Monitoring of intergranular variables for predicting technical breakage of wheat grains stored in vertical silos

• Published in: Journal of stored products research Vol. 102; p. 102115
• Main Authors: Leal, Marisa Menezes, Rodrigues, Dágila Melo, de Moraes, Rosana Santos, Acosta Jaques, Lanes Beatriz, Timm, Newiton da Silva, Coradi, Paulo Carteri
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01-05-2023
• Subjects: Carbon dioxide; Dry matter loss; Grain conservation; Intergranular air temperature; Intergranular relative humidity; Grain conservation; Intergranular relative humidity; Dry matter loss; Carbon dioxide; Intergranular air temperature
• ISSN: 0022-474X; 1879-1212
• DOI: 10.1016/j.jspr.2023.102115

The increase in the silos temperature and intergranular relative humidity can alter the equilibrium moisture content of the grains mass causing losses. Thus, the objective was to evaluate the temporal monitoring of temperature, relative humidity, and intergranular carbon dioxide for the prediction of dry matter loss in wheat grains stored in vertical silos, using a mathematical model and machine learning algorithms. Under these conditions, wheat grains had a slight increase in metabolic activity, causing a dry matter loss of 0.035–0.041%. The intergranular temperature, relative humidity, and carbon dioxide results were similar to the measurements on the surface of the grain mass. It was concluded that the monitoring of temperature, relative humidity, and the concentration of carbon dioxide in the intergranular air indirectly and early determined the changes in the quality of wheat grains during storage. Although the metabolic activity of wheat grains was low, as the lots remained in hygroscopic equilibrium with moisture contents close to 12% (w.b.), the temporal detection characterized the loss of dry matter and the reduction of grain weight stored. Finally, the Artificial Neural Networks and Multiple Linear Regression model satisfactorily predicted the dry matter loss of stored wheat grain mass.
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•Technologies for monitoring and predicting dry matter losses in stored wheat grains.•EMC and CO2 concentration monitoring indicated alterations in the stored wheat.•The temporal detection characterized dry matter losses in the stored wheat grains.•The ANNs model satisfactorily predicted the loss of dry matter of stored wheat.