Peanuts and peanut-containing products have been linked to at least seven salmonellosis outbreaks worldwide in the past two decades. In response, the Technical Committee on Food Microbiology of the North American Branch of the International Life Sciences Institute collaborated with the American Peanut Council to convene a workshop to develop a framework for managing risk in low-moisture food commodities where large data sets are unavailable (using peanuts as the example). Workshop attendees were charged with answering questions regarding the appropriate statistical and scientific methods for setting log reduction targets with limited pathogen prevalence and concentration data, suitable quantities of data needed for determining appropriate log reduction targets, whether the requirement of a 5-log reduction in the absence of data to establish a target log reduction is appropriate, and what targeted log reduction would protect public health. This report concludes that the judgment about sufficient data is not solely scientific, but is instead a science-informed policy decision that must weigh additional societal issues. The participants noted that modeling efforts should proceed with sampling efforts, allowing one to compare various assumptions about prevalence and concentration and how they are combined. The discussions made clear that data and risk models developed for other low-moisture foods like almonds and pistachios may be applicable to peanuts. Workshop participants were comfortable with the use of a 5-log reduction for controlling risk in products like peanuts when the level of contamination of the raw ingredients is low (,1 CFU/g) and the process well controlled, even when limited data are available. The relevant stakeholders from the food safety community may eventually conclude that as additional data, assumptions, and models are developed, alternatives to a 5-log reduction might also result in the desired level of protection for peanuts and peanut products.
All Science Journal Classification (ASJC) codes
- Food Science