Abstract
A cost-effective algorithm is presented, using a virtual dataset of growth rates from a cocktail of Bacillus cereus strains, for developing an open access, extended-range secondary growth model. Extended-range growth models can span the range of processing conditions typically used in food manufacturing and are therefore more relevant for industry. The open access extended-range secondary growth model for a cocktail of B. cereus strains was created using publicly available data, and the methodology can be adapted for modelling growth of other pathogens. An extended-range model can help manage B. cereus hazards in novel food categories with non-traditional formulations as estimations of B. cereus risks in these foods become more precise. This open access model, however, needs to be validated using data from B. cereus strain cocktails isolated from production facilities. Once validated, these independent factor models are valuable tools, in a pathogen decision support platform, which are tuned to local production environments. Such a platform can address the needs of current and future food product portfolios, effectively mitigating risks associated with B. cereus and other relevant pathogens.
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