Estimation of microbial contamination of food from prevalence and concentration data: application to Listeria monocytogenes in fresh vegetables

Plants as a realized niche for Listeria monocytogenes

Listeria monocytogenes is a human pathogen. It is the causative agent of listeriosis, the leading cause of bacterial-linked foodborne mortality in Europe and elsewhere. Outbreaks of listeriosis have been associated with the consumption of fresh produce including vegetables and fruits. In this review we summarize current data providing direct or indirect evidence that plants can serve as habitat for L. monocytogenes, enabling this human pathogen to survive and grow. The current knowledge of the mechanisms involved in the interaction of this bacterium with plants is addressed, and whether this foodborne pathogen elicits an immune response in plants is discussed.

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.

Guidance on date marking and related food information: part 1 (date marking)

A risk-based approach was developed to be followed by food business operators (FBO) when deciding on the type of date marking (i.e. 'best before' date or 'use by' date), setting of shelf-life (i.e. time) and the related information on the label to ensure food safety. The decision on the type of date marking needs to be taken on a product-by-product basis, considering the relevant hazards, product characteristics, processing and storage conditions. The hazard identification is food product-specific and should consider pathogenic microorganisms capable of growing in prepacked temperature-controlled foods under reasonably foreseeable conditions. The intrinsic (e.g. pH and aw), extrinsic (e.g. temperature and gas atmosphere) and implicit (e.g. interactions with competing background microbiota) factors of the food determine which pathogenic and spoilage microorganisms can grow in the food during storage until consumption. A decision tree was developed to assist FBOs in deciding the type of date marking for a certain food product. When setting the shelf-life, the FBO needs to consider reasonably foreseeable conditions of distribution, storage and use of the food. Key steps of a case-by-case procedure to determine and validate the shelf-life period are: (i) identification of the relevant pathogenic/spoilage microorganism and its initial level, (ii) characterisation of the factors of the food affecting the growth behaviour and (iii) assessment of the growth behaviour of the pathogenic/spoilage microorganism in the food product during storage until consumption. Due to the variability between food products and consumer habits, it was not appropriate to present indicative time limits for food donated or marketed past the 'best before' date. Recommendations were provided relating to training activities and support, using 'reasonably foreseeable conditions', collecting time-temperature data during distribution, retail and domestic storage of foods and developing Appropriate Levels of Protection and/or Food Safety Objectives for food-pathogen combinations.

Probabilistic model for estimating Listeria monocytogenes concentration in cooked meat products from presence/absence data

A quantitative probabilistic model was developed to estimate the concentration of Listeria monocytogenes in cooked meat products based on presence/absence data and an assumed zero-inflated distribution, i.e. zero-inflated Poisson (ZIP) or zero-inflated Poisson lognormal (ZIPL) distribution. The performance of these two distributions was compared in two data sets (data set A and B), which represented L. monocytogenes prevalence and concentrations in cooked meat products. In this study, L. monocytogenes contamination data consisted of 4.23% (8/189) and 4.17% (5/120) non-zero counts for data set A and B, respectively. The contamination level of L. monocytogenes, determined by the most probable number (MPN) technique, ranged from 3 to 93 MPN/g among 13 positive samples. The goodness-of-fit test indicated that the ZIPL distribution was better than the simpler ZIP distribution, when L. monocytogenes contamination levels on positive cooked meat samples illustrated large heterogeneity. Results obtained from ZIPL distribution showed that the logarithmic mean value of L. monocytogenes positive samples was 1.5 log MPN/g (log σ = 0.4) for data set A and B. This study provides an alternative probabilistic method when only qualitative data is available in Quantitative microbial risk assessment (QMRA), in particular if pathogen concentrations consist of large numbers of zero counts and represent high variability.

Biopreservation approaches to reduce Listeria monocytogenes in fresh vegetables

Two biopreservation approaches for fresh lettuce, rocket salad, parsley and spinach were studied. The potential of Pediococcus pentosaceus DT016, as a protective culture, to suppress Listeria monocytogenes in vegetables during storage was evaluated. The pathogen numbers in the vegetables inoculated with P. pentosaceus DT016 were significantly (p < 0.01) lower throughout the storage period and, at the last storage day, a minimum difference of 1.4 log CFU/g was reported when compared with the vegetables without the protective culture. Moreover, by using two levels of L. monocytogenes (about 6 and 4 log CFU/g), it was observed that the antagonist effect of P. pentosaceus was higher for the lower pathogen numbers. The second approach evaluated a pediocin DT016 solution to inactivate and control L. monocytogenes proliferation. The pathogen load was studied after washing with: water, chlorine and the pediocin solution and along storage at 4  °C. Comparing the various washing solutions, the vegetables washed with pediocin presented significantly (p < 0.01) lower pathogen numbers throughout storage, by a minimum of 3.2 and 2.7 log CFU/g, than in vegetables washed with water and chlorine, respectively. The proposed methodologies are promising alternatives to maintain the safety of fresh vegetables during extended storage at refrigeration temperature.

