A quantitative risk assessment model for salmonellosis due to milk chocolate consumption in Brazil

Salmonella prevalence in raw cocoa beans and a microbiological risk assessment to evaluate the impact of cocoa liquor processing on the reduction of Salmonella

Salmonella in raw cocoa beans (n= 870) from main sourcing areas over nine months was analyzed. It was detected in 71 (ca. 8.2%) samples, with a contamination level of 0.3-46 MPN/g except for one sample (4.1×104 CFU/g). Using prevalence and concentration data as input, the impact of thermal treatment in cocoa processing on the risk estimate of acquiring salmonellosis by a random Belgian chocolate consumer was calculated by a quantitative microbiological risk assessment (QMRA) approach. A modular process risk model from raw cocoa beans to cocoa liquor up to a hypothetical final product (70-90% dark chocolate tablet), was set up to understand changes of Salmonella concentrations following the production process. Different thermal treatments during bean or nib steam, nib roasting or liquor sterilization (achieving a 0-6 log reduction of Salmonella) were simulated. Based on the generic FAO/WHO Salmonella dose-response model and the chocolate consumption data in Belgium, salmonellosis risk per serving and cases per year at population level were estimated. When a 5 log reduction of Salmonella was achieved, the estimated mean risk per serving was 3.35×10-8 (95% CI: 3.27×10-10-1.59×10-7), and estimated salmonellosis cases per year (11.7 million population) was 88 (95% CI: <1-418). The estimated mean risk per serving was 3.35×10-9 (95% CI: 3.27×10-11-1.59×10-8), and the estimated salmonellosis cases per year was 9 (95% CI: <1-42), for a 6 log reduction. The current QMRA model solely considered Salmonella reduction in a single-step thermal treatment in the cocoa process. Inactivation obtained during other process steps (e.g. grinding) might occur but was not considered. As the purpose was to use QMRA as a tool to evaluate the log reduction in the cocoa processing, no post-contamination from the processing environment and ingredients was included. A minimum of 5 log reduction of Salmonella in the single-step thermal treatment of cocoa process, was considered to be adequate.

Food safety and food security through predictive microbiology tools: a short review

This article discusses the issues of food safety and food security as a matter of global health. Foodborne illness and deaths caused by pathogens in food continue to be a worldwide problem, with a reported 600 million cases per year, leading to around 420,000 deaths in 2010. Predictive microbiology can play a crucial role in ensuring safe food through mathematical modelling to estimate microbial growth and behaviour. Food security is described as the social and economical means of accessing safe and nutritious food that meets people's dietary preferences and requirements for an active and healthy life. The article also examines various factors that influence food security, including economic, environmental, technological, and geopolitical challenges globally. The concept of food safety is described as a science-based process or action that prevents food from containing substances that could harm human health. Food safety receives limited attention from policymakers and consumers in low- and middle-income countries, where food safety issues are most prevalent. The article also highlights the importance of detecting contaminants and pathogens in food to prevent foodborne illnesses and reduce food waste. Food and Agriculture Organization (FAO), an institution belonging to World Health Organization (WHO) presented calls to action to solve some of the emerging problems in food safety, as it should be a concern of all people to be involved in the pursue of safer food. The guarantee of safe food pertaining to microbiological contamination, as there are different types of active microorganisms in foods, could be obtained using predictive microbiology tools, which study and analyse different microorganisms' behaviour through mathematical models. Studies published by several authors show the application of primary, secondary, or tertiary models of predictive microbiology used for different food products.

Outbreak of monophasic Salmonella Typhimurium Sequence Type 34 linked to chocolate products

As of 3rd June 2022, 445 cases of monophasic Salmonella Typhimurium sequence type 34 infection had been reported globally. The outbreak was caused by two novel strains of monophasic S. Typhimurium with unusual multi-drug resistance. The majority of these cases involved children aged 10 or younger, and they had a hospitalization rate higher than most previous outbreaks of monophasic S. Typhimurium, but no fatalities were recorded. The infection was traced to certain Belgian chocolate products after extensive microbiological and epidemiological research. Public health officials took immediate action to recall all the contaminated products, and the risk of exposure was reduced. The common symptoms are bloody diarrhea, acute onset of fever, abdominal pain, and vomiting. This article aims to thoroughly review the recent outbreak of monophasic Salmonella Typhimurium ST-34, including its epidemiology and comparison with ongoing outbreaks. We also highlighted past chocolate-related salmonella outbreaks and current control and prevention guidelines and recommendations.

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The present salmonellosis outbreak in Europe was traced to certain chocolate products produced at a plant in Belgium.

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The majority of these cases involved children aged 10 or younger.

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This outbreak was caused by two novel strains of monophasic S. Typhimurium with unusual multi-drug resistance.

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Rare drug resistance to aminoglycosides, phenicols, and trimethoprim can be used for screening of probable cases.

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For improving food safety, active monitoring, surveillance, inspection, outbreak management, research, and education are needed.

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.