A JEMRA meeting was convened to help inform discussions about potential updates to Codex Alimentarius guidance, reflecting how scientific advances could strengthen microbiological risk assessments for food safety.
The Environmental Working Group’s 2026 Shopper’s Guide to Pesticides in Produce highlights PFAS pesticides for the first time. Although EWG recently updated its methodology, scientists argue it still does not consider key exposure science and risk assessment principles, therefore misleading consumers about the health risks of conventionally grown produce.
Researchers developed a quantitative microbiological risk assessment (QMRA) framework that evaluates the public health, environmental, and economic trade-offs of microbiological sampling plans. They suggested microbiological sampling may be most useful when risk-based or as a verification tool.
Garlic carries a distinct bacterial signature reflective of the soil in which it was grown, enabling geographic identification based on microbial composition. A novel method using microbiome data and AI analysis potentially offers a low-cost authentication technique.
The low-cost approach enables simultaneous detection of multiple foodborne pathogens and spoilage microorganisms in a shorter timeframe than traditional detection methods, without requiring advanced technical training.
The platform uses a DNAzyme-crosslinked hydrogel that produces a visible color change when E. coli is present, enabling equipment-free, point-of-use detection. It successfully detected E. coli in a range of foods, even when other pathogens were present.
The Infant Formula Safety Modernization Act includes provisions about expanded pathogen testing requirements and compulsory environmental monitoring for infant formula manufacturers, among other mandates.
Marking the country’s first use of whole genome sequencing (WGS) in an active foodborne illness outbreak investigation, advanced genomics enabled Moldovan authorities to rapidly solve and respond to a salmonellosis outbreak that sickened more than 140 people in 2025.
The researchers positioned the machine learning model as a low-cost complement to traditional testing workflows, helping dairy processors enhance food safety while targeting laboratory resources.