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.

Prevalence and Numbers of Listeria monocytogenes in Various Ready-to-Eat Foods over a 5-Year Period in Estonia

HIGHLIGHTS

This study is a significant contribution to L. monocytogenes risk analysis. L. monocytogenes was found in 3.6% of 30,016 analyzed RTE food samples. A food safety criterion of 100 CFU/g was exceeded for 0.3% of the RTE food samples. Salted and cold-smoked fish products were found to be potentially high-risk foods.

Thermal Inactivation of Listeria monocytogenes and Salmonella during Water and Steam Blanching of Vegetables

Thermal inactivation of Listeria monocytogenes and Salmonella was evaluated on peas, spinach, broccoli, potatoes, and carrots that were treated with hot water and steam. One gram-positive bacterium, L. monocytogenes, and one gram-negative bacterium, Salmonella, were selected as pertinent human pathogens for evaluation. Samples were inoculated with a composite of five strains each of L. monocytogenes and Salmonella to achieve approximately 10⁸ to 10⁹ CFU/g. Inoculated samples were treated with hot water at 85 and 87.8°C and with steam at 85 and 96.7°C for up to 3.5 min. A greater than 5-log reduction of L. monocytogenes and Salmonella was achieved on all products within 0.5 min by hot water blanching at 85 and 87.8°C. Steam blanching at 85°C reduced Salmonella populations by greater than 5 log on spinach and peas within 2 min and on carrots and broccoli within 3.5 min. Populations of Salmonella were reduced by more than 5 log within 1 min on carrot, spinach, and broccoli and within 2 min on peas by steam blanching at 96.7°C. Steam blanching at 85°C reduced L. monocytogenes populations by more than 5 log on carrots and spinach within 2 min and on broccoli and peas within 3.5 min. L. monocytogenes populations were reduced more than 5 log within 1 min on carrot, spinach, peas and broccoli by steam blanching at 96.7°C. Longer treatment times and higher temperatures were required for steam-blanched samples than for samples blanched with hot water. Results suggest that hot water and steam blanching practices commonly used by the frozen vegetable industry will achieve the desired 5-log lethality of L. monocytogenes and Salmonella and will enhance microbiological safety prior to freezing.

Fate of Listeria monocytogenes , Pathogenic Yersinia enterocolitica , and Escherichia coli O157:H7 gfp+ in Ready-to-Eat Salad during Cold Storage: What Is the Risk to Consumers?

In this study, we investigated the fate of Listeria monocytogenes , pathogenic Yersinia enterocolitica , and Escherichia coli O157:H7 gfp⁺ inoculated in low numbers into ready-to-eat baby spinach and mixed-ingredient salad (baby spinach with chicken meat). Samples were stored at recommended maximum refrigerator temperature (8°C in Sweden) or at an abuse temperature (15°C) for up to 7 days. Mixed-ingredient salad supported considerable growth when stored at 15°C during shelf life (3 days), with populations of L. monocytogenes , pathogenic Y. enterocolitica , and E. coli O157:H7 gfp⁺ increasing from less than 2.0 log CFU/g on day 0 to 7.0, 4.0, and 5.6 log CFU/g, respectively. However, when mixed-ingredient salad was stored at 8°C during shelf life, only L. monocytogenes increased significantly, reaching 3.0 log CFU/g within 3 days. In plain baby spinach, only pathogenic Y. enterocolitica populations increased significantly during storage for 7 days, and this was exclusively at an abuse temperature (15°C). Thus, mixing ready-to-eat leafy vegetables with chicken meat strongly influenced levels of inoculated strains during storage. To explore the food safety implications of these findings, bacterial numbers were translated into risks of infection by modeling. The risk of listeriosis (measured as probability of infection) was 16 times higher when consuming a mixed-ingredient salad stored at 8°C at the end of shelf life, or 200,000 times higher when stored at 15°C, compared with when consuming it on the day of inoculation. This indicates that efforts should focus on preventing temperature abuse during storage to mitigate the risk of listeriosis. The storage conditions recommended for mixed-ingredient salads in Sweden (maximum 8°C for 3 days) did not prevent growth of L. monocytogenes in baby spinach mixed with chicken meat. Manufacturers preparing these salads should be aware of this, and recommended storage temperature should be revised downwards to reduce the risk of foodborne disease.

