Overview of the Potential Impacts of Climate Change on the Microbial Safety of the Dairy Industry

Megatrends and emerging issues: Impacts on food safety

The world is changing at a pace, driven by global megatrends and their interactions. Megatrends, including climate change, the drive for sustainability, an aging population, urbanization, and geopolitical tensions, are producing an increasingly challenging environment for the provision of a safe and secure food supply. To ensure a robust, safe, and secure food supply for all, potential food safety impacts associated with these megatrends need to be understood, and mitigation and management plans must be implemented. This paper outlines the relevant megatrends, discusses their potential impact on food safety, and suggests steps to help ensure the production of safe food in the future. Megatrends are increasingly driving resource depletion, reducing the vitality of plants and animals, increasing the geographical spread of animal and plant pathogens, increasing the risk of mycotoxins, agrichemical residues, and antimicrobial-resistant pathogens contaminating foods, and threatening to destabilize food systems and the food regulatory network. Science-based actions, adopting continual and dynamic risk assessments, alongside the use of more sensitive and accurate methods for the detection of contaminants, may counter these challenges. The use of artificial intelligence, robotics and automation, the enhancement of food safety cultures, the continued education and training of workforces, and the implementation of risk-based food regulations will help ensure preventative controls are in place. As low-income countries and smallholder farmers are more likely to be exposed to the impact of these megatrends and less likely to have resources to counter them, geographical social inequality, unrest, and population migration are likely to be exacerbated unless urgent action is taken.

Human-model interaction-based decision support system for optimizing food safety assessment

Real-world decision support systems (DSS) operate in a continuous cycle of data collection, annotation, and model optimization, heavily relying on high-quality data. However, acquiring such data, particularly in specialized fields, is often expensive and resource-intensive, presenting significant challenges. To mitigate these challenges, recent machine learning research has increasingly focused on integrating experimental data and expert knowledge into user-friendly tools. In this paper, we present a novel framework named the Model-Human-interaction Risk Assessment (MHRA), which leverages human interaction and collaborative scenario construction to achieve better performance. We address the increasing demand for a 'Human in the loop (HITL)' approach, which ensures the updateability of expert system knowledge bases during the input, selection, calculation, and ranking phases. Furthermore, we highlight the contributions of a human interactive simulation model in developing enhanced systems to assist decision-makers in maximizing the accuracy and standardization of evaluation models while minimizing food safety risks. We demonstrate the practical application of our framework through an infant food assessment case study and discuss the model's strengths and limitations.

Dynamical modeling of Salmonellosis in humans and dairy cattle with temperature and pH effects

Approximately 20 million cases and 0.15 million human fatalities worldwide each year are caused by Salmonellosis. A mechanistic compartmental model based on ordinary differential equations is proposed to evaluate the effects of temperature and pH on the transmission dynamics of Salmonellosis. The transmission potential of the disease in areas with temperature and pH stresses is examined. The next-generation matrix method is applied to compute the temperature-pH-dependent reproduction number ℛPT. The dynamical regimes of the system are examined using Lyapunov stability theory and backward bifurcation analysis. The uncertainty and global sensitivity analysis are examined using the Latin Hypercube Sampling (LHS) and Partial Rank Correlation Coefficient (PRCC) methods. The numerical simulations of the proposed model under favorable and unfavorable temperatures are performed with a 95% confidence interval (CI) for the reliability assessment of the model parameters. The analysis shows that the ingestion rates of Salmonella enterica subsp. enterica serovar Typhimurium bacteria in humans and dairy cattle, human-to-human transmission rate, cattle-to-cattle transmission rate, human shedding rate, dairy cattle shedding rate, and the rate of producing contaminated dairy products are directly proportional to the number of infected humans and infected dairy cattle. The temperature ranges of 100C-200C and 300C-400C and pHs greater than 3.8 have a significant effect on the dynamics of Salmonellosis. In order to eliminate Salmonellosis, the study recommends treating natural water bodies using the recommended chemical disinfectants during summer seasons and in areas with temperature ranges of 100C-200C, cooking food at the hottest temperatures, and storing food at the lowest temperatures for all pHs.

