Based on data from the Salinas Valley, California growing region, the model could complement existing produce safety assessments by helping regulators, researchers, and industry stakeholders identify locations that may warrant additional environmental investigation.
The researchers determined that probabilistic modeling could help food businesses capitalize on existing scientific data to map processing and formulation conditions for controlling non-proteolytic C. botulinum in refrigerated, processed foods of extended durability.
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.
In a new paper supported by the Institute for the Advancement of Food and Nutrition Sciences (IAFNS), experts demystify Probabilistic Exposure Assessments (PEAs)—a more accurate approach for estimating dietary exposure to chemicals—by offering a comprehensive overview of their history, applications, and regulatory guidance.
This episode of Food Safety Five discusses a new study that leverages a novel quantitative microbial risk assessment (QMRA) model that suggests that half of E. coli O157:H7 illnesses linked to romaine lettuce are caused by contamination via untreated overhead irrigation water.
Using a newly developed quantitative microbial risk assessment (QMRA) model, researchers have identified interventions along the U.S. romaine lettuce supply chain that would most effectively reduce E. coli contamination. The QMRA is publicly available for use.
Researchers from the University of Córdoba
in Spain have developed a model for predicting the growth of Listeria monocytogenes in artisanal cheeses, which will be especially useful to producers that must demonstrate compliance with recently expanded EU regulations for controlling the pathogenin ready-to-eat (RTE) foods.
A research project funded by the Center for Produce Safety is developing a flexible computer model that enables food industry users to evaluate potential contamination risks along the supply chain and relevant control strategies.
Intended to inform food safety decision-making, a new risk assessment model developed by researchers at Penn State University helps milk processors evaluate possible consumer exposure to Bacillus cereus from milk subjected to high-temperature, short-time pasteurization.
A study has identified significant deficiencies in existing quantitative risk assessment models for Listeria monocytogenes on produce, such as failure to consider important contamination factors in primary production, among other gaps.
