Comparative analysis of thin-layer drying models in predicting papaya moisture content using optimization algorithms

doi:10.1108/EC-07-2025-0776

Comparative analysis of thin-layer drying models in predicting papaya moisture content using optimization algorithms

2026-01-01

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DOI：10.1108/EC-07-2025-0776

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PurposeThis study aims to comparatively analyze 15 widely used thin-layer drying models for predicting the moisture ratio (MR) of papaya slices and to evaluate the performance of four optimization algorithms - HFO-1, IGWO, GRG and LM - in estimating model parameters.Design/methodology/approachPapaya slices with thicknesses of 6, 8 and 10 mm were dried at air temperatures of 70, 80 and 90 degrees C in a convective tray dryer. Parameter estimation for each model was performed using four optimization algorithms. Model accuracy was evaluated using RMSE and MAE. Comparative performance ratios and model-algorithm matches were examined to determine the most accurate and stable methods.FindingsOverall, the HFO-1 method is superior to LM, achieving lower RMSE values in 83.70% of all evaluated cases, with 8.15% showing equal performance. Compared with GRG, RMSE values are equal in 69.63% of the cases, while HFO-1 performs better in 20.74%. In relation to IGWO, the RMSE values are equal in 52.60% of the cases, while HFO-1 is better in 45.18% of them. Across all drying temperatures and slice thicknesses, the Modified Henderson and Pabis-I model demonstrated the best predictive performance. The findings showed that HFO-1 consistently achieved strong error minimization and stable convergence, particularly under nonlinear and multimodal model structures.Research limitations/implicationsThe study focuses on convective drying of papaya slices under controlled laboratory conditions. Results may vary for other drying techniques or food materials.Practical implicationsSelecting the most effective algorithm-model combination can support better drying process design, improve energy efficiency and enhance moisture prediction accuracy in industrial food drying applications.Originality/valueThis study is the first to apply both IGWO and HFO-1 algorithms to optimize parameters of thin-layer drying models. Evaluating 15 different models simultaneously makes this work one of the most comprehensive analyses in drying-model optimization. HFO-1's strong accuracy and convergence capacity highlight its suitability for broader applications in food engineering and drying kinetics.

Keywords:

Papaya

Moisture ratio

Thin layer

Drying models

Algorithms

Comparative analysis of thin-layer drying models in predicting papaya moisture content using optimization algorithms

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