Artificial intelligence as a tool for predicting the quality attributes of garlic (Allium sativum L.) slices during continuous infrared-assisted hot air drying

Abstract

Effective drying methods are a highly suitable solution for ensuring stable food supply chains, reducing postharvest agricultural losses, and preventing the spoilage of perishable fruits and vegetables. Moreover, machine learning techniques are innovative and dependable, especially in addressing food spoilage and optimizing drying processes. This study utilized a continuous infrared (IR) hot air dryer to dry garlic (Allium sativum L.) slices. The experiments were conducted at different levels of IR power, air velocities (V), and temperature (T). The relationships between the input process parameters (IR, T, and V) and response parameters, including effective moisture diffusivity (D_eff), drying time, and physicochemical properties of the dried slices (rehydration ratio [RR], total color change, flavor strength, and allicin content in the garlic), were modeled using an artificial neural network (ANN). Our findings showed that the maximum D_eff of 6.8 × 10⁻¹⁰ m²/s and minimum drying time of 225 min were achieved with an IR of 3000 W/m², an air velocity of 0.7 m/s, and a temperature of 60°C. The total color change and RR values increased with IR and higher air temperature but declined with higher air velocity. Furthermore, the garlic's flavor strength and allicin content levels decreased as the IR and air temperature increased. The results demonstrated a significant influence of the independent parameters on the response parameters (p < 0.01). Interestingly, the ANN predictions closely matched the test data sets, providing valuable insights for understanding and controlling the factors affecting drying behaviors.