality control with a low computational cost. The bulk densities of four type of grains, i.e., soybeans, beans, chickpeas and corn, were predicted using numerical data represented by the average values of color histograms of a ten color scale (red - R, green - G, blue - B, hue - H, saturation - S, value - V, relative RGB and luminosity - L) from digital images combined with artificial neural networks (ANNs). The reference bulk densities were empirically measured. A very good correlation between the reference values and values predicted by the ANN was achieved, and with a single ANN developed for the four grains, a correlation coefficient of 0.98 was observed for the test set. Moreover, the relative errors were between 0.01 and 5.6% for the test set.
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Palavras-chave
Bulk density, Digital images, Error propagation, Grain, Learning algorithm
Como citar
Ecletica Quimica, v. 45, n. 1, p. 11-17, 2020.