Akademik Veri Yönetim Sistemi
International Scientific Symposium Current Trends in Natural Sciences, Pitesti, Romanya, 19 - 21 Mayıs 2022, cilt.1, sa.1, ss.1
Machine learning algorithms have been commonly used in food drying processing. These algorithms are also effectively used for nonlinear processes. Estimation of drying characteristics is important for optimizing drying conditions. Estimating the properties such as moisture content, moisture rate and drying rate ensures accurate and high quality drying of the product under air-convective drying conditions. In this study, moisture ratio and drying rates of apple slices were estimated in air-convective drying conditions. Three machine learning algorithms (random forest-RF; artificial neural network-ANN; and k-nearest neighbors-kNN) were performed to estimate moisture ratio and drying rate. In the study, correlation coefficients were found to be above 0.85 in the estimation of humidity and drying rate for all algorithms. The present findings show that machine learning algorithms could be successfully applied for the estimation of drying characteristics.