Campylobacter QMRA: A Bayesian Estimation of Prevalence and Concentration in Retail Foods Under Clustering and Heavy Censoring

A Bayesian statistical temporal-prevalence-concentration model (TPCM) was built to assess the prevalence and concentration of pathogenic campylobacter species in batches of fresh chicken and turkey meat at retail. The data set was collected from Finnish grocery stores in all the seasons of the year. Observations at low concentration levels are often censored due to the limit of determination of the microbiological methods. This model utilized the potential of Bayesian methods to borrow strength from related samples in order to perform under heavy censoring. In this extreme case the majority of the observed batch-specific concentrations was below the limit of determination. The hierarchical structure was included in the model in order to take into account the within-batch and between-batch variability, which may have a significant impact on the sample outcome depending on the sampling plan. Temporal changes in the prevalence of campylobacter were modeled using a Markovian time series. The proposed model is adaptable for other pathogens if the same type of data set is available. The computation of the model was performed using OpenBUGS software.

Microbiological quality of fresh produce obtained from retail stores on the Eastern Shore of Maryland, United States of America

The aim of this study was to investigate the microbiological quality of six types of fresh produce obtained from three retail stores located on the Eastern Shore of Maryland, USA. A total of 414 samples representing basil, cilantro, lettuce, scallion, spinach, and parsley were analyzed for total aerobic bacteria (APC), total coliforms, Escherichia coli, and three pathogenic bacteria (E. coli O157:H7, Listeria monocytogenes, and Salmonella), using standard methods. Presumptive pathogenic isolates were confirmed using BAX Polymerase Chain Reaction. Total aerobic populations varied widely between samples, while 38.41% were positive for total coliforms and only 10.15% for E. coli. Median abundance (log CFU/g) of total coliforms and E. coli were less than the limit of detection and that of APC ranged from 5.78 to 6.61 over the six produce types. There was a statistically significant difference in prevalence of total coliforms among the retail stores, but not for abundance of APC or prevalence of E. coli. E. coli O157:H7 and L. monocytogenes were detected in one spinach sample each, while one parsley and one cilantro sample were positive for Salmonella. There were no statistically significant differences in microbiological quality among produce types. Although the results of this study provided some indices of sanitary and/or spoilage level, no relationship was observed among the total aerobic bacteria, total coliforms, E. coli, and the presence of pathogenic bacteria in the samples tested.

Quantification of Nonproteolytic Clostridium botulinum Spore Loads in Food Materials

We have produced data and developed analysis to build representations for the concentration of spores of nonproteolytic Clostridium botulinum in materials that are used during the manufacture of minimally processed chilled foods in the United Kingdom. Food materials are categorized into homogenous groups which include meat, fish, shellfish, cereals, fresh plant material, dairy liquid, dairy nonliquid, mushroom and fungi, and dried herbs and spices. Models are constructed in a Bayesian framework and represent a combination of information from a literature survey of spore loads from positive-control experiments that establish a detection limit and from dedicated microbiological tests for real food materials. The detection of nonproteolytic C. botulinum employed an optimized protocol that combines selective enrichment culture with multiplex PCR, and the majority of tests on food materials were negative. Posterior beliefs about spore loads center on a concentration range of 1 to 10 spores kg(-1). Posterior beliefs for larger spore loads were most significant for dried herbs and spices and were most sensitive to the detailed results from control experiments. Probability distributions for spore loads are represented in a convenient form that can be used for numerical analysis and risk assessments.

Growth of Listeria monocytogenes within a caramel-coated apple microenvironment

A 2014 multistate listeriosis outbreak was linked to consumption of caramel-coated apples, an unexpected and previously unreported vehicle for Listeria monocytogenes. This outbreak was unanticipated because both the pH of apples (<4.0) and the water activity of the caramel coating (<0.80) are too low to support Listeria growth. In this study, Granny Smith apples were inoculated with approximately 4 log10 CFU of L. monocytogenes (a cocktail of serotype 4b strains associated with the outbreak) on each apple's skin, stem, and calyx. Half of the apples had sticks inserted into the core, while the remaining apples were left intact. Apples were dipped into hot caramel and stored at either 7°C or 25°C for up to 11 or 28 days, respectively. Data revealed that apples with inserted sticks supported significantly more L. monocytogenes growth than apples without sticks under both storage conditions. Within 3 days at 25°C, L. monocytogenes populations increased >3 log10 in apples with sticks, whereas only a 1-log10 increase was observed even after 1 week for caramel-coated apples without sticks. When stored at 7°C, apples with sticks exhibited an approximately 1.5-log10 increase in L. monocytogenes levels at 28 days, whereas no growth was observed in apples without sticks. We infer that insertion of a stick into the apple accelerates the transfer of juice from the interior of the apple to its surface, creating a microenvironment at the apple-caramel interface where L. monocytogenes can rapidly grow to levels sufficient to cause disease when stored at room temperature.

