Artificial intelligence and machine learning models for predicting and evaluating the influence of shelf-life environments and packaging materials on garlic (<i>Allium Sativum</i> L) physicochemical and phytochemical compositions.

ABSTRACT: The nutritional content and quality of garlic, a crop widely consumed, must be preserved after harvesting by overcoming several challenges. The necessity of this study arises from the growing demand for effective postharvest management solutions that can extend shelf life, maintain the nutritional integrity of garlic and enhance consumer satisfaction. This study explores the application of machine learning (ML) and artificial intelligence (AI) to predict the effects of storage environments and packaging materials on garlic's physicochemical properties, enzymatic activities, phytochemical content, and antioxidant characteristics. The results show that temperature significantly influenced most parameters, except for anthocyanin, and that packaging impacted all variables. Storing garlic at 4 

SUBMITTER: El-Mesery HS 

PROVIDER: S-EPMC12274686 | biostudies-literature | 2025 Jul

REPOSITORIES: biostudies-literature