Optimization of the Drying Process for Gamma-Irradiated Mushroom Slices Using Mathematical Models and Machine Learning Algorithms

Abstract

Concerns over dried product quality and energy consumption have prompted researchers to explore integrated techniques for improving quality and reducing energy use. This study investigates the effect of gamma irradiation pretreatment (0, 1.2, 2.4, and 3.6 kGy) on button mushroom slices, followed by thin-layer drying at 50, 60, and 70 degrees C. The results indicated that increasing irradiation dose and drying temperature significantly reduced drying time. The Midilli model provided the best f & imath;t to the drying data (R2 = 0.9969-0.9998). Artificial neural networks (ANN) accurately predicted moisture variations, achieving R2 = 0.9975 and RMSE = 0.0220. The Support Vector Machine (SVM) algorithm, employing the Pearson universal kernel in normalized mode, also performed well, with R2 = 0.9939 and RMSE = 0.0344. Similarly, in the k-nearest neighbors (kNN) algorithm with three neighbors (k = 3), the R2 and RMSE values were 0.9888 and 0.0458, respectively. Gamma irradiation enhanced the effective diffusion coefficient (Deff) to 10.796 x 10-8 m2/s, and reduced activation energy (Ea) to 11.09 kJ/mol. The highest heat utilization efficiency (41.1%) was observed at 3.6 kGy and 50 degrees C. These findings highlight the potential of integrating gamma irradiation pretreatment and advanced drying techniques to optimize energy use and improve the quality of dried mushroom slices.