Into the Jungle of Biological Agents of Foodborne Diseases: Time to Put Some Order for the French Risk Manager

Consumers can be exposed to many foodborne biological hazards that cause diseases with varying outcomes and incidence and, therefore, represent different levels of public health burden. To help the French risk managers to rank these hazards and to prioritize food safety actions, we have developed a three-step approach. The first step was to develop a list of foodborne hazards of health concern in mainland France. From an initial list of 335 human pathogenic biological agents, the final list of "retained hazards" consists of 24 hazards, including 12 bacteria (including bacterial toxins and metabolites), 3 viruses and 9 parasites. The second step was to collect data to estimate the disease burden (incidence, Disability Adjusted Life Years) associated with these hazards through food during two time periods: 2008-2013 and 2014-2019. The ranks of the different hazards changed slightly according to the considered period. The third step was the ranking of hazards according to a multicriteria decision support model using the ELECTRE III method. Three ranking criteria were used, where two reflect the severity of the effects (Years of life lost and Years lost due to disability) and one reflects the likelihood (incidence) of the disease. The multicriteria decision analysis approach takes into account the preferences of the risk managers through different sets of weights and the uncertainties associated with the data. The method and the data collected allowed to estimate the health burden of foodborne biological hazards in mainland France and to define a prioritization list for the health authorities.

Distribution of Bovine Mastitis Pathogens in Quarter Milk Samples from Bavaria, Southern Germany, between 2014 and 2023-A Retrospective Study

The objective of this study was to investigate the distribution of mastitis pathogens in quarter milk samples (QMSs) submitted to the laboratory of the Bavarian Animal Health Service (TGD) between 2014 and 2023 in general, in relation to the clinical status of the quarters, and to analyze seasonal differences in the detection risk. Each QMS sent to the TGD during this period was analyzed and tested using the California Mastitis Test (CMT). Depending on the result, QMSs were classified as CMT-negative, subclinical, or clinical if the milk character showed abnormalities. Mastitis pathogens were detected in 19% of the QMSs. Non-aureus staphylococci (NAS) were the most common species isolated from the culture positive samples (30%), followed by Staphylococcus (S.) aureus (19%), Streptococcus (Sc.) uberis (19%), and Sc. dysgalactiae (9%). In culture-positive QMSs from CMT-negative and subclinically affected quarters, the most frequently isolated pathogens were NAS (44% and 27%, respectively), followed by S. aureus (25% and 17%, respectively) and Sc. uberis (8% and 22%, respectively). In QMSs from clinically affected quarters, the most frequently isolated pathogens were Sc. uberis (32%), S. aureus (13%), Sc. dysgalactiae (11%), and Escherichia (E.) coli (11%). The distribution of NAS and Sc. uberis increased throughout the study period, while that of S. aureus decreased. From June to October, QMSs from subclinically affected quarters increased and environmental pathogens, such as Sc. uberis, were detected more frequently. In conclusion, this study highlights the dynamic nature of the distribution of mastitis pathogens, influenced by mastitis status and seasonal factors. Environmental pathogens still play an important role, especially in clinical mastitis and seasonal dependency, with the number of positive samples continuing to increase. It is therefore essential to continue mastitis control measures and to regularly monitor the spread of mastitis pathogens in order to track trends and adapt targeted prevention measures.

A metagenomic survey of bacterial communities from kurut: The fermented cow milk in Kyrgyzstan

Kurut is a traditional dry dairy product mostly consumed in Central Asia. In this study, the distribution of the dominant bacteria present in kurut samples (n=84) originated from seven (Chuy, Issyk-Kul, Talas, Naryn, Jalal-Abad, Osh, and Batken) regions in Kyrgyzstan were analyzed with Illumina iSeq100 platform. The dominant phylum detected was Firmicutes followed by Proteobacteria, Actinobacteria, Cyanobacteria/Chloroplast, and Tenericutes. The most abundant family detected was Lactobacillaceae followed by Streptococcaceae, Enterococcaceae, Chloroplast, and Leuconostocaceae. At the genus level, Lactobacillus was the predominant one in samples and Streptococcus, Enterococcus, Lactococcus, and Streptophyta followed this. Further comprehensive characterization analyses in kurut samples may have potential applications both in industrial starter culture developments and also future therapeutic approaches based on potential strains with probiotic properties.

