Artificial neural network and machine learning predictive model for assessing physicochemical properties of garlic slices (<i>Allium sativum L.</i>) during microwave-assisted convective drying process.

ABSTRACT: This study evaluates the physicochemical characteristics of garlic slices dried using a microwave-assisted convective dryer controlled by an artificial neural network. The chosen drying conditions included: microwave power (100, 200, and 300 W), air temperatures (45, 55, and 65 °C), and airflow velocity (0.3, 0.5, and 1.0 m/s). Results showed that at 65 °C, 300 W, and 0.3 m/s, the minimum flavor was 4.95 mg/g dry mass, marking a 39.50 % reduction in allicin content. The highest vitamin C content of 0.1751 mg/g with a water activity level of 0.505 was recorded at drying conditions of 1.0 m/s, 45 °C, and 100 W. However, it was observed that increasing power to 300 W at 45 °C and 0.5 m/s improved the rehydration ratio by 15.53 %. This study utilized precise ANN modelling to achieve an excelle

SUBMITTER: El-Mesery HS 

PROVIDER: S-EPMC12490423 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature