Use of Bayesian modelling in risk assessment: application to growth of Listeria monocytogenes and food flora in cold-smoked salmon

A perspective on data sharing in digital food safety systems

In this age of data, digital tools are widely promoted as having tremendous potential for enhancing food safety. However, the potential of these digital tools depends on the availability and quality of data, and a number of obstacles need to be overcome to achieve the goal of digitally enabled "smarter food safety" approaches. One key obstacle is that participants in the food system and in food safety often lack the willingness to share data, due to fears of data abuse, bad publicity, liability, and the need to keep certain data (e.g., human illness data) confidential. As these multifaceted concerns lead to tension between data utility and privacy, the solutions to these challenges need to be multifaceted. This review outlines the data needs in digital food safety systems, exemplified in different data categories and model types, and key concerns associated with sharing of food safety data, including confidentiality and privacy of shared data. To address the data privacy issue a combination of innovative strategies to protect privacy as well as legal protection against data abuse need to be pursued. Existing solutions for maximizing data utility, while not compromising data privacy, are discussed, most notably differential privacy and federated learning.

Growth behavior of Shiga toxin-producing Escherichia coli, Salmonella, and generic E. coli in raw pork considering background microbiota at 10, 25, and 40 °C

Recent epidemiological evidence suggests that pork products may be vehicles for the transmission of Shiga toxin-producing Escherichia coli (STEC) to humans. The severe morbidity associated with STEC infections highlights the need for research to understand the growth behavior of these bacteria in pork products. Classical predictive models can estimate pathogen growth in sterile meat. However, competition models considering background microbiota reflect a more realistic scenario for raw meat products. The objective of this study was to estimate the growth kinetics of clinically significant STEC (O157, non-O157, and O91), Salmonella, and generic E. coli in raw ground pork using competition primary growth models at temperature abuse (10 and 25 °C) and sublethal temperature (40 °C). A competition model incorporating the No lag Buchanan model was validated using the acceptable prediction zone (APZ) method where >92 % (1498/1620) of the residual errors fell within the APZ (pAPZ > 0.70). The background microbiota (mesophilic aerobic plate counts, APC) inhibited the growth of STEC and Salmonella indicating a simple one-directional competitive interaction between pathogens and the mesophilic microbiota of ground pork. The maximum specific growth rate (μ_max) of all the bacterial groups was not significantly different (p > 0.05) based on fat content (5 vs 25 %) except for generic E. coli at 10 °C. E. coli O157 and non-O157 behaved similarly in terms of μ_max and maximum population density (MPD). Salmonella showed a similar (p > 0.05) μ_max to E. coli O157 and non-O157 at 10 and 40 °C but a significantly higher rate (p < 0.05) at 25 °C. STEC were more prone to be inhibited by APC than Salmonella at 10 and 25 °C. The μ_max of O91 was lower (p < 0.05) than other STEC and Salmonella at 10 and 25 °C but similar (p > 0.05) at 40 °C. Generic E. coli showed a two- to five-times higher (p < 0.05) μ_max (0.028 ± 0.011 log₁₀ CFU/h) than other bacterial groups (0.006 ± 0.004 to 0.012 ± 0.003 log₁₀ CFU/h) at 10 °C making it a potential indicator bacteria for process control. Industry and regulators can use competitive models to develop appropriate risk assessment and mitigation strategies to improve the microbiological safety of raw pork products.

Transcriptomics Reveals the Effect of Strain Interactions on the Growth of A. Oryzae and Z. Rouxii

The microbial community structure in traditional fermented foods is quite complex, making the relationship between strains unclear. In this regard, the co-culture system can simulate microbial interactions during food fermentation and reveal the morphological changes, metabolic processes, and gene expression of microbial communities. The present study sought to investigate the effects of microbial interactions on the growth of Aspergillus oryzae and Zygosaccharomyces rouxii through omics. After co-cultivation, the pH value and dry weight were consistent with the pure culture of Z. rouxii. Additionally, the consumption of reducing sugar decreased, and the enzymatic activity increased compared with the pure culture of fungus. The analysis of volatile organic compounds (VOCs) and transcriptomics showed that co-culture significantly promoted the effect on Z. rouxii. A total of 6 different VOCs and 2202 differentially expressed genes were identified in the pure and co-culture of Z. rouxii. The differentially expressed genes were mainly related to the endonucleolytic cleavage of rRNA, ribosome biogenesis in eukaryotes, and RNA polymerase metabolic pathways. The study results will provide insights into the effect of microbial interactions on the growth of A. oryzae and Z. rouxii.

Modeling the Growth of Six Listeria monocytogenes Strains in Smoked Salmon Pâté

In this study, the growth of six L. monocytogenes strains isolated from different fish products was quantified and modeled in smoked salmon pâté at a temperature ranging from 2 to 20 °C. The experimental data obtained for each strain was fitted to the primary growth model of Baranyi and Roberts to estimate the following kinetic parameters: lag phase (λ), maximum specific growth rate (μ_max), and maximum cell density (N_max). Then, the effect of storage temperature on the obtained μ_max values was modeled by the Ratkowsky secondary model. In general, the six L. monocytogenes strains showed rapid growth in salmon pâté at all storage temperatures, with a relatively short lag phase λ, even at 2 °C. The growth behavior among the tested strains was similar at the same storage temperature, although significant differences were found for the parameters λ and μ_max. Besides, the growth variations among the strains did not follow a regular pattern. The estimated secondary model parameter T_min ranged from -4.25 to -3.19 °C. This study provides accurate predictive models for the growth of L. monocytogenes in fish pâtés that can be used in shelf life and microbial risk assessment studies. In addition, the models generated in this work can be implemented in predictive modeling tools and repositories that can be reliably and easily used by the fish industry and end-users to establish measures aimed at controlling the growth of L. monocytogenes in fish-based pâtés.

Development of a Modeling Tool To Assess and Reduce Regulatory and Recall Risks for Cold-Smoked Salmon Due to Listeria monocytogenes Contamination

ABSTRACT

Although public health risk assessments for Listeria monocytogenes (Lm) have been published for various foods, firm-level decision making on interventions targeting Lm involves considerations of both public health and enterprise risks. Smoked seafood is a ready-to-eat product with a high incidence of Lm contamination and has been associated with several recalls. We used cold-smoked salmon as a model product to develop a decision support tool (the regulatory and recall risk [3R] model) to estimate (i) baseline regulatory and recall (RR) risks (i.e., overall risks of a lot sampled and found positive for Lm, e.g., by food regulatory agencies) due to Lm contamination and (ii) the RR risk reduction that can be achieved through interventions with underlying mechanisms such as reducing the prevalence and/or level of Lm and retarding or preventing Lm growth. Given that a set number of samples (e.g., 10) are tested for a given lot, the RR risk equals the likelihood of detecting Lm in at least one sample. Under the baseline scenario, which assumes a 4% Lm prevalence and no interventions, the median predicted RR risk for a given production lot was 0.333 (95% credible interval: 0.288, 0.384) when 10 25-g samples were tested. Nisin treatments, which reduce both the prevalence and initial level of Lm, reduced RR risks in a concentration-dependent manner to 0.109 (0.074, 0.146) with 5 ppm, 0.049 (0.024, 0.083) with 10 ppm, and 0.017 (0.007, 0.033) with 20 ppm. In general, more effective reduction in RR risks can be achieved by reducing Lm prevalence than by retarding Lm growth; the RR risk was reduced to 0.182 (0.153, 0.213) by a 50% prevalence reduction but to only 0.313 (0.268, 0.367) by bacteriostatic growth inhibitors. Sensitivity analysis indicated that prevalence and initial level of Lm and storage temperature have the greatest impact on predicting RR risks, suggesting that reliable data for these parameters will improve model performance.

HIGHLIGHTS

Genetic, physiological, and cellular heterogeneities of bacterial pathogens in food matrices: Consequences for food safety

In complex food systems, bacteria live in heterogeneous microstructures, and the population displays phenotypic heterogeneities at the single-cell level. This review provides an overview of spatiotemporal drivers of phenotypic heterogeneity of bacterial pathogens in food matrices at three levels. The first level is the genotypic heterogeneity due to the possibility for various strains of a given species to contaminate food, each of them having specific genetic features. Then, physiological heterogeneities are induced within the same strain, due to specific microenvironments and heterogeneous adaptative responses to the food microstructure. The third level of phenotypic heterogeneity is related to cellular heterogeneity of the same strain in a specific microenvironment. Finally, we consider how these phenotypic heterogeneities at the single-cell level could be implemented in mathematical models to predict bacterial behavior and help ensure microbiological food safety.

Initial and Final Cell Concentrations Significantly Influence the Maximum Growth Rate of Listeria monocytogenes in Published Literature Data for Whole Intact Fresh Produce

ABSTRACT

Listeria monocytogenes has shown the ability to grow on fresh uncut produce; however, the factors that control growth are not well understood. Peer-reviewed journal articles (n = 29) meeting the inclusion criteria and related to the growth of L. monocytogenes on fresh produce were found through university library databases and Google Scholar searches. Growth models were fit to each of the extracted 130 data sets to estimate log CFU per day rates of growth by using the DMFit tool. Multiple linear stepwise regression models for factors influencing growth rate were developed using R software. Factors included were temperature, nutrient level of inoculation buffer, initial cell concentration, final cell concentration, inoculation method, container permeability, and surface characteristics. The full model produced adjusted R2, Akaike information criterion, and root mean square error values of 0.41, 488, and 1.61, respectively. Stepwise regression resulted in a reduced model with parameters for incubation temperature, inoculation buffer type, initial and final cell concentrations, container characteristics, and produce surface characteristics. Model fit statistics improved slightly in the reduced model. A further reduced three-parameter model included storage temperature and initial and final cell concentrations, with interaction terms. This three-parameter model had adjusted R2, Akaike information criterion, and root mean square error values of 0.66, 417, and 1.24, respectively. Incubation temperature (P = 1.00E-09) initial cell concentration (P = 3.05E-12), and final cell concentration (P = 4.17E-09) all had highly significant effects on maximum growth rate. Our findings show the importance of inoculum concentration and produce microbial carrying capacity on the estimated growth rate and highlight the overall importance that temperature has on growth rate. Future experiments should consider initial inoculum concentration carefully when conducting growth studies for L. monocytogenes on whole produce.

