The study represents the first publication of a large FDA dataset on ochratoxin A (OTA) occurrence in U.S. foods. OTA was detected above the limit of detection in 3.5 percent of all samples (which were targeted based on their susceptibility to contamination).
Wageningen University researchers developed a hybrid machine learning modeling framework that considers crop growth stages, various future scenarios, and a large geographic region. The model predicted deoxynivalenol will present the greatest risk, with coastal countries, the UK, and northern France most affected.
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.
Incorporating a novel “bio-fumigation” method based on a natural compound produced by mustard plants, the micro-malting machine effectively addresses mycotoxins while preserving quality. USDA is working with industry to scale the prototype for widespread adoption.
Although generally detected at low levels, mycotoxins were present in all plant-based meat alternative and beverage samples, suggesting that cumulative exposure may pose health concerns.
The Spanish Federation of Agricultural Cooperatives of Murcia (Fecoam) points to recent RASFF food safety notifications about U.S. nuts demonstrating dangerous levels of aflatoxin contamination—with almonds exceeding EU maximum levels by three times in one instance—and calls for stricter import controls.
As the food safety threats posed by fungal contamination continue to grow, ILSI Europe has introduced a practical risk prioritization framework designed to identify which mycotoxins present the greatest risk to consumers, and where mitigation efforts should be concentrated.
This episode of Food Safety Five discusses three recent studies that examine the risk of Listeria monocytogenes growth from multi-species biofilm contamination, how imported seafood may spread antibiotic resistance genes, and a promising AI mycotoxin detection method.
A new study led by the University of South Australia offers a promising real-time mycotoxin detection method for the food industry that is based in artificial intelligence (AI) technology, and overcomes some of the limitations of traditional detection methods.
Adding to its existing certification for corn, ImagoAI’s Galaxy Mycotoxins Test has received the AOAC Performance Tested Methods (PTM) certification for aflatoxins, deoxynivalenol, fumonisins, and zearalenone in Wheat, Dried Distillers Grains With Solubles (DDGS), and Corn Gluten Meal (CGM).
