Predictive modeling of garlic quality in hybrid infrared-convective drying using artificial neural networks

Abstract

Artificial Neural Networks (ANN) application for enhancing food drying processes has been gaining traction within the food industry, particularly because of its potential to optimize conditions while preserving quality. This study delves into the effects of varying convective temperatures (40, 50, 60 °C), air velocities (0.7, 1.0, and 1.5 m/s), and infrared radiation intensities (1500, 2000, 3000 W/m²) on the drying efficiency and quality of garlic slices. Additionally, it explores the capability of ANN to predict optimal drying conditions to balance time efficiency and quality retention. Experimental observations revealed that a combination of a 60°C air temperature, 1.0 m/s air velocity, and 3000 W/m² infrared radiation minimized the drying time to 4.5 h. However, this setting also resulted in a 13.4 % reduction in Allicin content and a decrease in flavor intensity to 4.09 mg/g, underscoring the complexity of optimizing drying conditions without compromising key quality attributes. The study highlights the intricate relationships between drying parameters and garlic quality through precise ANN modeling, which achieved an exceptional fit. Principal Component Analysis (PCA) further elucidated the inverse correlation between drying time and critical quality factors of color, Allicin content, and flavor. Utilizing the Self-Organizing Map (SOM) technique, the study identified five distinct optimization zones, suggesting that lower temperatures, intermediate infrared levels, and higher air velocities present an optimal balance for enhancing garlic slice quality while reducing drying time. This research underscores the potential of advanced computational tools in refining food drying processes, offering valuable insights for the food industry in its quest to improve efficiency without sacrificing product quality.