IMPORTANCE

Neither caramel nor apples are a food where the pathogenic bacterium Listeria monocytogenes should grow, as caramel does not contain enough free water and apples are too acidic. Caramel-coated apples, however, were recently linked to a deadly outbreak of listeriosis. We hypothesized that inserting a stick into the apple releases juice to the interface between the apple and caramel, providing a more hospitable environment than either component alone. To test this hypothesis, apples were inoculated with L. monocytogenes prior to caramel dipping. Some apples had sticks inserted into them before dipping, while others did not. No growth of L. monocytogenes occurred on refrigerated caramel apples without sticks, whereas slow growth was observed on refrigerated caramel apples with sticks. In contrast, significant pathogen growth was observed within 3 days at room temperature on caramel apples with sticks inserted. Food producers should consider interfaces between components within foods as potential niches for pathogen growth.

Prevalence and quantification of pathogenic Vibrio parahaemolyticus during shrimp culture in Thailand

Vibrio parahaemolyticus is a major cause of seafood-borne gastroenteritis. The human pathogenic strains possess tdh or trh or both genes. In Thai shrimp farming, the level of pathogenic V. parahaemolyticus contamination has not been completely characterized, although it has been identified as a risk for people who consume undercooked shrimp. In this study, the prevalence and concentration of V. parahaemolyticus (total Vp) and pathogenic V. parahaemolyticus (tdh+ Vp and trh+ Vp) were investigated during shrimp culture cycles using the most probable number (MPN) method and were confirmed by PCR and the loop-mediated isothermal amplification (LAMP) techniques. The prevalence and concentration of total Vp were high in broodstock and egg samples at the start of the hatchery cycle, but the organism decreased in the subsequent larval and postlarval stages. In contrast, total Vp was low at the beginning of the pond cycle and dramatically increased during the later stages of culture. Broodstock and fresh feed were important sources of V. parahaemolyticus. Numbers of tdh+ Vp and trh+ Vp detected by the LAMP technique were much greater than those detected by the PCR technique, especially in the late stages of the pond cycle. A direct correlation between total Vp and pathogenic Vp was demonstrated only during the harvest stage. This study will be useful as a guideline to establish levels of V. parahaemolyticus presence which can be considered as safe during shrimp culture. In addition, it could be used to identify the source of V. parahaemolyticus, which has recently been reported to be one of the etiologic agents of acute hepatopancreatic necrosis disease.

Impact of microbial count distributions on human health risk estimates

Quantitative microbiological risk assessment (QMRA) is influenced by the choice of the probability distribution used to describe pathogen concentrations, as this may eventually have a large effect on the distribution of doses at exposure. When fitting a probability distribution to microbial enumeration data, several factors may have an impact on the accuracy of that fit. Analysis of the best statistical fits of different distributions alone does not provide a clear indication of the impact in terms of risk estimates. Thus, in this study we focus on the impact of fitting microbial distributions on risk estimates, at two different concentration scenarios and at a range of prevalence levels. By using five different parametric distributions, we investigate whether different characteristics of a good fit are crucial for an accurate risk estimate. Among the factors studied are the importance of accounting for the Poisson randomness in counts, the difference between treating "true" zeroes as such or as censored below a limit of quantification (LOQ) and the importance of making the correct assumption about the underlying distribution of concentrations. By running a simulation experiment with zero-inflated Poisson-lognormal distributed data and an existing QMRA model from retail to consumer level, it was possible to assess the difference between expected risk and the risk estimated with using a lognormal, a zero-inflated lognormal, a Poisson-gamma, a zero-inflated Poisson-gamma and a zero-inflated Poisson-lognormal distribution. We show that the impact of the choice of different probability distributions to describe concentrations at retail on risk estimates is dependent both on concentration and prevalence levels. We also show that the use of an LOQ should be done consciously, especially when zero-inflation is not used. In general, zero-inflation does not necessarily improve the absolute risk estimation, but performance of zero-inflated distributions in QMRA tends to be more robust to changes in prevalence and concentration levels, and to the use of an LOQ to interpret zero values, compared to that of their non-zero-inflated counterparts.

