Machine Learning and Predictive Microbiology: Enhancing Food Safety Models

The field of food safety is critical in ensuring that the food supply remains safe and nutritious from production to consumption. One of the most pressing challenges in this area is controlling microbial growth, which can significantly reduce the shelf life of food products and pose health risks. The composition and physicochemical characteristics of food can either inhibit or promote the growth of foodborne pathogens. Traditional microbial growth models, often used in laboratory settings, do not always translate well to real-world food environments due to the unique conditions present in food systems. Predictive microbiology has emerged as a valuable tool in this context, allowing researchers to predict the behavior of pathogenic and spoilage microorganisms under various controlled conditions. Despite advancements, there remain significant gaps in our understanding of how to effectively apply these models across different stages of the food processing chain. The need for more comprehensive and adaptable models is evident, particularly as the food industry continues to evolve its processing techniques to enhance food safety and shelf life.
This research topic aims to explore the development and application of predictive models in food safety throughout the processing chain. The primary objectives include understanding how new processing conditions impact microbial safety, examining the interactions between food ingredients and antimicrobials, and developing robust models that can predict microbial behavior in diverse food environments. Specific questions to be addressed include: How do changes in food composition affect microbial growth? What are the best practices for integrating machine learning into predictive microbiology? How can we construct and validate models that are applicable across various stages of food production?
To gather further insights into the boundaries of predictive models in food safety, we welcome articles addressing, but not limited to, the following themes:
- Impact of new food processing conditions on the microbial safety of the final product
- Interaction of added antimicrobials and food ingredients on food safety
- Use of growth/no growth models for the growth of pathogens
- Impact of food composition modifications on the growth of pathogens or concentration of toxins
- Development of empirical or theoretical models for assessing microbial growth under food system conditions
- Machine learning applications in predictive microbiology
- Construction and validation of tertiary predictive models

Keywords: Predictive Microbiology, Food Safety, Shelf Life, Food Processing Modifications, Microbial Growth Models, Machine Learning

Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.