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A Web Resource for Quantitative and Predictive Food Microbiology
ComBase describes and predicts quantified microbial responses (growth and survival/inactivation) to different food environments. It can be used by companies to help them develop, produce, and store new food products, and by regulatory officers to aid them in conducting quantitative risk assessments.
United States Department of Agriculture
Arsenic in Rice and Rice Products Risk Assessment
In 2016, FDA published its risk assessment on the health risks of inorganic arsenic in rice and rice products. The assessment provides a quantitative estimate of lung and bladder cancer risk from long-term exposure to inorganic arsenic, and a qualitative assessment of potential non-cancer risks in certain vulnerable life stages.
Food and Drug Administration
Stochastic Human Exposure and Dose Simulation (SHEDS)
SHEDS models are probabilistic models that estimate human chemical exposures from inhalation, skin contact, and dietary and non-dietary ingestion. Estimates are based on available data on dietary consumption, human activity, and chemical levels in food, water, air and on surfaces. The models can generate predictions of aggregate (single chemical) and cumulative (multiple chemicals) exposures over time to improve risk assessments.
Environmental Protection Agency
Cumulative and Aggregate Risk Evaluation System Next Generation (CARES NG)
CARES NG is a probabilistic model used to estimate aggregate (single chemical) and cumulative (multiple chemicals) exposure to pesticides from multiple routes and sources. Its dietary module estimates dietary exposure from pesticide residues in food or drinking water using algorithms that incorporate EPA/OPP guidance. The module can estimate exposure and cancer risk for different populations and different time frames (acute, multi-day, chronic).
Cumulative and Aggregate Risk Evaluation System Next Generation