Risk-Based Approach for Microbiological Food Safety Management in the Dairy Industry: The Case of Listeria monocytogenes in Soft Cheese Made from Pasteurized Milk

A Critical Review of Risk Assessment Models for Listeria monocytogenes in Produce

A review of quantitative risk assessment (QRA) models of Listeria monocytogenes in produce was carried out, with the objective of appraising and contrasting the effectiveness of the control strategies placed along the food chains. Despite nine of the thirteen QRA models recovered being focused on fresh or RTE leafy greens, none of them represented important factors or sources of contamination in the primary production, such as the type of cultivation, water, fertilisers or irrigation method/practices. Cross-contamination at processing and during consumer's handling was modelled using transfer rates, which were shown to moderately drive the final risk of listeriosis, therefore highlighting the importance of accurately representing the transfer coefficient parameters. Many QRA models coincided in the fact that temperature fluctuations at retail or temperature abuse at home were key factors contributing to increasing the risk of listeriosis. In addition to a primary module that could help assess current on-farm practices and potential control measures, future QRA models for minimally processed produce should also contain a refined sanitisation module able to estimate the effectiveness of various sanitisers as a function of type, concentration and exposure time. Finally, L. monocytogenes growth in the products down the supply chain should be estimated by using realistic time-temperature trajectories, and validated microbial kinetic parameters, both of them currently available in the literature.

A Critical Review of Risk Assessment Models for Listeria monocytogenes in Dairy Products

A review of the published quantitative risk assessment (QRA) models of L. monocytogenes in dairy products was undertaken in order to identify and appraise the relative effectiveness of control measures and intervention strategies implemented at primary production, processing, retail, and consumer practices. A systematic literature search retrieved 18 QRA models, most of them (9) investigated raw and pasteurized milk cheeses, with the majority covering long supply chains (4 farm-to-table and 3 processing-to-table scopes). On-farm contamination sources, either from shedding animals or from the broad environment, have been demonstrated by different QRA models to impact the risk of listeriosis, in particular for raw milk cheeses. Through scenarios and sensitivity analysis, QRA models demonstrated the importance of the modeled growth rate and lag phase duration and showed that the risk contribution of consumers' practices is greater than in retail conditions. Storage temperature was proven to be more determinant of the final risk than storage time. Despite the pathogen's known ability to reside in damp spots or niches, re-contamination and/or cross-contamination were modeled in only two QRA studies. Future QRA models in dairy products should entail the full farm-to-table scope, should represent cross-contamination and the use of novel technologies, and should estimate L. monocytogenes growth more accurately by means of better-informed kinetic parameters and realistic time-temperature trajectories.

Food Supply Chain Safety Research Trends From 1997 to 2020: A Bibliometric Analysis

Background

The COVID-19 pandemic has exposed the fragility of the global food supply chain, strengthened consumers' awareness of the traceability system throughout the supply chain, and gradually changed consumers' consumption concepts and consumption patterns. Therefore, the aim of this study was to analyse the relevant literature on food safety in the food supply chain, examine its current status, hot spots, and development trends, and provide some suggestions for academics and relevant government departments in food supply chain safety research.

Methods

We collected the literature on the food safety research of the food supply chain from the Scopus database, used BibExcel to count the subject categories, published journals, geographical distributions, research institutions, authors, and keywords in the literature, and used Pajek software to analyse the keywords in the literature, perform co-occurrence analysis, draw related knowledge maps, and perform cluster analysis on primary keywords. Finally, to study the development trend, we used CorTexT software to illustrate the theme evolution path map in this research field.

Results

The keyword visualization network revealed the following key research topics: (1) food safety at the consumer end of the food supply chain, (2) food safety management in the food supply chain, (3) risk management of food safety in the food safety chain, and (4) food safety at the production end of the food supply chain.

Conclusions

After comprehensive discussion and analysis, we concluded that food supply chain management may be a hot topic in the future, especially in traceability management combined with the blockchain. It is necessary to explore in-depth how the blockchain can affect the food supply chain to provide a theoretical basis for managing the latter.

