Proteolytic Clostridium botulinum (group I) – evaluation of predictive models for time-to-toxicity and identification of product characteristics that prevent growth and toxin formation in different types of foods

Preventing toxin formation by proteolytic Clostridium botulinum is critical for food safety but remains a challenge for example when developing or reformulating various products including sodium-reduced or nitrite-free foods. Validated predictive food microbiology models can support this process by identifying combinations of product characteristics and storage conditions that prevent growth and toxin formation. An existing cardinal parameter growth and growth boundary model ( Koukou et al., 2022b ), including the effect of 11 environmental factors and their interactions, was extended to predict time-to-toxicity (TTT) for proteolytic C. botulinum . Data from 962 published TTT experiments were compiled to evaluate performance for different versions of this model. Product characteristics, storage conditions and TTT were collected for meat, poultry, vegetables, seafood, dough, processed cheese, other cheeses and uncategorized foods. These data, with 648 TTT positive and 314 TTT negative, covered a wide range of environmental factors and included responses for 138 proteolytic C. botulinum strains. Model performance was evaluated using percentages of fail-dangerous, fail-safe and correct predictions along with bias and accuracy factors for predicted and observed TTT. Model performance was markedly different for non-dairy and dairy foods. For non-dairy foods and within a wide range of applicability, including 494 TTT-responses, the new and extensive Koukou-TTT-model had 0.2% fail-dangerous TTT predictions and bias/accuracy factors of 0.53/2.5. For dairy products ( n  = 312), including processed and other cheeses, the Koukou-TTT-model provided 26.0% fail-dangerous TTT predictions. This unacceptable model performance was primarily due to calcium complexation with citric and lactic acids which reduced the inhibiting effect of these organic acids in the studied dairy foods. The Koukou-TTT-model was modified to predict the effect of total, rather than undissociated, concentrations of citric and lactic acids in dairy foods. This modified model predicted 144 TTT-responses with 0.0% fail-dangerous TTT predictions and bias/accuracy factors of 0.37/4.42. The original and the modified growth and growth boundary Koukou-models can be used, within defined and wide ranges of applicability, to determine combinations of product characteristics and storage conditions where growth and TTT for proteolytic C. botulinum are prevented in meat, poultry, vegetables, seafood, dough and dairy products.