Quantitative microbial risk assessment of Salmonella in fresh chicken patties.

Quantitative microbial risk assessment (QMRA) has witnessed rapid development within the context of food safety in recent years. As a means of contributing to these advancements, a QMRA for Salmonella spp. in fresh chicken patties for the general European Union (EU) population was developed. A two-dimensional (Second Order) Monte-Carlo simulation method was used for separating variability and uncertainty of model's parameters. The stages of industrial processing, retail storage, domestic storage, and cooking in the domestic environment were considered in the exposure assessment. For hazard characterization, a dose-response model was developed by combining 8 published dose-response models using a Pert distribution for describing uncertainty. The QMRA model predicted a mean probability of illness of 1.19*10-4 (5.28*10-5 - 3.57*10-4 95 % C.I.), and a mean annual number of illnesses per 100,000 people of 2.13 (0.96 - 6.59 95 % C.I.). Moreover, sensitivity analysis was performed, and variability in cooking preferences was found to be the most influential model parameter (r = -0.39), followed by dose-response related variability (r = 0.22), and variability in the concentration of Salmonella spp. at the time of introduction at the processing facility (r = 0.11). Various mitigation strategy scenarios were tested, from which, "increasing the internal temperature of cooking" and "decreasing shelf life" were estimated to be the most effective in reducing the predicted risk of illness. Salmonella-related illnesses exhibit particularly high severity, making them some of the most prominent zoonotic diseases in the EU. Regular monitoring of this hazard in order to further highlight its related parameters and causes is a necessary procedure. This study not only provides an updated assessment of Salmonella spp. risk associated with chicken patties, but also facilitates the identification of crucial targets for scientific investigation and implementation of real-world intervention strategies.

Development and validation of a mathematical model for pseudomonads growth as a basis for predicting spoilage of fresh poultry breast and thigh fillets.

The growth of naturally contaminated pseudomonads on fresh breast and thigh poultry fillets during aerobic storage was studied and modeled as a function of temperature (0-30°C). A statistical comparison of the models for breast and thigh fillets showed that muscle type does not significantly affect the temperature dependence of pseudomonads growth kinetics. A unified model for breast and thigh was developed and validated against pseudomonads growth rate data under isothermal conditions extracted from literature and experimental data under dynamic temperature conditions. The validation results showed a satisfactory performance of the model with the bias and accuracy factors ranging from 0.85 to 1.09 and 1.02 to 1.21, respectively. The model was further used to predict the shelf life of fresh poultry as the time required by pseudomonads to reach the spoilage level for various scenarios of temperature, initial contamination level, and physiological state of pseudomonads demonstrating its application in a risk-based shelf-life assessment of fresh poultry products.

Quantitative risk assessment of Listeria monocytogenes in ready-to-eat (RTE) cooked meat products sliced at retail stores in Greece.

A quantitative microbial risk assessment (QMRA) model predicting the listeriosis risk related to the consumption of Ready- To- Eat (RTE) cooked meat products sliced at retail stores in Greece was developed. The probability of illness per serving assessed for 87 products available in the Greek market was found highly related to the nitrite concentration; products having a lower concentration showed a higher risk per serving. The predicted 95th percentiles of the annual listeriosis cases totaled 33 of which 13 cases were <65 years old and 20 cases ≥65 years old. The highest number of cases was predicted for mortadella, smoked turkey, boiled turkey and parizer, which were the most frequently consumed product categories. Two scenarios for assessing potential interventions to reduce the risk were tested: setting a use-by date of 14 days (these products have no use-by date based on current European Union legislation) and improving the temperature control during domestic storage. The two scenarios resulted in a decrease of the 95th and 99th percentiles of the total annual cases by 97% and 88%, respectively.

Variability in microbial inactivation: From deterministic Bigelow model to probability distribution of single cell inactivation times.

Phenotypic heterogeneity seems to be an important component leading to biological individuality and is of great importance in the case of microbial inactivation. Bacterial cells are characterized by their own resistance to stresses. This inherent stochasticity is reflected in microbial survival curve which, in this context, can be considered as cumulative probability distribution of lethal events. The objective of the present study was to present an overview on the assessment and quantification of variability in microbial inactivation originating from single cells and discuss this heterogeneity in the context of predicting microbial behavior and Risk assessment studies. The detailed knowledge of the distribution of the single cells' inactivation times can be the basis for stochastic inactivation models which, in turn, may be employed in a risk - based food safety approach.

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.

Simultaneous growth, survival and death: The trimodal behavior of Salmonella cells under osmotic stress giving rise to "Phoenix phenomenon".