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

Climate change is expected to affect many different sectors across the food supply chain. The current review paper presents an overview of the effects of climate change on the microbial safety of the dairy supply chain and suggest potential mitigation strategies to limit the impact. Raw milk, the common raw material of dairy products, is vulnerable to climate change, influenced by changes in average temperature and amount of precipitation. This would induce changes in the microbial profile and heat stress in lactating cows, increasing susceptibility to microbial infection and higher levels of microbial contamination. Moreover, climate change affects the entire dairy supply chain and necessitates adaptation of all the current food safety management programs. In particular, the review of current prerequisite programs might be needed as well as revisiting the current microbial specifications of the receiving dairy products and the introduction of new pretreatments with stringent processing regimes. The effects on microbial changes during distribution and consumer handling also would need to be quantified through the use of predictive models. The development of Quantitative Microbial Risk Assessment (QMRA) models, considering the whole farm-to-fork chain to evaluate risk mitigation strategies, will be a key step to prioritize actions towards a climate change-resilient dairy industry.

EnABLe: An agent-based model to understand Listeria dynamics in food processing facilities

Detection of pathogens in food processing facilities by routine environmental monitoring (EM) is essential to reduce the risk of foodborne illness but is complicated by the complexity of equipment and environment surfaces. To optimize design of EM programs, we developed EnABLe ("Environmental monitoring with an Agent-Based Model of Listeria"), a detailed and customizable agent-based simulation of a built environment. EnABLe is presented here in a model system, tracing Listeria spp. (LS) (an indicator for conditions that allow the presence of the foodborne pathogen Listeria monocytogenes) on equipment and environment surfaces in a cold-smoked salmon facility. EnABLe was parameterized by existing literature and expert elicitation and validated with historical data. Simulations revealed different contamination dynamics and risks among equipment surfaces in terms of the presence, level and persistence of LS. Grouping of surfaces by their LS contamination dynamics identified connectivity and sanitary design as predictors of contamination, indicating that these features should be considered in the design of EM programs to detect LS. The EnABLe modeling approach is particularly timely for the frozen food industry, seeking science-based recommendations for EM, and may also be relevant to other complex environments where pathogen contamination presents risks for direct or indirect human exposure.

Listeria monocytogenes contamination of ready-to-eat foods and the risk for human health in the EU

Food safety criteria for Listeria monocytogenes in ready-to-eat (RTE) foods have been applied from 2006 onwards (Commission Regulation (EC) 2073/2005). Still, human invasive listeriosis was reported to increase over the period 2009-2013 in the European Union and European Economic Area (EU/EEA). Time series analysis for the 2008-2015 period in the EU/EEA indicated an increasing trend of the monthly notified incidence rate of confirmed human invasive listeriosis of the over 75 age groups and female age group between 25 and 44 years old (probably related to pregnancies). A conceptual model was used to identify factors in the food chain as potential drivers for L. monocytogenes contamination of RTE foods and listeriosis. Factors were related to the host (i. population size of the elderly and/or susceptible people; ii. underlying condition rate), the food (iii. L. monocytogenes prevalence in RTE food at retail; iv. L. monocytogenes concentration in RTE food at retail; v. storage conditions after retail; vi. consumption), the national surveillance systems (vii. improved surveillance), and/or the bacterium (viii. virulence). Factors considered likely to be responsible for the increasing trend in cases are the increased population size of the elderly and susceptible population except for the 25-44 female age group. For the increased incidence rates and cases, the likely factor is the increased proportion of susceptible persons in the age groups over 45 years old for both genders. Quantitative modelling suggests that more than 90% of invasive listeriosis is caused by ingestion of RTE food containing > 2,000 colony forming units (CFU)/g, and that one-third of cases are due to growth in the consumer phase. Awareness should be increased among stakeholders, especially in relation to susceptible risk groups. Innovative methodologies including whole genome sequencing (WGS) for strain identification and monitoring of trends are recommended.

Inhibition of Listeria monocytogenes in Fresh Cheese Using Chitosan-Grafted Lactic Acid Packaging

A chitosan from biologically obtained chitin was successfully grafted with d,l-lactic acid (LA) in aqueous media using p-toluenesulfonic acid as catalyst to obtain a non-toxic, biodegradable packaging material that was characterized using scanning electron microscopy, water vapor permeability, and relative humidity (RH) losses. Additionally, the grafting in chitosan with LA produced films with improved mechanical properties. This material successfully extended the shelf life of fresh cheese and inhibited the growth of Listeria monocytogenes during 14 days at 4 °C and 22% RH, whereby inoculated samples with chitosan-g-LA packaging presented full bacterial inhibition. The results were compared to control samples and commercial low-density polyethylene packaging.

