With advances in artificial intelligence (AI) technologies, the development and implementation of digital food systems are becoming increasingly possible. There is tremendous interest in using different AI applications, such as machine learning models, natural language processing, and computer vision to improve food safety. Possible AI applications are broad and include, but are not limited to, ( a) food safety risk prediction and monitoring as well as food safety optimization throughout the supply chain, ( b) improved public health systems (e.g., by providing early warning of outbreaks and source attribution), and ( c) detection, identification, and characterization of foodborne pathogens. However, AI technologies in food safety lag behind in commercial development because of obstacles such as limited data sharing and limited collaborative research and development efforts. Future actions should be directed toward applying data privacy protection methods, improving data standardization, and developing a collaborative ecosystem to drive innovations in AI applications to food safety. Expected final online publication date for the Annual Review of Food Science and Technology, Volume 14 is March 2023. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.

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Late blowing defect (LBD) is an important spoilage issue in semi-hard cheese, with the outgrowth of Clostridium tyrobutyricum spores during cheese aging considered to be the primary cause. Although previous studies have explored the microbial and physicochemical factors influencing the defect, a risk assessment tool that allows for improved and rational management of LBD is lacking. The purpose of this study was to develop a predictive model to estimate the probability of LBD in Gouda cheese and evaluate different intervention strategies. The spore concentration distribution of butyric acid bacteria (BAB) in bulk tank milk was obtained from 8 dairy farms over 12 mo. The concentration of C. tyrobutyricum from raw milk to the end of aging was simulated based on Gouda brined for 2 d in saturated brine at 8°C and aged at 13°C. Predicted C. tyrobutyricum concentrations during aging and estimated concentration thresholds in cheese at onset of LBD were used to predict product loss due to LBD during a simulated 1-yr production. With the estimated concentration thresholds in cheese ranging from 4.36 to 4.46 log most probable number (MPN)/ kg of cheese, the model predicted that 9.2% (±1.7%) of Gouda cheese showed LBD by d 60; cheeses predicted to show LBD at d 60 showed a mean pH of 5.39 and were produced with raw milk with a mean BAB spore count of 143 MPN/L. By d 90, 36.1% (±3.4%) of cheeses were predicted to show LBD, indicating that LBD typically manifests between d 60 and 90, which is consistent with observations from the literature and the cheese industry. Sensitivity analysis indicated that C. tyrobutyricum maximum growth rate as well as concentration threshold in cheese at onset of LBD are the most important variables, identifying key data needs for development of more accurate models. The implementation of microfiltration or bactofugation of raw milk (assumed to show 98% efficiency of spore removal) in our model prevented occurrence of LBD during the first 60 d of aging. Overall, our findings provide a framework for predicting the occurrence of LBD in Gouda as well as other cheeses and illustrate the value of developing digital tools for managing dairy product quality.

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Graphical Abstract Summary: The levels of anaerobic butyric acid-producing sporeforming bacteria (BAB) in bulk tank raw milk from 7 similarly managed conventional dairy farms were studied. It is important to control and minimize BAB spores in raw milk to prevent late blowing defect in certain styles of cheese during aging. Different farm management practices such as bedding type used, cleaning agents, and udder clipping frequency may affect BAB spore concentrations in raw milk. We employed statistical models to test the relationship between farm-level factors and BAB spore concentrations. No significant association between management practices surveyed (i.e., bedding management, milking preparation procedures, teat and udder cleanliness scoring, holding area cleaning procedures, and udder clipping or flaming frequency) and BAB spore concentration was found, with one possible reason being the small sample size used. The minimum number of individual samples required was calculated using parameters from this study, demonstrating how this research can be used for future study design.