Potential uptake of Escherichia coli O157:H7 and Listeria monocytogenes from growth substrate into leaves of salad plants and basil grown in soil irrigated with contaminated water

Outbreaks of foodborne illness, resulting from the consumption of fresh produce contaminated with human pathogens, are increasing. Potential uptake and persistence of human pathogens within edible parts of consumed fresh vegetables become an important issue in food safety. This study was conducted to assess the potential uptake and internalization of Escherichia coli O157:H7 and Listeria monocytogenes from an autoclaved substrate into edible parts of basil and baby salad plants (lettuce, cultivated rocket, wild rocket and corn salad) from 20 to 60-80days after inoculation, when plants are ready to be harvested and commercialized. Plants were grown in mesocosms under different temperature conditions (24°C and 30°C) and the growing substrate was inoculated using contaminated irrigation water (7logCFU/mL). E. coli O157:H7 could be internalized in the leaves of the tested leafy vegetables through the roots and persist up to the harvesting time with negligible differences between 24°C and 30°C. Significant decreases in pathogen titers were observed over time in the growing substrate on which the plants grew, until the last sampling time. In contrast, L. monocytogenes internalized and persisted only in lettuce mesocosms at 24°C. Neither pathogen was observed in basil leaves. Similarly, in basil growing substrates, enteric bacteria were undetectable at the end of the experiments, suggesting that basil plants may produce and release antimicrobial compounds active against both bacteria in root exudates. These results suggest that enteric bacteria are able to persist within baby salad leaves up to market representing a risk for consumer's health.

An educationally inspired illustration of two-dimensional Quantitative Microbiological Risk Assessment (QMRA) and sensitivity analysis

Quantitative Microbiological Risk Assessment (QMRA) is a structured methodology used to assess the risk involved by ingestion of a pathogen. It applies mathematical models combined with an accurate exploitation of data sets, represented by distributions and - in the case of two-dimensional Monte Carlo simulations - their hyperparameters. This research aims to highlight background information, assumptions and truncations of a two-dimensional QMRA and advanced sensitivity analysis. We believe that such a detailed listing is not always clearly presented in actual risk assessment studies, while it is essential to ensure reliable and realistic simulations and interpretations. As a case-study, we are considering the occurrence of listeriosis in smoked fish products in Belgium during the period 2008-2009, using two-dimensional Monte Carlo and two sensitivity analysis methods (Spearman correlation and Sobol sensitivity indices) to estimate the most relevant factors of the final risk estimate. A risk estimate of 0.018% per consumption of contaminated smoked fish by an immunocompromised person was obtained. The final estimate of listeriosis cases (23) is within the actual reported result obtained for the same period and for the same population. Variability on the final risk estimate is determined by the variability regarding (i) consumer refrigerator temperatures, (ii) the reference growth rate of L. monocytogenes, (iii) the minimum growth temperature of L. monocytogenes and (iv) consumer portion size. Variability regarding the initial contamination level of L. monocytogenes tends to appear as a determinant of risk variability only when the minimum growth temperature is not included in the sensitivity analysis; when it is included the impact regarding the variability on the initial contamination level of L. monocytogenes is disappearing. Uncertainty determinants of the final risk indicated the need of gathering more information on the reference growth rate and the minimum growth temperature of L. monocytogenes. Uncertainty in the dose-response relationship was not included in the analysis, hence the level of its influence cannot be assessed in the present research. Finally, a baseline global workflow for QMRA and sensitivity analysis is proposed.

Proposal of performance objectives and sampling schemes for Listeria monocytogenes in fresh meat intended to be eaten cooked under different storage practices

The goal of this study was to define a practical approach to derive risk management measures, such as performance objectives (POs), for Listeria monocytogenes in pork cuts intended to be eaten cooked. Moreover, sampling plans to verify the compliance of meat lots to such POs are presented. The POs were estimated as prevalence and/or concentration values that should not be exceeded at time of consumption. To derive possible POs for L. monocytogenes, ten lots of pork cuts, collected within the same slaughterhouse along a one-year period, were tested for the presence and concentration of the pathogen under four different scenarios through the product shelf life. Our results indicated that the median values of the prevalence distributions ranged between 0.41 and 0.68. The number of samples to be tested in order to verify lot compliance ranged between six, for samples tested immediately after packaging, and three, for samples tested at the end of the shelf life. The concentration values ranged between 2.02 log10 CFU/g, for samples tested immediately after packaging, up to 3.14 log10 CFU/g for samples tested after final storage at 14°C. The concentration of L. monocytogenes in the samples contaminated by less than 10 CFU/g was estimated between 7 CFU/10g to 7 CFU/g, after storage at retail and abuse temperature, respectively. Basing on the knowledge of log normal distributions, the maximum contamination level of the lots in order to achieve the suggested POs, was calculated. It was obtained that mean concentration estimated as PO should be between -0.43 and 0.48 log10 CFU/g. Other risk management options are further evaluated and discussed. These results would help food operators and authorities to establish safety targets and corrective actions regarding inhibition of L. monocytogenes in fresh pork meat.

