چکیده مقاله
Food fraud, encompassing economically motivated adulteration and mislabeling, poses a significant threat to consumer safety and industry trust, particularly within increasingly complex global supply chains Existing detection methods are often reactive, expensive, and sample intensive This extended abstract proposes the development and deployment of Artificial Intelligence AI and Machine Learning ML models—specifically Anomaly Detection algorithms to proactively identify high risk nodes and transactions within the food supply chain By integrating diverse data streams e g , blockchain records, commodity price fluctuations, weather data, and import/export logs , AI can create a predictive risk score for specific ingredients or product batches, moving the industry from detection to prevention of food fraud before it occurs
کلیدواژهها
نویسندگان
Amir Salar Montazerfarj
Department of Food Science and Technology, Varamin Pishva Branch, Islamic Azad University, Varamin, Iran
شیوه ارجاع
Montazerfarj, Amir Salar,1404,Artificial Intelligence and Predictive Modeling for Mitigating Food Fraud in Complex Global Supply Chains,The first national conference environment, smart agriculture, food security,Varamin
ارائهشده در
مجموعه مقالات اولین همایش ملی محیط زیست، کشاورزی هوشمند، امنیت غذایی28 آبان 1404 · ورامین