Real-Time Colour Monitoring and Predictive Modelling of Shahani Date Slices during Electrohydrodynamic-Assisted Drying

Articles in Press

Document Type : Research Article- En

Authors

S. S Mohtasebi ¹

M. Moradi ^{2, 3}

M. Keramat-Jaromi ³

¹ Department of Agricultural Machinery Engineering, Faculty of Agricultural Engineering and Technology, University of Tehran, Karaj, Iran

² Department of Biosystems Engineering, Faculty of Agriculture, Ferdowsi University of Mashhad, Mashhad, Iran

³ Department of Biosystems Engineering, Faculty of Agriculture, Shiraz University, Shiraz, Iran

https://doi.org/10.22067/jam.2026.100880.1527

Abstract

This study investigated how drying temperature, air velocity, and drying time affect the color evolution of Shahani date slices during electrohydrodynamic (EHD)-assisted drying integrated with forced convection. Drying experiments were conducted at 25, 35, and 60 °C and air velocities of 0.5, 1.0, and 1.5 m s⁻¹ until the moisture content reached approximately 12% (wet basis). An online machine vision system monitored the CIELAB coordinates (L*, a*, and b*), total color difference (ΔE), Chroma, Hue angle, and browning index (BI) during drying. To facilitate comparison of color changes relative to the initial state, relative values of lightness (L*/L₀), redness (a*/a₀), yellowness (b*/b₀), and browning index (BI/BI₀) were used. Linear mixed-effects models evaluated the main and interactive effects of the drying variables, while polynomial regression models were developed for prediction. Drying time decreased markedly with increasing temperature and air velocity, with the shortest duration obtained at 60 °C and 1.5 m s⁻¹. Color evolution was time-dependent and parameter-specific: L*/L₀ and b*/b₀ generally decreased, whereas relative redness, ΔE, and BI/BI₀ increased. Significant interactions indicated that the effects of drying conditions on color responses varied with drying stage. The predictive models, based on MR, temperature, and air velocity, achieved validation-set R² values of 0.8717–0.9490, with corresponding RMSE and MAE values of 0.00292–0.06375 and 0.00226–0.05148, respectively. These results demonstrate the potential of combining online color monitoring with interpretable models for non-destructive process monitoring and future optimization of EHD-assisted drying.

Keywords

Colour evolution

Machine vision

Moisture ratio

Polynomial regression

Subjects

• Postharvest, Processing and Agri-food Engineering

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Authors retain the copyright. This is an open access article distributed under Creative Commons Attribution 4.0 International License (CC BY 4.0)

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