HIGHLIGHTS

A Bayesian Approach to Describe and Simulate the pH Evolution of Fresh Meat Products Depending on the Preservation Conditions

Measuring the pH of meat products during storage represents an efficient way to monitor microbial spoilage, since pH is often linked to the growth of several spoilage-associated microorganisms under different conditions. The present work aimed to develop a modelling approach to describe and simulate the pH evolution of fresh meat products, depending on the preservation conditions. The measurement of pH on fresh poultry sausages, made with several lactate formulations and packed under three modified atmospheres (MAP), from several industrial production batches, was used as case-study. A hierarchical Bayesian approach was developed to better adjust kinetic models while handling a low number of measurement points. The pH changes were described as a two-phase evolution, with a first decreasing phase followed by a stabilisation phase. This stabilisation likely took place around the 13th day of storage, under all the considered lactate and MAP conditions. The effects of lactate and MAP on pH previously observed were confirmed herein: (i) lactate addition notably slowed down acidification, regardless of the packaging, whereas (ii) the 50%CO₂-50%N₂ MAP accelerated the acidification phase. The Bayesian modelling workflow—and the script—could be used for further model adaptation for the pH of other food products and/or other preservation strategies.

Modeling the combined effect of temperature, pH, acetic and lactic acid concentrations on the growth/no growth interface of acid-tolerant Bacillus spores

The application of minimal processing technologies has led to increased spoilage incidents in low-acid pasteurized sauces due to the outgrowth of acid-tolerant spore-forming spoilage bacteria (ATSSB). Controlling the germination and subsequent growth of ATSSB spores is vital to enhance the ambient storage stability of pasteurized sauces. This study developed and validated a set of growth/no growth (G/NG) models for spores of two ATSSB strains (Bacillus velezensis and Bacillus subtilis subsp. subtilis) isolated from pasteurized sauces. The G/NG data at two levels of temperature (22 and 30 °C) were collected in Nutrient Broth (a_w = 0.98 adjusted with NaCl) by a full factorial design with five equidistant levels of pH (4.4-5.6), four concentrations of total acetic acid (0.0-0.3% (w/w)), and four concentrations of total lactic acid (0.00-1.00% (w/w)). The growth, starting from heat-treated (10 min 80 °C) spores, of each strain was assessed under 160 combinations by regular optical density measurements during three months. Twelve replicates were made for each combination. The developed models demonstrate that without organic acids even the lowest pH (4.4) allows a high growth possibility of the ATSSB spores, while acetic and lactic acids exhibit a significant antibacterial activity, which can be enhanced at decreased pH. The growth starting from B. subtilis spores can be inhibited for at least three months with 1.0% (w/w) total lactic acid in the water phase at both temperatures, which was not the case for B. velezensis, while 0.3% acetic acid achieves a full inhibition on both strains at 22 °C. With a combination of 0.3% acetic acid and 0.7% lactic acid, no growth should occur in the investigated range. This research is one of the first studies exploring the feasibility of ambient storage for low-acid pasteurized sauces eliminating preservatives such as benzoic and sorbic acids, and proves the synergistic effect of decreased pH and the presence of acetic and lactic acids on inhibiting bacterial growth from ATSSB spores.

Competitive growth kinetics of Campylobacter jejuni, Escherichia coli O157:H7 and Listeria monocytogenes with enteric microflora in a small-intestine model

Aims

The biological events occurring during human digestion help to understand the mechanisms underlying the dose–response relationships of enteric bacterial pathogens. To better understand these events, we investigated the growth and reduction behaviour of bacterial pathogens in an in vitro model simulating the environment of the small intestine.

Methods and Results

The foodborne pathogens Campylobacter jejuni, Listeria monocytogenes and Escherichia coli O157:H7 were cultured with multiple competing enteric bacteria. Differences in the pathogen's growth kinetics due to the relative amount of competing enteric bacteria were investigated. These growth differences were described using a mathematical model based on Bayesian inference. When pathogenic and enteric bacteria were inoculated at 1 log CFU per ml and 9 log CFU per ml, respectively, L. monocytogenes was inactivated over time, while C. jejuni and E. coli O157:H7 survived without multiplying. However, as pathogen inocula were increased, its inhibition by enteric bacteria also decreased.

Conclusions

Although the growth of pathogenic species was inhibited by enteric bacteria, the pathogens still survived.

Significance and Impact of the Study

Competition experiments in a small‐intestine model have enhanced understanding of the infection risk in the intestine and provide insights for evaluating dose–response relationships.

