Estimating bulk density in leguminous grains with different traits using color parameters from digital images combined with artificial neural networks

Bruna Gava Floriam, Fabíola Manhas Verbi Pereira, Érica Regina Filletti


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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Costa, G. E. A., Queiroz-Monici, K. S., Reis, S. M. P. M., & Oliveira, A. C. Chemical composition, dietary fibre and resistant starch contents of raw and cooked pea, common bean, chickpea and lentil legumes. Food Chemistry 94 (2006) 327-330.

Bhattacharya, K. R. Rice quality, A guide to rice properties and analysis. Woodhead Publishing Series Limited, Cambridge, 2013.

Haque, E. Estimating bulk density of compacted grains in storage bins and modifications of Janssen's load equations as affected by bulk density. Food Science & Nutrition 1 (2013) 150-156.

Bart-Plange, A., & Baryeh, E. A. The physical properties of Category B cocoa beans. Journal of Food Engeneering 60 (2003) 219-227.

Wong, J.X.H., Liu, F.S.F., & Yu, H.Z. Mobile app-based quantitative scanometric analysis. Analytical Chemistry 86 (2014) 11966-11971.

Shouche, S. P., Rastogi, R., Bhagwat, S. G., & Sainis, J. K. Shape analysis of grains of Indian wheat varieties. Computers and Electronics in Agriculture 33 (2001) 55-76.

Chen, X., Xun, Y., Li, W., & Zhang, J. Combining discriminant analysis and neural networks for corn variety identification. Computers and Electronics in Agriculture 71S (2010) S48-S53.

Camargo, V. R., Santos, L. J., & Pereira, F. M. V. A Proof of Concept Study for the Parameters of Corn Grains Using Digital Images and a Multivariate Regression Model. Food Analytical Methods 11 (2018) 1852-1856.

Santos, P. M., Wentzell, P. D., & Pereira-Filho, E. R. Scanner digital images combined with color parameters: a case study to detect adulterations in liquid cow’s milk. Food Analytical Methods 5 (2012) 89-95.

Borin, A., Ferrão, M. F., Mello, C., Cordi, L., Pataca, L. C. M., Durán, N., & Poppi, R. J. Quantification of Lactobacillus in fermented milk by multivariate image analysis with least-squares support-vector machines. Analytical & Bioanalytical Chemistry 387 (2007) 1105-1112.

Pereira, F. M. V., Milori, D. M. B. P., Pereira-Filho, E. R., Venâncio, A. L., Russo, M. S. T., Martins, P. K. & Freitas-Astúa, J. Fluorescence images combined to statistic test for fingerprinting of citrus plants after bacterial infection, Analytical Methods 3 (2011) 552-556.

Hagan, M.T., Demuth HB, Beale M. Neural network design. PWS Publishing Company, Boston, 1996.

Haykin, S. Neural Networks: a comprehensive foundation. Prentice Hall, New Jersey, 1999.

Li, G., & Shi, J. On comparing three artificial neural networks for wind speed forecasting. Applied Energy 87 (2010) 2313-2320.

Nafey, A.S. Neural network based correlation for critical heat flux in steam-water flows. International Journal of Thermal Sciences 48 (2009) 2264-270.

Niemi, H., Bulsari, A., & Palosaari, S. Simulation of membrane separation by neural networks. Journal of Membrane Science 102 (1995) 185-191.

Zhang, Z., & Friedrich, K. Artificial neural networks applied to polymer composites: a review. Composites Science and Technology 63 (2003) 2029-2044. 10.1016/S0266-3538(03)00106-4.

Tanzifi, M., Yaraki, M. T., Kiadehi, A. D., Hosseini, S. H., Olazar, M., Bharti, A. K., Agarwal, S., Gupta, V. K., & Kazemi, A. Adsorption of Amido Black 10B from aqueous solution using polyaniline/ SiO2 nanocomposite: Experimental investigation and artificial neural network modelling. Journal of Colloid and Interface Science 510 (2018) 246-261. 10.1016/j.jcis.2017.09.055.

Ramil, A., López, A. J., & Rivas, T. A computer vision system for identification of granite-forming minerals based on RGB data and artificial neural networks. Measurement 117 (2018) 90-95.

Walczak, S., & Velanovich, V. Improving prognosis and reducing decision regret for pancreatic cancer treatment using artificial neural networks. Decision Support Systems 106 (2018) 110-118.

Ortega-Zamorano, F., Jerez, J.M., Juarez, G. E., & Franco, L. FPGA Implementation of Neurocomputational Models: Comparison Between Standard Back-Propagation and C-Mantec Constructive Algorithm. Neural Processing Letters 46 (2017) 899-914.

Filletti, É. R., & Roque, W. L. Estimating the mechanical competence parameter of the trabecular bone: a neural network approach. Research in Biomedical Engineering 32 (2016) 137-143.

León-Roque, N., Abderrahim, M., Nuñez-Alejos, L., Arribas, S.M., & Condezo-Hoyos, L. Prediction of fermentation index of cocoa beans (Theobroma cacao L.) based on colour measurement and artificial neural networks. Talanta 161 (2016) 31-39. 10.1016/j.talanta.2016.08.022.

Dubey, B. P., Bhagwat, S. G., Shouche, S. P., & Sainis, J. K. Potential of Artificial Neural Networks in Varietal Identification using Morphometry of Wheat Grains. Biosystems Engineering 95 (2006) 61-67.

Demuth, H., Beale, M., & Hagan, M. Neural Network toolbox 6: User´s Guide. The MathWorks, Natick, 2010.



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