Quantitative microbial spoilage risk assessment caused by fungi in sports drinks through multilevel modelling

The risk of fungal spoilage of sports drinks produced in the beverage industry was assessed using quantitative microbial spoilage risk assessment (QMSRA). The most relevant pathway was the contamination of the bottles during packaging by mould spores in the air. Mould spores' concentration was estimated by longitudinal sampling for 6 years (936 samples) in different production areas and seasons. This data was analysed using a multilevel model that separates the natural variability in spore concentration (as a function of sampling year, season, and area) and the uncertainty of the sampling method. Then, the expected fungal contamination per bottle was estimated by Monte Carlo simulation, considering their settling velocity and the time and exposure area. The product's shelf life was estimated through the inoculation of bottles with mould spores, following the determination of the probability of visual spoilage as a function of storage time at 20 and 30 °C using logistic regression. The Monte Carlo model estimated low expected spore contamination in the product (1.7 × 10-6 CFU/bottle). Nonetheless, the risk of spoilage is still relevant due to the large production volume and because, as observed experimentally, even a single spore has a high spoilage potential. The applicability of the QMSRA during daily production was made possible through the simplification of the model under the hypothesis that no bottle will be contaminated by more than one spore. This simplification allows the calculation of a two-dimensional performance objective that combines the spore concentration in the air and the exposure time, defining "acceptable combinations" according to an acceptable level of spoilage (ALOS; the proportion of spoiled bottles). The implementation of the model at the operational level was done through the representation of the simplified model as a two-dimensional diagram that defines acceptable and unacceptable areas. The innovative methodology employed here for defining and simplifying QMSRA models can be a blueprint for future studies aiming to quantify the risk of spoilage of other beverages with a similar scope.

Quantitative Associations between Season, Month, and Temperature-Humidity Index with Milk Yield, Composition, Somatic Cell Counts, and Microbial Load: A Comprehensive Study across Ten Dairy Farms over an Annual Cycle

This current study addresses the knowledge gap regarding the influence of seasons, months, and THI on milk yield, composition, somatic cell counts (SCC), and total bacterial counts (TBC) of dairy farms in northeastern regions of Iran. For this purpose, ten dairy herds were randomly chosen, and daily milk production records were obtained. Milk samples were systematically collected from individual herds upon delivery to the dairy processing facility for subsequent analysis, including fat, protein, solids-not-fat (SNF), pH, SCC, and TBC. The effects of seasons, months, and THI on milk yield, composition, SCC, and TBC were assessed using an analysis of variance. To account for these effects, a mixed-effects model was utilized with a restricted maximum likelihood approach, treating month and THI as fixed factors. Our investigation revealed noteworthy correlations between key milk parameters and seasonal, monthly, and THI variations. Winter showed the highest milk yield, fat, protein, SNF, and pH (p < 0.01), whereas both SCC and TBC reached their lowest values in winter (p < 0.01). The highest values for milk yield, fat, and pH were recorded in January (p < 0.01), while the highest protein and SNF levels were observed in March (p < 0.01). December marked the lowest SCC and TBC values (p < 0.01). Across the THI spectrum, spanning from -3.6 to 37.7, distinct trends were evident. Quadratic regression models accounted for 34.59%, 21.33%, 4.78%, 20.22%, 1.34%, 15.42%, and 13.16% of the variance in milk yield, fat, protein, SNF, pH, SCC, and TBC, respectively. In conclusion, our findings underscore the significant impact of THI on milk production, composition, SCC, and TBC, offering valuable insights for dairy management strategies. In the face of persistent challenges posed by climate change, these results provide crucial guidance for enhancing production efficiency and upholding milk quality standards.

