AFLATOXIN DETECTION USING ARTIFICIAL INTELLIGENCE TECHNIQUES: OPPORTUNITIES AND CHALLENGES

Mycotoxins are secondary metabolites that threaten the food and feed industries, in which aflatoxins are the most common and toxic of all mycotoxins. Aflatoxins can contaminate crop products at any stage – before cultivation, on the field, during transportation, storage, or processing period. It has become a global concern affecting the health of man and animals and the economy. There are several popular methods for determining and quantifying aflatoxin. However, early detection of this pollutant is critical for feed and food safety and human well-being. Traditional methods of aflatoxin detection though effective, often require expensive equipment and expertise, making them less accessible in resource-limited settings. This paper explores the growing role of artificial intelligence (AI) in enhancing aflatoxin detection. Artificial intelligence (AI) techniques, including machine learning (ML) algorithms, computer vision, and spectral imaging, offer more accurate, efficient, and affordable detection methods. Therefore, this review discusses some of the traditional methods of detecting aflatoxin and explores the prospective opportunity of using artificial intelligence to detect food and feed. It further discusses some challenges and recommends using ongoing AI techniques. This review concludes that advancement in AI-driven detection technologies, collaboration and capacity building will strengthening aflatoxin analysis and ensuring food and feed safety.