Evaluation of Strain Variability in Inactivation of Campylobacter jejuni in Simulated Gastric Fluid by Using Hierarchical Bayesian Modeling

This study was conducted to quantitatively evaluate the variability of stress resistance in different strains of Campylobacter jejuni and the uncertainty of such strain variability. We developed Bayesian statistical models with multilevel analysis to quantify variability within a strain, variability between different strains, and the uncertainty associated with these estimates. Furthermore, we measured the inactivation of 11 strains of C. jejuni in simulated gastric fluid with low pH, using the Weibullian survival model. The model was first developed for separate pH conditions and then analyzed over a range of pH levels. We found that the model parameters developed under separate pH conditions exhibited a clear dependence of survival on pH. In addition, the uncertainty of the variability between different strains could be described as the joint distribution of the model parameters. The latter model, including pH dependency, accurately predicted the number of surviving cells in individual as well as multiple strains. In conclusion, variabilities and uncertainties in inactivation could be simultaneously evaluated and interpreted via a probabilistic approach based on Bayesian theory. Such hierarchical Bayesian models could be useful for understanding individual-strain variability in quantitative microbial risk assessment. IMPORTANCE Since microbial strains vary in their growth and inactivation patterns in food materials, it is important to accurately predict these patterns for quantitative microbial risk assessment. However, most previous studies in this area have used highly resistant strains, which could lead to inaccurate predictions. Moreover, variability, including measurement errors and variability within a strain and between different strains, can contribute to predicted individual-level outcomes. Therefore, a multilevel framework is required to resolve these levels of variability and estimate their uncertainties. We developed a Bayesian predictive model for the survival of Campylobacter jejuni under simulated gastric conditions taking into account the variabilities and uncertainties. We demonstrated a high correspondence between predictions from the model and empirical measurements. The modeling procedure proposed in this study recommends a novel framework for predicting pathogen behavior, which can help improve quantitative microbial risk assessment during food production and distribution.

Predicting growth of Listeria monocytogenes at dynamic conditions during manufacturing, ripening and storage of cheeses - Evaluation and application of models

Mathematical models were evaluated to predict growth of L. monocytogenes in mould/smear-ripened cheeses with measured dynamic changes in product characteristics and storage conditions. To generate data for model evaluation three challenge tests were performed with mould-ripened cheeses produced by using milk inoculated with L. monocytogenes. Growth of L. monocytogenes and lactic acid bacteria (LAB) in the rind and in the core of cheeses were quantified together with changes in product characteristics over time (temperature, pH, NaCl/a_w, lactic- and acetic acid concentrations). The performance of nine available L. monocytogenes growth models was evaluated using growth responses from the present study and from literature together with the determined or reported dynamic product characteristics and storage conditions (46 kinetics). The acceptable simulation zone (ASZ) method was used to assess model performance. A reduced version of the Martinez-Rios et al. (2019) model (https://doi.org/10.3389/fmicb.2019.01510) and the model of Østergaard et al. (2014) (https://doi.org/10.1016/j.ijfoodmicro.2014.07.012) had acceptable performance with a ASZ-score of 71-70% for L. monocytogenes growth in mould/smear-ripened cheeses. Models from Coroller et al. (2012) (https://doi.org/10.1016/j.ijfoodmicro.2011.09.023) had close to acceptable performance with ASZ-scores of 67-69%. The validated models (Martinez-Rios et al., 2019; Østergaard et al., 2014) can be used to facilitate the evaluation of time to critical L. monocytogenes growth for mould/smear-ripened cheeses including modification of recipes with for example reduced salt/sodium or to support exposure assessment studies for these cheeses.

Strain variability in biofilm formation: A food safety and quality perspective

The inherent differences in microbial behavior among identically treated strains of the same microbial species, referred to as "strain variability", are regarded as an important source of variability in microbiological studies. Biofilms are defined as the structured multicellular communities with complex architecture that enable microorganisms to grow adhered to abiotic or living surfaces and constitute a fundamental aspect of microbial ecology. The research studies assessing the strain variability in biofilm formation are relatively few compared to the ones evaluating other aspects of microbial behavior such as virulence, growth and stress resistance. Among the available research data on intra-species variability in biofilm formation, compiled and discussed in the present review, most of them refer to foodborne pathogens as compared to spoilage microorganisms. Molecular and physiological aspects of biofilm formation potentially related to strain-specific responses, as well as information on the characterization and quantitative description of this type of biological variability are presented and discussed. Despite the considerable amount of available information on the strain variability in biofilm formation, there are certain data gaps and still-existing challenges that future research should cover and address. Current and future advances in systems biology and omics technologies are expected to aid significantly in the explanation of phenotypic strain variability, including biofilm formation variability, allowing for its integration in microbiological risk assessment.

Preliminary Characterization of Yeasts from Bombino Bianco, a Grape Variety of Apulian Region, and Selection of an Isolate as a Potential Starter

Eighty-seven yeasts were isolated from Bombino bianco, a white grape variety from Apulian Region (Southern Italy). The isolates were characterized for the splitting of arbutin, the hydrolysis of pectins, sulphite production, the resistance to acetic acid, SO2, and ethanol. An enhanced arbutin splitting (β-glucosidase) and a moderate pectolytic activity were found. Concerning ethanol resistance, the most of yeast population showed a low-to-moderate resistance, but some isolates, identified as Saccharomyces cerevisiae, were able to grow in presence of 15% v/v of ethanol. Four isolates were selected (coded as 43D, 44D, 45D, and 46D), studied for their ability to decarboxylate amino acids and used in small-scale fermentation trial; for this last experiment a reference strain was used (S. cerevisiae EC1118). This experiment suggested the existence of an isolate (S. cerevisiae 46D) with interesting traits and performances, which could be potentially proposed as a starter for Bombino bianco.