Efficacy of household washing treatments for the control of Listeria monocytogenes on salad vegetables

The efficacy of household decontamination methods at reducing Listeria monocytogenes on fresh lettuce (Lactuca sativa), cucumber (Cucumis sativus) and parsley (Petroselinum sativum) was studied. Inoculated vegetable pieces were immersed in washing solutions and surviving L. monocytogenes enumerated. Parameters investigated were storage temperature prior to washing, dipping water temperature, agitation, acetic acid concentration and immersion time. The results indicated that the storage temperature significantly affects the efficacy of dipping vegetables in water for the control of L. monocytogenes, as the reduction in count was greatest when products had been stored at cooler temperatures. Decontamination with acetic acid (up to 2.0% v/v) was shown to have some effect in most cases, but the highest observed decrease in count was 2.6 log cfu/g. Experiments investigating the effect of exposure time to acetic acid (0.5% and 1.0% v/v, up to 30 min immersion) indicated that immersing the vegetables for more than 10 min is of minimal benefit. The most significant factor affecting washing and decontamination efficacy was the vegetable itself: L. monocytogenes colonizing cucumber epidermis was far more resistant to removal by washing and to acid treatment than that on the leafy vegetables, and L. monocytogenes on parsley was the most susceptible. This shows that published decontamination experiments (often performed with lettuce) cannot necessarily be extrapolated to other vegetables.

Quick and simple estimation of bacteria using a fluorescent paracetamol dimer-Au nanoparticle composite

Rapid, simple and sensitive detection of bacterial contamination is critical for safeguarding public health and the environment. Herein, we report an easy method of detection as well as enumeration of the bacterial cell number on the basis of fluorescence quenching of a non-antibacterial fluorescent nanocomposite, consisting of paracetamol dimer (PD) and Au nanoparticles (NPs), in the presence of bacteria. The composite was synthesized by reaction of paracetamol (p-hydroxyacetanilide) with HAuCl(4). The Au NPs of the composite were characterized using UV-Vis spectroscopy, transmission electron microscopy (TEM), X-ray diffraction and selected area electron diffraction analysis. The paracetamol dimer in the composite showed emission peak at 435 nm when excited at 320 nm. The method successfully detected six bacterial strains with a sensitivity of 100 CFU mL(-1). The gram-positive and gram-negative bacteria quenched the fluorescence of the composite differently, making it possible to distinguish between the two. The TEM analysis showed interaction of the composite with bacteria without any apparent damage to the bacteria. The chi-square test established the accuracy of the method. Quick, non-specific and highly sensitive detection of bacteria over a broad range of logarithmic dilutions within a short span of time demonstrates the potential of this method as an alternative to conventional methods.

Hierarchical Bayesian models to assess between- and within-batch variability of pathogen contamination in food

Assessing within-batch and between-batch variability is of major interest for risk assessors and risk managers in the context of microbiological contamination of food. For example, the ratio between the within-batch variability and the between-batch variability has a large impact on the results of a sampling plan. Here, we designed hierarchical Bayesian models to represent such variability. Compatible priors were built mathematically to obtain sound model comparisons. A numeric criterion is proposed to assess the contamination structure comparing the ability of the models to replicate grouped data at the batch level using a posterior predictive loss approach. Models were applied to two case studies: contamination by Listeria monocytogenes of pork breast used to produce diced bacon and contamination by the same microorganism on cold smoked salmon at the end of the process. In the first case study, a contamination structure clearly exists and is located at the batch level, that is, between batches variability is relatively strong, whereas in the second a structure also exists but is less marked.

Prevalence of foodborne pathogens in grilled chicken from street vendors and retail outlets in Reynosa, Tamaulipas, Mexico

We analyzed a total of 70 grilled chicken samples bought randomly from street vendors and retail outlets in the city of Reynosa, Mexico, to determine the prevalence of Escherichia coli (Shiga toxin producing and enterotoxin producing), Salmonella spp., Staphylococcus aureus, Listeria spp., and Campylobacter spp. using microbiological methods and PCR detection of bacterial sequences. Of the 70 samples, 27 (38.5%) were from retail outlets and 43 (61.4%) from street vendors. All specimens were negative by both microbiological and molecular methods for Listeria monocytogenes, Shiga toxin 2 of Shiga toxin-producing E. coli, lt of enterotoxin-producing E. coli, and st enterotoxin, and all were negative for Salmonella spp. and Campylobacter jejuni by PCR. Of the samples studied, 49 (70%) had undetectable levels of the foodborne pathogens studied with the methods used. In the remaining 21 (30%) specimens, at least one pathogen was isolated or detected, with E. coli being the pathogen most frequently isolated and with two samples bearing the hlyA gene. We found no statistical difference in bacterial prevalence between retail and street vendor samples. The presence of pathogens in grilled chicken is an important public health risk because of the great demand for and daily consumption of this product in this region.

