Editorial: Machine Learning and Predictive Microbiology: Enhancing Food Safety Models

Raul Avila-Sosa¹Guadalupe Virginia Nevárez-Moorillón^2*

• ¹Benemerita Universidad Autonoma de Puebla, Puebla, Mexico
• ²Autonomous University of Chihuahua, Chihuahua, Mexico

Machine Learning and Predictive Microbiology: Enhancing Food Safety Models 14The goal of food safety is to guarantee that the food provided to consumers does not represent a 15 health risk due to the presence of chemical, physical, or biological hazards. In particular, microbial 16 growth of spoilage or pathogenic microorganisms can reduce the shelf life or pose a health hazard. 17The composition, physical, and chemical properties of a food product can either promote or inhibit 18 microbial growth; also, food processing methods can favor or restrain microbial proliferation. The 19 response of microorganisms to food composition, processing, or storage conditions will determine 20 their growth capacity. Traditional microbial growth models, often used in laboratory settings, do not 21 always translate well to real-world food environments due to the unique conditions present in food 22 systems. Predictive microbiology has emerged as a valuable tool in this context, enabling researchers 23 to predict the behavior of pathogenic and spoilage microorganisms in food systems based on growth 24 results obtained under controlled conditions (Kumar et al. 2024). 25The food industry constantly modifies processing conditions, develops or enhances more efficient 26 preservation methods, and creates new food products. In all these scenarios, the use of predictive 27 microbiology can help select the best conditions to increase shelf life and reduce the risk of pathogen 28 contamination. Even when predictive models have been utilized in the food industry as a complement 29 to quality control programs, such as HACCP (Hazard Analysis and Critical Control Points), 30 numerous challenges remain in this research area. The development of new predictive primary 31 models is an ongoing task, as new variables can be incorporated and new statistical and mathematical 32 methods become available. Typically, predictive models examine the growth of an organism under 33 controlled conditions; however, the impact of interactions with other organisms present in the food 34 environment is often overlooked. Additionally, the initial physiological state of the microorganisms needs to be taken into consideration (Koseki et al., 2021). The incorporation of new information 36 provided by the "omics" sciences, particularly functional genomics, can also improve the prediction 37 of microbial growth in foods. Tools such as data mining or machine learning can utilize up-to-date 38 information on microbial growth in food systems to generate more comprehensive predictive models 39 (Taiwo et al. 2024). 40In the present Research Topic, the combination of preservation methods is reported as efficient in 41 increasing the shelf life of food products. In Chen et al (2024), the antimicrobial effect of lemon 42 essential oil alone and in nanoemulsion was tested against common foodborne pathogens 43 (Escherichia coli, Staphylococcus aureus, Listeria monocytogenes). The effect was also tested on the 44 shelf life of fresh-cut kiwifruits, showing that the increase in shelf life was related to the 45 antimicrobial and antioxidant effects of the nanoemulsion. In another study, the effect of lactic acid 46 bacteria (LAB) inoculants on the quality of oat silage was investigated, seeking to reduce the 47 concentration of biogenic amines and thereby improve product safety (Huang and Jia, 2025 was used to construct the secondary and tertiary models, which were then validated with 60 experimental data. The author emphasizes the importance of developing tertiary models that can be 61 used in the food industry. 62The incorporation of novel strategies to manage data, such as data mining, neural networks, and 63 machine learning, presents an opportunity to enhance the goals of predictive microbiology. A better 64 understanding of the physiological stages of microorganisms in food systems, along with the changes 65 in growth conditions at different steps in the food management chain, can also provide a more 66 accurate view of the effects of processing on food preservation and safety. Several questions remain 67 to be answered in this topic. 68