Inactivation of Listeria monocytogenes, Staphylococcus aureus, and Salmonella enterica under High Hydrostatic Pressure: A Quantitative Analysis of Existing Literature Data

High hydrostatic pressure processing (HPP) is a mild preservation technique, and its use for processing foods has been widely documented in the literature. However, very few quantitative synthesis studies have been conducted to gather and analyze bacterial inactivation data to identify the mechanisms of HPP-induced bacterial inactivation. The purpose of this study was to conduct a quantitative analysis of three-decimal reduction times (t_3δ) from a large set of existing studies to determine the main influencing factors of HPP-induced inactivation of three foodborne pathogens (Listeria monocytogenes, Staphylococcus aureus, and Salmonella enterica) in various foods. Inactivation kinetics data sets from 1995 to 2017 were selected, and t_3δ values were first estimated by using the nonlinear Weibull model. Bayesian inference was then used within a metaregression analysis to build and test several models and submodels. The best model (lowest error and most parsimonious) was a hierarchical mixed-effects model including pressure intensity, temperature, study, pH, species, and strain as explicative variables and significant factors. Values for t_3δ and Z_P associated with inactivation under HPP were estimated for each bacterial pathogen, with their associated variability. Interstudy variability explained most of the variability in t_3δ values. Strain variability was also important and exceeded interstudy variability for S. aureus, which prevented the development of an overall model for this pathogen. Meta-analysis is not often used in food microbiology but was a valuable quantitative tool for modeling inactivation of L. monocytogenes and Salmonella in response to HPP treatment. Results of this study could be useful for refining quantitative assessment of the effects of HPP on vegetative foodborne pathogens or for more precisely designing costly and labor-intensive experiments with foodborne pathogens.

Describing Uncertainty in Salmonella Thermal Inactivation Using Bayesian Statistical Modeling

Uncertainty analysis is the process of identifying limitations in scientific knowledge and evaluating their implications for scientific conclusions. In the context of microbial risk assessment, the uncertainty in the predicted microbial behavior can be an important component of the overall uncertainty. Conventional deterministic modeling approaches which provide point estimates of the pathogen's levels cannot quantify the uncertainty around the predictions. The objective of this study was to use Bayesian statistical modeling for describing uncertainty in predicted microbial thermal inactivation of Salmonella enterica Typhimurium DT104. A set of thermal inactivation data in broth with water activity adjusted to 0.75 at 9 different temperature conditions obtained from the ComBase database (www.combase.cc) was used. A log-linear microbial inactivation was used as a primary model while for secondary modeling, a linear relation between the logarithm of inactivation rate and temperature was assumed. For comparison, data were fitted with a two-step and a global Bayesian regression. Posterior distributions of model's parameters were used to predict Salmonella thermal inactivation. The combination of the joint posterior distributions of model's parameters allowed the prediction of cell density over time, total reduction time and inactivation rate as probability distributions at different time and temperature conditions. For example, for the time required to eliminate a Salmonella population of about 107 CFU/ml at 65°C, the model predicted a time distribution with a median of 0.40 min and 5th and 95th percentiles of 0.24 and 0.60 min, respectively. The validation of the model showed that it can describe successfully uncertainty in predicted thermal inactivation with most observed data being within the 95% prediction intervals of the model. The global regression approach resulted in less uncertain predictions compared to the two-step regression. The developed model could be used to quantify uncertainty in thermal inactivation in risk-based processing design as well as in risk assessment studies.

Quantitative risk assessment of Listeria monocytogenes in bulk cooked meat from production to consumption in China: a Bayesian approach

BACKGROUND

To estimate the public health risk related to cooked meat in bulk products contaminated with Listeria monocytogenes, a generic Bayesian network (BN) risk-assessment model was developed to simulate influencing factors and processes of products from the industry level to the consumer level. To quantify the model, parameter values of prior distributions were acquired from the literature, websites, and expert opinions. Using the Markov chain Monte Carlo (MCMC) simulation approach, posterior probability distributions were calculated according to the incorporated evidence, which allowed us to predict various risks affected by processing variability from production to consumption.

RESULTS

The average risks of listeriosis from consuming cooked meat in bulk products are 8.40 × 10^-7 , 2.58 × 10^-8 , 8.24 × 10^-7 , and 1.05 × 10^-6 per meal for children, young people, elderly people, and pregnant women, respectively. The estimated mean number of listeriosis cases is 5 per 100 000 people per year in China.

CONCLUSION

Although only a conceptual BN model is given, it manifests the principles and characteristics of mathematical methods. The BN model can also provide significant benefits for quantitative risk assessment by incorporating all available data and by updating beliefs. © 2018 Society of Chemical Industry.

