Modelling the sphericity of cereal grains after different drying treatments

This paper analyses the influence of different drying methods (fluidized, convection, and vacuum drying) and temperatures (60?C, 70?C, and 80?C) on the morphometric properties and sphericity of maize, wheat, and barley grains. The results indicate that the grain type has the greatest influence on basic dimensions and sphericity, while the effects of temperature and drying method are statistically significant but less pronounced. An artificial neural network was developed to predict grain sphericity with exceptional accuracy (R? = 0.999, RMSE = 0.01, and MBE = 0.001), confirming its reliability and applicability in modelling the physical properties of grains. The findings contribute to a better understanding of the influence of technological drying parameters on grain quality and indicate artificial neural network suitability for optimizing and controlling drying processes.