Toxicological Evaluation of the Undesirable Components in Processed Infant Foods Using Artificial Intelligence

Abstract

Infant foods and baby formulas are becoming increasingly popular across the globe owing to their ease of consumption and nutritional value specific to infants. Impurities may find their way into the food chain at any point from the acquisition of raw materials to final packaging, causing serious health hazards. Fruit and vegetable-based infant foods have also been reported to be contaminated with plant toxins, mycotoxins, and microbial toxins, in addition to microbial residues that can prove extremely hazardous for infants. Current trends in artificial intelligence (AI) bring exciting opportunities for the instantaneous and nondestructive analysis of such contaminants. AI-driven methods, such as machine learning (ML) and deep learning (DL) algorithms, are capable of analyzing vast datasets from spectroscopic, imaging, or sensor inputs to detect trace amounts of toxic substances with high accuracy. This review aims to identify the different poisonous substances and contaminants that are harmful to infant health and to emphasize how AI systems can enhance the identification, tracking, and prevention of such contaminants in baby food items.