A case study on the use of ultrasound for the inhibition of Escherichia coli O157:H7 and Listeria monocytogenes in almond milk

The interest towards not-dairy beverages is increasing and almond milk is widespread diffused. The main topic of this paper was a focus on Ultrasound (US) to inhibit Escherichia coli O157:H7 and Listeria monocytogenes. The variables of the treatment (power, duration, and pulse) were combined through a randomized design; the use of DoE theory (Design of Experiments) and its outputs (Pareto chart, 3D plots, desirability profiles) pointed out that the effect of the treatment relies upon the total energy distributed into the system on E. coli O157:H7, as suggested by the significance of interactions of power, pulse and time, while power was the most important factor for L. monocytogenes. A final challenge test was done by using two combinations (H-80% of power, 8 min and pulse at 6 s- for E. coli and F-80%; 2 min; 6 s- for L. monocytogenes) and storing the samples at 4 °C for 2 weeks. This experiment suggests that the treatment could exert a sub-lethal injury on the pathogens, which, combined with the storage under refrigeration, could contribute to increase the shelf life.

A Preliminary Report on the Use of the Design of Experiments for the Production of a Synbiotic Yogurt Supplemented With Gluten FriendlyTM Flour and Bifidobacterium infantis

The main goal of this paper was to design a synbiotic yogurt containing Bifidobacterium infantis and Gluten Friendly FlourTM; the proposed approach relies upon milk fermentation through the classical starter of yogurt (Lactobacillus delbrueckii subsp. bulgaricus and Streptococcus thermophilus) to avoid a strong production of acetic acid by bifidobacterial and inoculum of B. infantis after the fermentation. The research was divided in 3 steps. The aim of the first step was the optimization of fermentation kinetic by L. delbureckii and S. thermophilus, by combining the amount of flour (either Gluten Friendly Flour-GF- or Control Flour-CF) in milk, temperature and inoculum level; the factors were combined through a mixture design. As a result of this step, the best combination was pointed out: flour at 2.5 g/l; L. delbrueckii subsp. bulgaricus at 6 log cfu/ml; temperature at 37-40°C. The goal of the second step was to study the effect of flour (2.5 g/l) on the viability of B. infantis. GF prolonged the viability of the probiotic for 14 days. In the last step, a synbiotic yogurt, supplemented with GF and fermented with L. delbureckii and S. thermophilus, and then inoculated with B. infantis, was produced. The product was stored at 8 and 15°C. A positive effect of GF was found at 15°C, with B. infantis at 7.0 log cfu/g in GF sample and 5.5.5.7 log cfu/g in CF sample.

The Impact of Gluten Friendly Flour on the Functionality of an Active Drink: Viability of Lactobacillus acidophilus in a Fermented Milk

The Gluten Friendly^TM Technology is an innovative method that induces structural changes in gluten proteins. In this paper a synbiotic fermented milk, containing Lactobacillus acidophilus La-5 and Gluten Friendly Flour (GFF), was proposed. A mixture design was used to combine flour, temperature and probiotic to study the effects of these variables on the acidification. The experiments were done on both GFF and control flour (CF). Thus, the following conditions were chosen to produce the fermented milk: L. acidophilus at 6.5 log cfu/ml; flour at 2.5 g/l; temperature at 37°C. Then, the fermented milk was produced and stored at 4°C for 90 days. The most important result was the positive effect of GFF on the viability of the probiotic, with a prolongation of the shoulder length to 20 days (12–13 days in the control). Moreover, GFF did not act on the sensory scores and on the physico-chemical parameters.

Listeria monocytogenes strains show large variations in competitive growth in mixed culture biofilms and suspensions with bacteria from food processing environments

Interactions and competition between resident bacteria in food processing environments could affect their ability to survive, grow and persist in microhabitats and niches in the food industry. In this study, the competitive ability of L. monocytogenes strains grown together in separate culture mixes with other L. monocytogenes (L. mono mix), L. innocua (Listeria mix), Gram-negative bacteria (Gram- mix) and with a multigenera mix (Listeria + Gram- mix) was investigated in biofilms on stainless steel and in suspensions at 12 °C. The mixed cultures included resident bacteria from processing surfaces in meat and salmon industry represented by L. monocytogenes (n = 6), L. innocua (n = 5) and Gram-negative bacteria (n = 6; Acinetobacter sp., Pseudomonas fragi, Pseudomonas fluorescens, Serratia liquefaciens, Stenotrophomonas maltophilia). Despite hampered in growth in mixed cultures, L. monocytogenes established in biofilms with counts at day nine between 7.3 and 9.0 log per coupon with the lowest counts in the Listeria + G- mix that was dominated by Pseudomonas. Specific L. innocua inhibited growth of L. monocytogenes strains differently; inhibition that was further enhanced by the background Gram-negative microbiota. In these multispecies and multibacteria cultures, the growth competitive effects lead to the dominance of a strong competitor L. monocytogenes strain that was only slightly inhibited by L. innocua and showed strong competitive abilities in mixed cultures with resident Gram-negative bacteria. The results indicates complex patterns of bacterial interactions and L. monocytogenes inhibition in the multibacteria cultures that only partially depend on cell contact and likely involve various antagonistic and bacterial tolerance mechanisms. The study indicates large variations among L. monocytogenes in their competitiveness under multibacterial culture conditions that should be considered in further studies towards understanding of L. monocytogenes persistence in food processing facilities.

