Bacillus Predict - Predictive tool for dairy products
Bacillus cereus is a human pathogen that forms spores, and these are present in many environments including milk, dairy products, and cheeses. Disease outbreaks associated with B. cereus have been reported for cheeses, milk and milk powder. B. cereus spores survive heating (pasteurization; spray drying) and may occur in milk and whey powder. Growth of psychrotolerant and mesophilic/thermo‐tolerant variants of B. cereus can be observed from 5 to > 50°C and they are important to control in dairy products including reconstituted milk and whey powders. So far, there are no recommendations on suitable formulations to prevent their growth, and predictive models will facilitate the development of dairy products stabilized against growth of B. cereus.
By: Grith Mortensen
Several growth and growth boundary models are available for B. cereus. However, available models are either not validated for dairy products or they do not contain the effect of relevant factors including organic acids and ranges of pH (4.6‐7.0) and temperature (5 ‐ > 50°C) needed to predict growth in different dairy products and ingredients. New validated dairy models are needed to help manage growth of the pathogen.
The overall objective of the proposed project is to develop a predictive food microbiology tool that allows the growth boundaries, growth potential and spore formation to be predicted for psychrotolerant and mesophilic/thermo‐tolerant variants of B. cereus. The models can contribute to product development, risk assessments and documentation of food safety in relation to formulation and processing of dairy products and ingredients. The new models are included in user‐friendly software to benefit the entire dairy sector. Existing growth models for psychro‐ and thermo‐tolerant B. cereus will be expanded to include the effect of relevant characteristics for dairy products. A new modelling technique is used in combination with the developed growth models to predict spore formation by B. cereus in processing of dairy products and ingredients e.g., at different temperatures. This is important to predict conditions that prevent the formation of resistant spores.
The developed predictive tool can reduce costs and time for product/process development and it has potential to contribute to improved food safety.
Project period: January 2022 - December 2024
Budget: 4.337.016 DKK
Financing: Milk Levy Fund, National Food Institute, Technical University of Denmark, Arla Foods and Arla Foods Ingredients
Project manager: Paw Dalgaard
Institution: National Food Institute, Technical University of Denmark
Participants: National Food Institute, Technical University of Denmark, Arla Foods, Arla Foods Ingredients