A recent publication from the Food and Agriculture Organization of the United Nations (FAO) has provided an in-depth review of early warning systems for food safety risks, an explanation of available open access tools, and the potential applications of Big Data and artificial intelligence (AI) in the field.
The advent of artificial technologies like electronic noses, electronic eyes, and electronic tongues has fundamentally reshaped our approach to evaluating food samples, as underscored by their ability to capture intricate sensory attributes. The symbiotic relationship between artificial instruments and machine learning underscores their potential to reshape the food industry, ensuring that the food we relish is not only delectable but also safe and of the highest quality.
The use of artificial intelligence (AI) tools like big data and machine learning for antimicrobial resistance (AMR) surveillance in livestock production shows promise for informing AMR mitigation efforts, according to a recent study led by University of Nottingham researchers.
The U.S. Food and Drug Administration (FDA) has launched the third phase of the Artificial Intelligence (AI) Imported Seafood Pilot program, which uses AI and machine learning to strengthen the screening process for seafood imports entering the U.S.
Opportunities exist for the use of data science in preventing and mitigating foodborne disease outbreaks, often using publicly accessible data. This article examines machine learning/data science approaches, including whole-genome sequencing, to enhance food safety.