A Quantitative Assessment of the Risk of Human Salmonellosis Arising from the Consumption of Almonds in the United States: The Impact of Preventive Treatment Levels

The presence of Salmonella on almonds continues to result in product-related outbreaks and recalls in the United States. In this study, the impact of microbial reduction treatment levels (1 to 5 log CFU) on the risk of human salmonellosis from the consumption of almond kernels in the United States was evaluated. An exposure model, including major steps in almond processing, was used to estimate prevalence and levels of contamination of Salmonella on almonds at the point of consumption. A Salmonella dose-response model and consumption data for almonds in the United States were used to assess risk of illness per serving and per year, quantifying variability and uncertainty separately. A 3-log reduction treatment resulted in a predicted mean risk of illness of two cases per year for almonds consumed as a core product not cooked at home (95% confidence interval [CI], one to four cases), one case per year for almonds consumed as an ingredient not cooked at home (95% CI, one to two cases), and less than one case per year for almonds consumed as an ingredient cooked at home (95% CI, 7 × 10-7 to 3 × 10-6 cases). A minimum 4-log reduction treatment resulted in an estimated mean risk of illness below one case per year in the United States. This study also includes an assessment of the risk of human salmonellosis as a result of an exceptional situation, which results in higher risk estimates compared with the baseline model. The exceptional situations modeled posttreatment resulted in estimates of mean risk that were not significantly affected by treatment level. Sensitivity analysis results showed initial Salmonella contamination level to be the factor with the most impact on risk per serving estimates, given a certain treatment level. The risk assessment also includes a simulation of the events that occurred in 2001. Treatment levels with a minimum 4-log microbial reduction would have been sufficient to prevent the outbreak cases. The uncertainty range in the estimates indicates that additional information is needed to make more precise predictions of this specific outbreak event.

Modelling and predicting growth of psychrotolerant pseudomonads in milk and cottage cheese

Mathematical models were developed and evaluated for growth of psychrotolerant pseudomonads in chilled milk and in cottage cheese with cultured cream dressing. The mathematical models include the effect of temperature, pH, NaCl, lactic acid and sorbic acid. A simplified cardinal parameter growth rate model was developed based on growth in broth. Subsequently, the reference growth rate parameter μref25°C-broth of 1.031/h was calibrated by fitting the model to a total of 35 growth rates from cottage cheese with cultured cream dressing. This resulted in a μref25°C-cottage cheese value of 0.621/h. Predictions from both growth rate models were evaluated by comparison with literature and experimental data. Growth of psychrotolerant pseudomonads in heat-treated milk (n=33) resulted in a bias factor (Bf) of 1.08 and an accuracy factor (Af) of 1.32 (μref25°C-broth), whereas growth in cottage cheese with cultured cream dressing and in non-heated milk (n=26) resulted in Bf of 1.08 and Af of 1.43 (μref25°C-cottage cheese). Lag phase models were developed by using relative lag times and data from both the present study and from literature. The acceptable simulation zone method showed the developed models to successfully predict growth of psychrotolerant pseudomonads in milk and cottage cheese at both constant and dynamic temperature storage conditions. The developed models can be used to predict growth of psychrotolerant pseudomonads and shelf life of chilled cottage cheese and milk at constant and dynamic storage temperatures. The applied methodology and the developed models seem likely to be applicable for shelf life assessment of other types of products where psychrotolerant pseudomonads are important for spoilage.

Beyond QMRA: Modelling microbial health risk as a complex system using Bayesian networks

BACKGROUND

Quantitative microbial risk assessment (QMRA) is the current method of choice for determining the risk to human health from exposure to microorganisms of concern. However, current approaches are often constrained by the availability of required data, and may not be able to incorporate the many varied factors that influence this risk. Systems models, based on Bayesian networks (BNs), are emerging as an effective complementary approach that overcomes these limitations.

OBJECTIVES

This article aims to provide a comparative evaluation of the capabilities and challenges of current QMRA methods and BN models, and a scoping review of recent published articles that adopt the latter for microbial risk assessment. Pros and cons of systems approaches in this context are distilled and discussed.

METHODS

A search of the peer-reviewed literature revealed 15 articles describing BNs used in the context of QMRAs for foodborne and waterborne pathogens. These studies were analysed in terms of their application, uses and benefits in QMRA.

DISCUSSION

The applications were notable in their diversity. BNs were used to make predictions, for scenario assessment, risk minimisation, to reduce uncertainty and to separate uncertainty and variability. Most studies focused on a segment of the exposure pathway, indicating the broad potential for the method in other QMRA steps. BNs offer a number of useful features to enhance QMRA, including transparency, and the ability to deal with poor quality data and support causal reasoning.

CONCLUSION

The method has significant untapped potential to describe the complex relationships between microbial environmental exposures and health.

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.

