Multiple-Strain Approach and Probabilistic Modeling of Consumer Habits in Quantitative Microbial Risk Assessment: A Quantitative Assessment of Exposure to Staphylococcal Enterotoxin A in Raw Milk

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.

Probabilistic modelling of Escherichia coli concentration in raw milk under hot weather conditions

Climate change is one of the threats to the dairy supply chain as it may affect the microbiological quality of raw milk. In this context, a probabilistic model was developed to quantify the concentration of Escherichia coli in raw milk and explore what may happen to France under climate change conditions. It included four modules: initial contamination, packaging, retailing, and consumer refrigeration. The model was built in R using the 2nd order Monte Carlo mc2d package to propagate the uncertainty and analysed its impact independently of the variability. The initial microbial counts were obtained from a dairy farm located in Saudi Arabia to reflect the impact of hot weather conditions. This country was taken as representative of what might happen in Europe and therefore in France in the future due to climate change. A large dataset containing 622 data points was analysed. They were fitted by a Normal probability distribution using the fitdistrplus package. The microbial growth was determined across various scenarios of time and temperature storage reflecting the raw milk supply-chain in France. Existing growth rate data from literature and ComBase were analysed by the Ratkowsky secondary model. Results were interpreted using the nlstools package. The mean E. coli initial concentration in raw milk was estimated to be 1.31 [1.27; 1.35] log CFU/ mL and was found to increase at the end of the supply chain as a function of various time and temperature conditions. The estimations varied from 1.73 [1.42; 2.28] log CFU/mL after 12 h, 2.11 [1.46; 3.22] log CFU/mL after 36 h, and 2.41 [1.69;3.86] log CFU/mL after 60 h of consumer storage. The number of milk packages exceeding the 2-log French hygiene criterion for E. coli increased from 10% [8;12%] to 53% [27;77%] during consumer storage. In addition, the most significant factors contributing to the uncertainty of the model outputs were identified by running a sensitivity analysis. The results showed that the uncertainty around the Ratkowsky model parameters contributed the most to the uncertainty of E. coli concentration estimates. Overall, the model and its outputs provide an insight on the possible microbial raw milk quality in the future in France due to higher temperatures conditions driven by climate change.

Paediatric HUS Cases Related to the Consumption of Raw Milk Sold by Vending Machines in Italy: Quantitative Risk Assessment Based on Escherichia coli O157 Official Controls over 7 years

A quantitative risk assessment (RA) was developed to estimate haemolytic-uremic syndrome (HUS) cases in paediatric population associated with the consumption of raw milk sold in vending machines in Italy. The historical national evolution of raw milk consumption phenomenon since 2008, when consumer interest started to grow, and after 7 years of marketing adjustment, is outlined. Exposure assessment was based on the official Shiga toxin-producing Escherichia coli O157:H7 (STEC) microbiological records of raw milk samples from vending machines monitored by the regional Veterinary Authorities from 2008 to 2014, microbial growth during storage, consumption frequency of raw milk, serving size, consumption preference and age of consumers. The differential risk considered milk handled under regulation conditions (4°C throughout all phases) and the worst time-temperature field handling conditions detected. In case of boiling milk before consumption, we assumed that the risk of HUS is fixed at zero. The model estimates clearly show that the public health significance of HUS cases due to raw milk STEC contamination depends on the current variability surrounding the risk profile of the food and the consumer behaviour has more impact than milk storage scenario. The estimated HUS cases predicted by our model are roughly in line with the effective STEC O157-associated HUS cases notified in Italy only when the proportion of consumers not boiling milk before consumption is assumed to be 1%. Raw milk consumption remains a source of E. coli O157:H7 for humans, but its overall relevance is likely to have subsided and significant caution should be exerted for temporal, geographical and consumers behaviour analysis. Health education programmes and regulatory actions are required to educate people, primarily children, on other STEC sources.