Predicting mycotoxins in foods: a review

Area of Science:

• Food science
• Microbiology
• Risk assessment

Background:

• Mathematical models are essential for predicting microbial behavior in food products.
• Predictive microbiology has historically focused on bacteria, with limited tools for fungi.
• There is a growing need for predictive models for fungi due to their impact on food safety.

Purpose of the Study:

• To provide the first comprehensive review of predictive mycology in food safety.
• To analyze existing kinetic and probability models for mycotoxigenic fungi.
• To cover fungal germination, growth, and mycotoxin production.

Main Methods:

• Literature review of predictive models for fungi in food.
• Analysis of kinetic and probability models.
• Inclusion of mycotoxin production in the scope.

Main Results:

• A significant increase in studies on predictive modeling for fungi is observed.
• Existing models address fungal germination, growth, and mycotoxin production.
• This review consolidates current knowledge on predictive mycology for food safety.

Conclusions:

• Predictive mycology is an emerging field crucial for enhancing food safety.
• The reviewed models offer valuable tools for assessing risks associated with mycotoxigenic fungi.
• Further development and application of these models are needed to ensure global food security.