Artificial intelligence method developed for classifying raw sugarcane in the presence of the solid impurity
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Abstract
An investigation dedicated to evaluating a big issue in biorefineries, solid impurity in raw sugarcane, is presented. This relevant industrial sector requests a high-frequency, low-cost, and noninvasive method. Then, the developed method uses the averaged color values of ten color-scale descriptors: R (red), G (green), B (blue), their relative colors (r, g, and b), H (hue), S (saturation), V (value) and L (luminosity) from digital images acquired from 146 solid mixtures among sugarcane stalks and solid impurity — vegetal parts (green and dry leaves) and soil. The solid mixture of samples was prepared considering desirable and undesirable scenarios for the solid impurity amounts. The outstanding result was revealed by an artificial neural network (ANN), achieving 100% of accurate classifications for two ranges of raw sugarcane in the samples: from 90 to 100 wt% and from 41 to 87 wt%. Low-computational cost and a simple setup for image acquisition method could screen solid impurity in sugarcane shipments as a promising application.
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References
Andrade, D. F.; Guedes, W. N.; Pereira, F. M. V. Detection of chemical elements related to impurities leached from raw sugarcane: Use of laser-induced breakdown spectroscopy (LIBS) and chemometrics, Microchem. J. 2018, 137, 443–448. https://doi.org/10.1016/j.microc.2017.12.005.
Braga, A. P.; Carvalho, A. C. P. L. F.; Ludermir, T. B. Redes Neurais Artificiais: Teoria e Aplicações; Livros Técnicos e Científicos, 2000.
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 Anal. Meth. 2018, 11, 1852–1856. https://doi.org/10.1007/s12161-017-1028-6.
Capitán-Vallvey, L. F.; López-Ruiz, N.; Martínez-Olmos, A.; Erenas, M. M.; Palma, A. J. Recent developments in computer vision-based analytical chemistry: A tutorial review. Anal. Chim. Acta 2007, 899, 23–56. https://doi.org/10.1016/j.aca.2015.10.009.
Diniz, P. H. G. D. Chemometrics‐assisted color histogram‐based analytical systems. J. Chemom. 2020, 34 (12), e3242. https://doi.org/10.1002/cem.3242.
Eggleston, G.; Grisham. M.; Antoine, A. Clarification properties of trash and stalk tissues from sugar cane. J. Agric. Food Chem. 2010, 58 (1), 366–373. https://doi.org/10.1021/jf903093q.
Guedes, W. N.; Pereira, F. M. V. Classifying impurity ranges in raw sugarcane using laser-induced breakdown spectroscopy (LIBS) and sum fusion across a tuning parameter window. Microchem. J. 2018, 143, 331–336. https://doi.org/10.1016/j.microc.2018.08.030.
Guedes, W. N.; Pereira, F. M. V. Raw sugarcane classification in the presence of small solid impurity amounts using a simple and effective digital imaging system. Comput. Electron. Agric. 2019, 156, 307–311. https://doi.org/10.1016/j.compag.2018.11.039.
Guedes, W. N.; Santos, L. J.; Filletti, É. R.; Pereira, F. M. V. Sugarcane stalk content prediction in the presence of a solid impurity using an artificial intelligence method focused on sugar manufacturing. Food Anal. Methods 2020, 13, 140–144. https://doi.org/10.1007/s12161-019-01551-2.
Lisboa, I. P.; Cherubin, M. R.; Lima, R. P.; Cerri, C. C.; Satiro, L. S.; Wienhold, B. J.; Schmer, M. R.; Jin, V. L.; Cerri, C. E. P. Sugarcane straw removal effects on plant growth and stalk yield. Ind. Crops Prod. 2018, 111, 794–806. https://doi.org/10.1016/j.indcrop.2017.11.049.
Norris, C. P.; Norris, S. C.; Landers, G. P. A new paradigm for enhanced industry profitability: Post-harvest cane cleaning. In Proceedings of the 37th Conference of the Australian Society of Sugar Cane Technologists, April 28-30, 2015, Bundaberg, Queensland, Australia. Australian Society of Sugar Cane Technologists: Mackay, Australia, 2015.
Pereira, F. M. V.; Bueno, M. I. M. S. Image evaluation with chemometric strategies for quality control of paints. Anal. Chim. Acta 2007, 588 (2), 184–191. https://doi.org/10.1016/j.aca.2007.02.009.
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. Anal. Methods 2011, 3, 552–556. https://doi.org/10.1039/c0ay00538j.
Romera, J. P. R.; Barsanelli, P. L.; Pereira, F. M. V. Expeditious prediction of fiber content in sugar cane: An analytical possibility with LIBS and chemometrics. Fuel 2016, 166, 473–476. https://doi.org/10.1016/j.fuel.2015.11.029.
Santos, M. C.; Nascimento, P. A. M.; Guedes, W. N.; Pereira-Filho, E. R.; Filletti, E. R.; Pereira, F. M. V. Chemometrics in analytical chemistry – an overview of applications from 2014 to 2018. Eclet. Quim. J. 2019, 44 (2), 11–25. https://doi.org/10.26850/1678-4618eqj.v44.2.2019.p11-25.