Farm to Fork Quantitative Risk Assessment of Listeria monocytogenes Contamination in Raw and Pasteurized Milk Cheese in Ireland

The objective of this study was to model and quantify the level of Listeria monocytogenes in raw milk cheese (RMc) and pasteurized milk cheese (PMc) from farm to fork using a Bayesian inference approach combined with a quantitative risk assessment. The modeling approach included a prediction of contamination arising from the farm environment as well from cross-contamination within the cheese-processing facility through storage and subsequent human exposure. The model predicted a high concentration of L. monocytogenes in contaminated RMc (mean 2.19 log10 CFU/g) compared to PMc (mean -1.73 log10 CFU/g). The mean probability of illness (P1 for low-risk population, LR) and (P2 for high-risk population, HR, e.g., immunocompromised) adult Irish consumers following exposure to contaminated cheese was 7 × 10(-8) (P1 ) and 9 × 10(-4) (P2 ) for RMc and 7 × 10(-10) (P1 ) and 8 × 10(-6) (P2 ) for PMc, respectively. In addition, the model was used to evaluate performance objectives at various stages, namely, the cheese making and ripening stages, and to set a food safety objective at the time of consumption. A scenario analysis predicted various probabilities of L. monocytogenes contamination along the cheese-processing chain for both RMc and PMc. The sensitivity analysis showed the critical factors for both cheeses were the serving size of the cheese, storage time, and temperature at the distribution stage. The developed model will allow food processors and policymakers to identify the possible routes of contamination along the cheese-processing chain and to reduce the risk posed to human health.

Quantitative risk assessment of haemolytic and uremic syndrome linked to O157:H7 and non-O157:H7 Shiga-toxin producing Escherichia coli strains in raw milk soft cheeses

Shiga-toxin producing Escherichia coli (STEC) strains may cause human infections ranging from simple diarrhea to Haemolytic Uremic Syndrome (HUS). The five main pathogenic serotypes of STEC (MPS-STEC) identified thus far in Europe are O157:H7, O26:H11, O103:H2, O111:H8, and O145:H28. Because STEC strains can survive or grow during cheese making, particularly in soft cheeses, a stochastic quantitative microbial risk assessment model was developed to assess the risk of HUS associated with the five MPS-STEC in raw milk soft cheeses. A baseline scenario represents a theoretical worst-case scenario where no intervention was considered throughout the farm-to-fork continuum. The risk level assessed with this baseline scenario is the risk-based level. The impact of seven preharvest scenarios (vaccines, probiotic, milk farm sorting) on the risk-based level was expressed in terms of risk reduction. Impact of the preharvest intervention ranges from 76% to 98% of risk reduction with highest values predicted with scenarios combining a decrease of the number of cow shedding STEC and of the STEC concentration in feces. The impact of postharvest interventions on the risk-based level was also tested by applying five microbiological criteria (MC) at the end of ripening. The five MCs differ in terms of sample size, the number of samples that may yield a value larger than the microbiological limit, and the analysis methods. The risk reduction predicted varies from 25% to 96% by applying MCs without preharvest interventions and from 1% to 96% with combination of pre- and postharvest interventions.

Modelling the effect of lactic acid bacteria from starter- and aroma culture on growth of Listeria monocytogenes in cottage cheese

Four mathematical models were developed and validated for simultaneous growth of mesophilic lactic acid bacteria from added cultures and Listeria monocytogenes, during chilled storage of cottage cheese with fresh- or cultured cream dressing. The mathematical models include the effect of temperature, pH, NaCl, lactic- and sorbic acid and the interaction between these environmental factors. Growth models were developed by combining new and existing cardinal parameter values. Subsequently, the reference growth rate parameters (μref at 25°C) were fitted to a total of 52 growth rates from cottage cheese to improve model performance. The inhibiting effect of mesophilic lactic acid bacteria from added cultures on growth of L. monocytogenes was efficiently modelled using the Jameson approach. The new models appropriately predicted the maximum population density of L. monocytogenes in cottage cheese. The developed models were successfully validated by using 25 growth rates for L. monocytogenes, 17 growth rates for lactic acid bacteria and a total of 26 growth curves for simultaneous growth of L. monocytogenes and lactic acid bacteria in cottage cheese. These data were used in combination with bias- and accuracy factors and with the concept of acceptable simulation zone. Evaluation of predicted growth rates of L. monocytogenes in cottage cheese with fresh- or cultured cream dressing resulted in bias-factors (Bf) of 1.07-1.10 with corresponding accuracy factor (Af) values of 1.11 to 1.22. Lactic acid bacteria from added starter culture were on average predicted to grow 16% faster than observed (Bf of 1.16 and Af of 1.32) and growth of the diacetyl producing aroma culture was on average predicted 9% slower than observed (Bf of 0.91 and Af of 1.17). The acceptable simulation zone method showed the new models to successfully predict maximum population density of L. monocytogenes when growing together with lactic acid bacteria in cottage cheese. 11 of 13 simulations of L. monocytogenes growth were within the acceptable simulation zone, which demonstrated good performance of the empirical inter-bacterial interaction model. The new set of models can be used to predict simultaneous growth of mesophilic lactic acid bacteria and L. monocytogenes in cottage cheese during chilled storage at constant and dynamic temperatures. The applied methodology is likely to be applicable for safety prediction of other types of fermented and unripened dairy products where inhibition by lactic acid bacteria is important for growth of pathogenic microorganisms.

