One of the food industry's most pressing challenges is providing safe and nutritious food for all. Microbial growth in the food supply or processed food products can reduce shelf life. On the other hand, foods' composition and physicochemical characteristics can allow the growth and distribution of foodborne pathogens. The response of microorganisms to food composition, processing, or storage conditions will determine their growth capacity. The development of microbial growth models in food product environments differs from traditional growth models used in fermentations or lab cultures due to restrictions or advantages provided by the food environment. In predictive microbiology, the growth of pathogenic or food-spoilage microorganisms is determined under controlled conditions and used to predict their behavior in food systems.
The food industry constantly modifies processing conditions to increase the shelf life of the products, decrease the risks of contamination, and preserve the nutrients present in the raw food materials. Predictive microbiology is a useful tool to analyze the microbiological risks associated with modifications in process conditions, food formulation, or storage conditions. The effect of food composition on the efficiency of added antimicrobials is particularly important in the formulation of new products. Tools such as data mining or machine learning can use the information compiled up to date on microbial growth in food systems to generate more comprehensive predictive models. The challenge is to generate models that can be applied to the several stages in the food production chain. The ultimate goal is to propose sustainable processes for safe foods with expanded shelf life.
High-quality original research articles, reviews, mini-reviews, and prospective opinions are welcome on the following aspects:
• 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 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 the 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.
One of the food industry's most pressing challenges is providing safe and nutritious food for all. Microbial growth in the food supply or processed food products can reduce shelf life. On the other hand, foods' composition and physicochemical characteristics can allow the growth and distribution of foodborne pathogens. The response of microorganisms to food composition, processing, or storage conditions will determine their growth capacity. The development of microbial growth models in food product environments differs from traditional growth models used in fermentations or lab cultures due to restrictions or advantages provided by the food environment. In predictive microbiology, the growth of pathogenic or food-spoilage microorganisms is determined under controlled conditions and used to predict their behavior in food systems.
The food industry constantly modifies processing conditions to increase the shelf life of the products, decrease the risks of contamination, and preserve the nutrients present in the raw food materials. Predictive microbiology is a useful tool to analyze the microbiological risks associated with modifications in process conditions, food formulation, or storage conditions. The effect of food composition on the efficiency of added antimicrobials is particularly important in the formulation of new products. Tools such as data mining or machine learning can use the information compiled up to date on microbial growth in food systems to generate more comprehensive predictive models. The challenge is to generate models that can be applied to the several stages in the food production chain. The ultimate goal is to propose sustainable processes for safe foods with expanded shelf life.
High-quality original research articles, reviews, mini-reviews, and prospective opinions are welcome on the following aspects:
• 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 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 the 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.