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Researchers have introduced a novel, thermal biosensor for real-time detection of Escherichia coli,demonstrating its ability to detect the pathogen in milk without sample preparation. The sensor would be easy to mass produce, and shows potential as a low-cost, rapid tool for onsite microbial indication.
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, Listeriamonocytgenes, 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.
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.