Modeling the growth of Listeria monocytogenes on the surface of smear- or mold-ripened cheese

Surface-ripened cheeses are matured by means of manual or mechanical technologies posing a risk of cross-contamination, if any cheeses are contaminated with Listeria monocytogenes. In predictive microbiology, primary models are used to describe microbial responses, such as growth rate over time and secondary models explain how those responses change with environmental factors. In this way, primary models were used to assess the growth rate of L. monocytogenes during ripening of the cheeses and the secondary models to test how much the growth rate was affected by either the pH and/or the water activity (aw) of the cheeses. The two models combined can be used to predict outcomes. The purpose of these experiments was to test three primary (the modified Gompertz equation, the Baranyi and Roberts model, and the Logistic model) and three secondary (the Cardinal model, the Ratowski model, and the Presser model) mathematical models in order to define which combination of models would best predict the growth of L. monocytogenes on the surface of artificially contaminated surface-ripened cheeses. Growth on the surface of the cheese was assessed and modeled. The primary models were firstly fitted to the data and the effects of pH and aw on the growth rate (μmax) were incorporated and assessed one by one with the secondary models. The Logistic primary model by itself did not show a better fit of the data among the other primary models tested, but the inclusion of the Cardinal secondary model improved the final fit. The aw was not related to the growth of Listeria. This study suggests that surface-ripened cheese should be separately regulated within EU microbiological food legislation and results expressed as counts per surface area rather than per gram.

Development and validation of a stochastic model for potential growth of Listeria monocytogenes in naturally contaminated lightly preserved seafood

A new stochastic model for the simultaneous growth of Listeria monocytogenes and lactic acid bacteria (LAB) was developed and validated on data from naturally contaminated samples of cold-smoked Greenland halibut (CSGH) and cold-smoked salmon (CSS). During industrial processing these samples were added acetic and/or lactic acids. The stochastic model was developed from an existing deterministic model including the effect of 12 environmental parameters and microbial interaction (O. Mejlholm and P. Dalgaard, Food Microbiology, submitted for publication). Observed maximum population density (MPD) values of L. monocytogenes in naturally contaminated samples of CSGH and CSS were accurately predicted by the stochastic model based on measured variability in product characteristics and storage conditions. Results comparable to those from the stochastic model were obtained, when product characteristics of the least and most preserved sample of CSGH and CSS were used as input for the existing deterministic model. For both modelling approaches, it was shown that lag time and the effect of microbial interaction needs to be included to accurately predict MPD values of L. monocytogenes. Addition of organic acids to CSGH and CSS was confirmed as a suitable mitigation strategy against the risk of growth by L. monocytogenes as both types of products were in compliance with the EU regulation on ready-to-eat foods.

Sensitivity analysis for critical control points determination and uncertainty analysis to link FSO and process criteria: application to Listeria monocytogenes in soft cheese made from pasteurized milk

Microbiological food safety is an important economic and health issue in the context of globalization and presents food business operators with new challenges in providing safe foods. The hazard analysis and critical control point approach involve identifying the main steps in food processing and the physical and chemical parameters that have an impact on the safety of foods. In the risk-based approach, as defined in the Codex Alimentarius, controlling these parameters in such a way that the final products meet a food safety objective (FSO), fixed by the competent authorities, is a big challenge and of great interest to the food business operators. Process risk models, issued from the quantitative microbiological risk assessment framework, provide useful tools in this respect. We propose a methodology, called multivariate factor mapping (MFM), for establishing a link between process parameters and compliance with a FSO. For a stochastic and dynamic process risk model of Listeriamonocytogenes in soft cheese made from pasteurized milk with many uncertain inputs, multivariate sensitivity analysis and MFM are combined to (i) identify the critical control points (CCPs) for L.monocytogenes throughout the food chain and (ii) compute the critical limits of the most influential process parameters, located at the CCPs, with regard to the specific process implemented in the model. Due to certain forms of interaction among parameters, the results show some new possibilities for the management of microbiological hazards when a FSO is specified.