Study of microwave and convective drying kinetics of pea pods (Pisum sativum L.): A new modeling approach using support vector regression methods optimized by dragonfly algorithm techniques

Abstract

Machine learning and mathematical modeling techniques have been conducted to model the thin layer drying kinetics of pea pods, under either microwave or conventional air drying,. The effect of nine different microwave output powers (200-1000 W) and five different ventilated oven temperatures (40, 60, 80, 100, and 120 C) on drying kinetics was studied. The experimental drying rates were fitted to 11 literature semi-empirical models to determine the kinetic parameters, finding the higher goodness-of-fit for the Midilli et al. model (average R 2 = 0.999 for both drying methods). Moreover, the data were modeled using support vector machine (SVM) for regression which was optimized with dragonfly algorithm (DA) technique. The best result was obtained by Gaussian kernel with the optimal parameters σ, C, and ε values estimated as 0.2871, 78.45, and 0, respectively. The small root mean square error (RMSE = 0.0132) and the high determination coefficient (R 2 = 0.9983) values proved how robust the SVM model is. DA-SVM techniques can reliably be utilized to describe the thin layer drying kinetics of pea pods. It is useful to provide models that can assist in the development of food process control algorithms, and provided insights into complex processes, for the technological design of microwave or convective drying for pea pods preservation. Practical applicationsDrying of by-products from pea processing industry was investigated as a critical step prior to their valorization. The drying of pea pods has never been investigated before which is the case of the present study whose objective was to study and model the

Prediction of CO2 and ethylene produced in‐packaged apricot under cold plasma treatment by machine learning approach

“…To compare the performance of applied machine learning methods with other studies, the results present the robustness of the models. Hadjout‐Krimat et al (2023) developed a new modeling approach using SVR methods optimized by dragonfly algorithm techniques. They could predict the drying kinetics of pea pods with a small RMSE (RMSE = 0.0132) and a high determination coefficient ( R 2 = 0.9983).…”

“…SVR uses kernel functions for mapping the input vector to a multidimensional space hence minimize the prediction error (Hadjout‐Krimat et al, 2023; Nirere et al, 2023). Two different SVR models consisting of radial basis functions (RBF) and polynomials have been applied.…”

“…To compare the performance of applied machine learning methods with other studies, the results present the robustness of the models Hadjout- Krimat et al (2023). developed a new modeling approach using SVR methods optimized by dragonfly algorithm techniques.…”

Mathematical Modeling Approach and Simulation in Food Drying Applications

“…Recent developments in the branch of food drying involve advancements in the development of mathematical models [1,2], spanning empirical, semi-empirical, and theoretical approaches [3,4]. Researchers have increasingly employed computational methods, including artificial neural networks [5,6], convolutional networks [7][8][9], random forests [10,11], support vector machines [12,13], and more, to analyze the impacts of diverse drying conditions and methods on food quality and safety. The integration of these computational techniques provides a sophisticated understanding of how different variables influence the drying process.…”