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

Ehsan Fartash Naeimi, Mohammad Hadi Khoshtaghaza (), Kemal Çağatay Selvi, Nicoleta Ungureanu () and Soleiman Abbasi
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Ehsan Fartash Naeimi: Department of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Ondokuz Mayıs University, 55139 Samsun, Türkiye
Mohammad Hadi Khoshtaghaza: Department of Biosystems Engineering, Faculty of Agriculture, Tarbiat Modares University, Tehran 14115-336, Iran
Kemal Çağatay Selvi: Department of Agricultural Machinery and Technologies Engineering, Faculty of Agriculture, Ondokuz Mayıs University, 55139 Samsun, Türkiye
Nicoleta Ungureanu: Department of Biotechnical Systems, Faculty of Biotechnical Systems Engineering, National University of Science and Technology Politehnica Bucharest, 060042 Bucharest, Romania
Soleiman Abbasi: Department of Food Science and Technology, Faculty of Agriculture, Tarbiat Modares University, Tehran 14115-336, Iran

Agriculture, 2024, vol. 14, issue 12, 1-21

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 °C. The results indicated that increasing irradiation dose and drying temperature significantly reduced drying time. The Midilli model provided the best fıt to the drying data (R 2 = 0.9969–0.9998). Artificial neural networks (ANN) accurately predicted moisture variations, achieving R 2 = 0.9975 and RMSE = 0.0220. The Support Vector Machine (SVM) algorithm, employing the Pearson universal kernel in normalized mode, also performed well, with R 2 = 0.9939 and RMSE = 0.0344. Similarly, in the k-nearest neighbors (kNN) algorithm with three neighbors (k = 3), the R 2 and RMSE values were 0.9888 and 0.0458, respectively. Gamma irradiation enhanced the effective diffusion coefficient (D eff ) to 10.796 × 10 −8 m 2 /s, and reduced activation energy (E a ) to 11.09 kJ/mol. The highest heat utilization efficiency (41.1%) was observed at 3.6 kGy and 50 °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.

Keywords: irradiation; artificial neural network model; k-nearest neighbors; SVM algorithm; activation energy; heat utilization efficiency (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2024
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