Prospective head-to-head study comparing 2 commercial interferon gamma release assays for the diagnosis of tuberculous uveitis

PURPOSE

To perform a head-to-head comparison of 2 commercially available interferon-gamma release assays, QuantiFERON-TB Gold In-Tube (Cellestis, Chadstone, Victoria, Australia) and T-SPOT.TB (Oxford Immunotech, Abingdon, Oxfordshire, UK), in the diagnosis of tuberculous uveitis.

DESIGN

Prospective cohort to study diagnostic accuracy.

METHODS

We recruited consecutive new patients who presented with uveitis to a tertiary institution over a 2-year period. All patients underwent complete ocular examination and systemic evaluation, including T-SPOT.TB, QuantiFERON-TB Gold In-Tube, and tuberculin skin test. Patients were followed-up for a minimum of 1 year after completion of antituberculous therapy where indicated. The main outcome measures were the sensitivity, specificity and accuracy of each test, estimated using Bayesian latent class analysis (presented with 95% Bayesian credible intervals) (Crl). Prior information was obtained from published meta-analyses for diagnostic tests: QuantiFERON Gold In-Tube sensitivity (0.64, 0.59-0.69) and specificity (0.99, 0.99-1.00); T-SPOT. tuberculosis sensitivity (0.50, 0.33-0.67) and specificity (0.91, 0.88-0.93).

RESULTS

From our study in patients with uveitis, QuantiFERON-TB Gold In-Tube was more specific but slightly less sensitive (sensitivity: 0.64, 0.60-0.69; specificity: 0.995, 0.988-0.999) than T-SPOT.TB (sensitivity: 0.67, 0.60-0.74; specificity: 0.91, 0.88-0.93). However, QuantiFERON Gold In-Tube was significantly more accurate in identifying true-positive tuberculous uveitis cases than was T-SPOT.TB among discordant cases (QuantiFERON Gold In-Tube positive 98% vs T-SPOT.TB positive 76%; ratio 1.28, 95% Crl: 1.11-1.72, ie, 95% Crl >1.0, statistically significant).

CONCLUSION

Based on statistical decision theory, our head-to-head study suggests that QuantiFERON-TB Gold In-Tube is the first-line test that should be performed in preference to T-SPOT.TB (and the tuberculin skin test) for diagnosing tuberculous uveitis.

Inferring an augmented Bayesian network to confront a complex quantitative microbial risk assessment model with durability studies: application to Bacillus cereus on a courgette purée production chain

The Monte Carlo (MC) simulation approach is traditionally used in food safety risk assessment to study quantitative microbial risk assessment (QMRA) models. When experimental data are available, performing Bayesian inference is a good alternative approach that allows backward calculation in a stochastic QMRA model to update the experts' knowledge about the microbial dynamics of a given food-borne pathogen. In this article, we propose a complex example where Bayesian inference is applied to a high-dimensional second-order QMRA model. The case study is a farm-to-fork QMRA model considering genetic diversity of Bacillus cereus in a cooked, pasteurized, and chilled courgette purée. Experimental data are Bacillus cereus concentrations measured in packages of courgette purées stored at different time-temperature profiles after pasteurization. To perform a Bayesian inference, we first built an augmented Bayesian network by linking a second-order QMRA model to the available contamination data. We then ran a Markov chain Monte Carlo (MCMC) algorithm to update all the unknown concentrations and unknown quantities of the augmented model. About 25% of the prior beliefs are strongly updated, leading to a reduction in uncertainty. Some updates interestingly question the QMRA model.

Potential application of quantitative microbiological risk assessment techniques to an aseptic-UHT process in the food industry

Aseptic ultra-high-temperature (UHT)-type processed food products (e.g., milk or soup) are ready to eat products which are consumed extensively globally due to a combination of their comparative high quality and long shelf life, with no cold chain or other preservation requirements. Due to the inherent microbial vulnerability of aseptic-UHT product formulations, the safety and stability-related performance objectives (POs) required at the end of the manufacturing process are the most demanding found in the food industry. The key determinants to achieving sterility, and which also differentiates aseptic-UHT from in-pack sterilised products, are the challenges associated with the processes of aseptic filling and sealing. This is a complex process that has traditionally been run using deterministic or empirical process settings. Quantifying the risk of microbial contamination and recontamination along the aseptic-UHT process, using the scientifically based process quantitative microbial risk assessment (QMRA), offers the possibility to improve on the currently tolerable sterility failure rate (i.e., 1 defect per 10,000 units). In addition, benefits of applying QMRA are (i) to implement process settings in a transparent and scientific manner; (ii) to develop a uniform common structure whatever the production line, leading to a harmonisation of these process settings, and; (iii) to bring elements of a cost-benefit analysis of the management measures. The objective of this article is to explore how QMRA techniques and risk management metrics may be applied to aseptic-UHT-type processed food products. In particular, the aseptic-UHT process should benefit from a number of novel mathematical and statistical concepts that have been developed in the field of QMRA. Probabilistic techniques such as Monte Carlo simulation, Bayesian inference and sensitivity analysis, should help in assessing the compliance with safety and stability-related POs set at the end of the manufacturing process. The understanding of aseptic-UHT process contamination will be extended beyond the current "as-low-as-reasonably-achievable" targets to a risk-based framework, through which current sterility performance and future process designs can be optimised.