Prevalence and characterization of antimicrobial resistance of foodborne Listeria monocytogenes isolates in Hebei province of Northern China, 2005-2007

A total of 2177 food samples collected from nine cities in northern China during 2005 to 2007 were screened for the presence of Listeria monocytogenes. All L. monocytogenes isolates were subjected to serotyping, antimicrobial susceptibility, pulsed-field gel electrophoresis (PFGE), as well as PCR screening to identify genes responsible for tetracycline resistance [tet(L), tet(M), tet(K), tet(S) and tet(B)], transposon Tn916, and class 1 integron. Contamination with L. monocytogenes was detected in 4.13% (90/2177) of the total samples representing various food products. The pathogen was mainly isolated from frozen food made of wheat flour or rice products (26/252, 10.32%) and raw meat products (46/733, 6.28%). Besides, 3.31% (10/302) of cooked meat, 1.17% (4/343) of seafood, 0.98% (2/204) of non-fermented bean products and 0.62% (2/323) of vegetables samples were contaminated by this bacterium. The L. monocytogenes isolates belonged to five serotypes (1/2a, 1/2b, 1/2c, 4b, and 3a), with serotype 1/2a being dominant (48.88%). Antimicrobial resistance was most frequently observed for ciprofloxacin (17.8%), tetracycline (15.6%) and streptomycin (12.2%). Overall, resistance was observed against 14 out of 18 antimicrobials tested while multiple resistances occurred among 18.9% (17/90) isolates. Interestingly, two isolates were resistant to more than five antimicrobials. Among 14 tetracycline-resistant isolates, 13 carried tet(M) gene including nine possessing Tn916, and one harbored tet(S) gene. PFGE analysis revealed genetic heterogeneity among individual serotypes as well as scattered occurrence of some genotypes without any clear-cut correlation to source or food type. The widespread distribution of epidemiologically important serotypes (1/2a, 1/2b and 4b) of L. monocytogenes, and their resistance to commonly used antibiotics indicate a potential public health risk. Our data also indicate that L. monocytogenes could act as a reservoir of mobile tet genes along the food chain.

A probabilistic approach to determine thermal process setting parameters: application for commercial sterility of products

The objective of the probabilistic data analysis presented in this paper was to enable the thermal process to be set on actual data rather than on generic or conservative rules. The application was an ambient stable soup product, heated in a continuous UHT line. The data set comes from a decade of microbiological analysis: initial spore load and survival spore concentration after moderate heat-treatment (100°C for 15 min and 110°C for 15 min) have been enumerated in forty eight ingredients. The probabilistic analysis was carried out within a risk-based context, considering a Performance Objective, PO, set after the heat-treatment process and an initial spore contamination (H₀) at the ingredient mixing step. The probabilistic analysis was based upon Bayesian inference, chosen for its flexibility when dealing with censored data (some values were reported as less than 1 log cfu/g) and also for its ability to incorporate in the data analysis prior information. Beforehand, Z values around 10°C for aerobic bacterial spores, and log count error around 1 log, were assumed. The methodology and the results are reported using two ingredients (garlic and milk powder) illustrating the 'not detected' (censored data) issue and also the inter-ingredient variability. Indeed, Z was estimated to be 13.6°C (mean) for spores selected from garlic and 5.9°C for those selected from milk powder. Based upon a hypothetical soup recipe with these two ingredients, the sterilization value was estimated to be 13 min (95th percentile). The potential use of similar methodology to design and set the sterilization value for the thermal process of future products, is discussed.

Modeling prevalence and counts from most probable number in a bayesian framework: an application to Salmonella typhimurium in fresh pork sausages

Prevalence and counts of Salmonella Typhimurium in fresh pork sausage packs at the point of retail were modeled by using Irish and United Kingdom retail surveys' data. A methodology for modeling a second-order distribution for the initial Salmonella concentration (lambda0) in pork sausage at retail was presented considering the uncertainty originated from the most probable-number (MPN) serial dilutions. A conditional probability of observing the tube counts given true Salmonella concentration in a contaminated pack was built from the MPN triplets of every sausage tested. A posterior distribution was then modeled under the assumption that the counts from each of the portions of sausage mix stuffed into casings (and subsequently packed) are Poisson distributed. In order to model the variability of lambda0 among contaminated sausage packs, MPN uncertainties were propagated to a predefined lognormal distribution. Because the sausage samples from the Irish survey were frozen prior to MPN analysis (which is expected to cause reduction in viable cells), the resulting distribution for lambda0 appeared greatly underestimated (mean: 0.514 CFU/g; 95% confidence interval [CI]: 0.02 to 2.74 CFU/g). The lambda0 distribution produced with the United Kingdom survey data (mean: 69.7 CFU/g; 95% CI: 15 to 200 CFU/g) was, however, more conservative, and is to be used along with the fitted distribution for prevalence of Salmonella Typhimurium in pork sausage packs in Ireland (gamma[37.997, 0.0013]; mean: 0.046; 95% CI: 0.032 to 0.064) as the main inputs of a stochastic consumer-phase exposure assessment model.

