Artificial Intelligence (AI) with optical imaging may be a promising solution for detecting pathogens in foods, and would save the food industry time and resources, according to a recent study.

Food safety sampling and testing strategies must seek ways to adapt food safety plans that reflect the reality of contamination to improve hazard detection and ultimately help ensure that food is safe for consumers. One solution is to maximize the power of sampling plans to detect target hazards present at explicitly defined risk levels—prevalence, level, and/or distribution. This would allow food safety professionals to better manage risk in their specific system.

Sixth Wave Innovations Inc. recently announced that its Accelerated Molecularly Imprinted Polymer (AMIPs™) technology has expanded its library of detectable pathogens, which already includes Escherichia coli, to encompass Salmonella, Listeria monocytgenes, and Sarcina.

Scientists have developed a rapid detection method for microbial contaminants in food that can identify the presence of certain pathogens by color in as little as one hour.

Researchers at the University of Georgia are developing a biosensor-based rapid diagnostic test for Listeria monocytogenes in food.

A recent study suggests that the cold foods supply chain is the optimal environment for the COVID-19 virus to spread over long distances. The study explores various prevention and testing methods that could be used to mitigate the pathogen’s spread through cold-chain foods.

A Michigan State University-led research team has received a grant from the U.S. Department of Agriculture to develop a rapid biosensor test for foodborne pathogens, with a focus on Salmonella and Campylobacter in poultry.

A recent study explores the use of nanosensors and biosensors as an easy, cost-effective, and rapid method of detecting foodborne pathogens.

Pthotrak’s Rapid Pathogen Detection Enrichment Technology is the first AOAC-certified product to enable pathogen results within an eight-hour shift.