Effects of Environmental Temperature and Humidity on Milk Composition, Microbial Load, and Somatic Cells in Milk of Holstein Dairy Cows in the Northeast Regions of Iran

The present study aims to examine the relationships between temperature and humidity and milk composition, microbial load, and somatic cells in the milk of Holstein dairy cows. For this purpose, the temperature−humidity index, ambient temperature, and relative humidity data were obtained from the nearest weather stations. Production data were obtained from four dairy farms in Golestan province, Iran, collected from 2016 to 2021. The traits investigated were protein, fat, solids-not-fat (SNF), microbial load, and somatic cell count (SCC) in milk. The effects of the environmental temperature, humidity, month, and season on the milk composition, microbial load, and somatic cells were analyzed through analysis of variance. The effects of environmental temperature, humidity, month, and season on the milk composition, microbial load, and somatic cell composition were analyzed using a mixed procedure with a restricted maximum likelihood model. Although our findings revealed that there were significant differences in fat, protein, SNF, and SCC among the different months of the year (p < 0.01), no significant difference was observed in the total microbial count in milk. Environmental temperature presented significant impacts on fat, protein, SNF, SCC, and total microbial count within various temperature ranges (p < 0.01). When the temperature increased from 6.2 °C to 31.3 °C, the milk protein, fat, SNF, and somatic cell count significantly decreased, by approximately 4.09%, 5.75%, 1.31%, and 16.8%, respectively; meanwhile, the microbial count in milk significantly increased, by approximately 13.7%. Humidity showed an influence on fat, protein, non-fat solids, somatic cells, and total microbial count within different temperature ranges (p < 0.01). When the humidity increased from 54% to 82%, the milk protein, fat, SNF, and SCC significantly increased, by approximately 3.61%, 4.84%, 1.06%, and 10.2%, respectively; meanwhile, the microbial count in milk significantly decreased, by approximately 16.3%. The results demonstrate that there is a negative correlation between different months of the year, temperature, and the humidity of the environment, in terms of milk components and SCC. Our findings demonstrate that the optimum performance, in terms of milk composition, occurred in the first quarter of the year. As temperature increases and humidity decreases, milk quality decreases. Therefore, the adverse effects of environmental conditions on agricultural profits are not negligible, and strategies to better deal with the negative environmental effects are needed in order to improve milk quality in dairy cows.

Advances in multi-omics based quantitative microbial risk assessment in the dairy sector: A semi-systematic review

With the increasing consumption of packaged and ready-to-eat food products, the risk of foodborne illness has drastically increased and so has the dire need for proper management. The conventional Microbial Risk Assessment (MRA) investigations require prior knowledge of process flow, exposure, and hazard assessment throughout the supply chain. These data are often generated using conventional microbiological approaches based either on shelf-life studies or specific spoilage organisms (SSOs), frequently overlooking crucial information such as antimicrobial resistance (AMR), biofilm formation, virulence factors and other physiological variations coupled with bio-chemical characteristics of food matrix. Additionally, the microbial risks in food are diverse and heterogenous, that might be an outcome of growth and activity of multiple microbial populations rather than a single species contamination. The uncertainty on the microbial source, time as well as point of entry into the food supply chain poses a constraint to the efficiency of preventive approaches and conventional MRA. In the last few decades, significant breakthroughs in molecular methods and continuously progressing bioinformatics tools have opened up a new horizon for risk analysis-based approaches in food safety. Real time polymerase chain reaction (qPCR) and kit-based assays provide better accuracy and precision with shorter processing time. Despite these improvements, the effect of complex food matrix on growth environment and recovery of pathogen is a persistent problem for risk assessors. The dairy industry is highly impacted by spoilage and pathogenic microorganisms. Therefore, this review discusses the evolution and recent advances in MRAmethodologies equipped with predictive interventions and "multi-omics" approach for robust MRA specifically targeting dairy products. It also highlights the limiting gap area and the opportunity for improvement in this field to ensure precision food safety.