Modeling logistic performance in quantitative microbial risk assessment

In quantitative microbial risk assessment (QMRA), food safety in the food chain is modeled and simulated. In general, prevalences, concentrations, and numbers of microorganisms in media are investigated in the different steps from farm to fork. The underlying rates and conditions (such as storage times, temperatures, gas conditions, and their distributions) are determined. However, the logistic chain with its queues (storages, shelves) and mechanisms for ordering products is usually not taken into account. As a consequence, storage times-mutually dependent in successive steps in the chain-cannot be described adequately. This may have a great impact on the tails of risk distributions. Because food safety risks are generally very small, it is crucial to model the tails of (underlying) distributions as accurately as possible. Logistic performance can be modeled by describing the underlying planning and scheduling mechanisms in discrete-event modeling. This is common practice in operations research, specifically in supply chain management. In this article, we present the application of discrete-event modeling in the context of a QMRA for Listeria monocytogenes in fresh-cut iceberg lettuce. We show the potential value of discrete-event modeling in QMRA by calculating logistic interventions (modifications in the logistic chain) and determining their significance with respect to food safety.

Listeria monocytogenes isolated from vegetable salads sold at supermarkets in Santiago, Chile: prevalence and strain characterization

Between 2000 and 2005, 717 samples of three types of salads were analysed for Listeria monocytogenes in Santiago, Chile in order to provide information to Chilean health authorities on the presence of the pathogen in vegetable salad samples and to ascertain the risk of these products for consumers. L. monocytogenes isolates were found in 88 out of 347 (25.4%) samples of frozen vegetable salads and in 22 out of 216 (10.2%) freshly supermarkets prepared, cooked or raw ready-to-eat vegetable salads; no Listeria was isolated from 154 samples of raw minimally processed salads industrially prepared. Enumeration of L. monocytogenes was done by plate count for 20 positive frozen samples, randomly chosen. Most of them (90%) had < 10 cfu/g. MPN technique was performed for 34 another positive samples; 12 had > or = 1100/g, five ranged between 240 and 93, eight between 23 and three and nine had < 3.0. No L. monocytogenes was recovered after cooking 12 contaminated frozen samples. Isolation of strains was done using three selective agars. Sixty-two L. monocytogenes were isolated from lithium chloride phenylethanol moxalactam agar, 95 from Listeria selective agar Oxford formulation, and 103 from polymixin acriflavine lithium chloride ceftazidime aesculin mannitol agar. Fifty isolates (45.5%) belong to PCR group IIb (including strains serovar 1/2b), 41 (37.3%) to PCR group IVb (including strains serovar 4b), 17 (15.5%) to PCR group IIa (including strains serovar 1/2a), and 2 (1.8%) to PCR group IIc. With the use of DNA macrorestriction patterns analysis, 17 different clusters were detected among 71 isolates, with P10, the most frequent with 25 isolates (35.2%) of PCR group IIb.

Development of a hierarchical Bayesian model to estimate the growth parameters of Listeria monocytogenes in minimally processed fresh leafy salads

The optimal growth rate mu(opt) of Listeria monocytogenes in minimally processed (MP) fresh leafy salads was estimated with a hierarchical Bayesian model at (mean+/-standard deviation) 0.33+/-0.16 h(-1). This mu(opt) value was much lower on average than that in nutrient broth, liquid dairy, meat and seafood products (0.7-1.3 h(-1)), and of the same order of magnitude as in cheese. Cardinal temperatures T(min), T(opt) and T(max) were determined at -4.5+/-1.3 degrees C, 37.1+/-1.3 degrees C and 45.4+/-1.2 degrees C respectively. These parameters were determined from 206 growth curves of L. monocytogenes in MP fresh leafy salads (lettuce including iceberg lettuce, broad leaf endive, curly leaf endive, lamb's lettuce, and mixtures of them) selected in the scientific literature and in technical reports. The adequacy of the model was evaluated by comparing observed data (bacterial concentrations at each experimental time for the completion of the 206 growth curves, mean log(10) increase at selected times and temperatures, L. monocytogenes concentrations in naturally contaminated MP iceberg lettuce) with the distribution of the predicted data generated by the model. The sensitivity of the model to assumptions about the prior values also was tested. The observed values mostly fell into the 95% credible interval of the distribution of predicted values. The mu(opt) and its uncertainty determined in this work could be used in quantitative microbial risk assessment for L. monocytogenes in minimally processed fresh leafy salads.