Time-lapse microscopy methods were used to monitor growth, survival and death of Salmonella enterica serotype Agona individual cells on solid laboratory medium (tryptone soy agar) in the presence of various salt concentrations (0.5%, 3.5%, 4.5% and 5.7% NaCl). The results showed a highly heterogeneous behavior. As NaCl concentration increased, the distribution of the first division time was shifted to higher values and became wider. The mean first division time increased from 1.8 h at 0.5% NaCl to 5.48 h, 16.2 h, and 35.9 h at 3.5%, 4.5% and 5.7% NaCl, respectively. The concentration of NaCl in the growth medium also affected the ability of the cells to divide. The percentage of cells able to grow decreased from 88.9% at 0.5% NaCl to 66.5%, 32.8%, and 6.9% at 3.5%, 4.5% and 5.7% NaCl, respectively. In the latter case (5.7% NaCl), 74 cells out of 406 cells tested (18%) died with mean time to death 5.03 h and standard deviation 6.70 h. To investigate the effect of the behavior of individual cells on the dynamics of the whole population, simulation analysis was used. The simulation results showed that the simultaneous growth, survival and death of cells observed under osmotic stress can lead to a total population behavior known as the "Phoenix" phenomenon. The simulation findings were confirmed by validation experiments using both viable counts and time lapse microscopy. The results of the present study show the high heterogeneity of individual cell responses and the complexity in the behavior of microbial populations at conditions approaching the boundaries of growth.

Image analysis driven single-cell analytics for systems microbiology.

BACKGROUND: Time-lapse microscopy is an essential tool for capturing and correlating bacterial morphology and gene expression dynamics at single-cell resolution. However state-of-the-art computational methods are limited in terms of the complexity of cell movies that they can analyze and lack of automation. The proposed Bacterial image analysis driven Single Cell Analytics (BaSCA) computational pipeline addresses these limitations thus enabling high throughput systems microbiology. RESULTS: BaSCA can segment and track multiple bacterial colonies and single-cells, as they grow and divide over time (cell segmentation and lineage tree construction) to give rise to dense communities with thousands of interacting cells in the field of view. It combines advanced image processing and machine learning methods to deliver very accurate bacterial cell segmentation and tracking (F-measure over 95%) even when processing images of imperfect quality with several overcrowded colonies in the field of view. In addition, BaSCA extracts on the fly a plethora of single-cell properties, which get organized into a database summarizing the analysis of the cell movie. We present alternative ways to analyze and visually explore the spatiotemporal evolution of single-cell properties in order to understand trends and epigenetic effects across cell generations. The robustness of BaSCA is demonstrated across different imaging modalities and microscopy types. CONCLUSIONS: BaSCA can be used to analyze accurately and efficiently cell movies both at a high resolution (single-cell level) and at a large scale (communities with many dense colonies) as needed to shed light on e.g. how bacterial community effects and epigenetic information transfer play a role on important phenomena for human health, such as biofilm formation, persisters' emergence etc. Moreover, it enables studying the role of single-cell stochasticity without losing sight of community effects that may drive it.

Individual cell heterogeneity in Predictive Food Microbiology: Challenges in predicting a "noisy" world.

Gene expression is a fundamentally noisy process giving rise to a significant cell to cell variability at the phenotype level. The phenotypic noise is manifested in a wide range of microbial traits. Heterogeneous behavior of individual cells is observed at the growth, survival and inactivation responses and should be taken into account in the context of Predictive Food Microbiology (PMF). Recent methodological advances can be employed for the study and modeling of single cell dynamics leading to a new generation of mechanistic models which can provide insight into the link between phenotype, gene-expression, protein and metabolic functional units at the single cell level. Such models however, need to deal with an enormous amount of interactions and processes that influence each other, forming an extremely complex system. In this review paper, we discuss the importance of noise and present the future challenges in predicting the "noisy" microbial responses in foods.

Individual cell heterogeneity as variability source in population dynamics of microbial inactivation.

A statistical modeling approach was applied for describing and evaluating the individual cell heterogeneity as variability source in microbial inactivation. The inactivation data (Nt vs time) of Salmonella enterica serotype Agona, with initial concentration N0 = 10(9) CFU/ml in acidified tryptone soy broth (pH 3.5), were transformed to (N0 - Nt)/N0 vs time leading to the cumulative probability distribution of the individual cell inactivation times (ti), which was further fitted to a variety of continuous distributions using @Risk software. The best-fitted ti distribution (Gamma) was used to predict the inactivation of S. Agona populations of various N0 using Monte Carlo simulation, with the number of iterations in each simulation being equal to N0 and the number of simulations representing the variability of the population inactivation behavior. The Monte Carlo simulation results for a population with N0 = 10,000 CFU/ml showed that the variability in the predicted inactivation behavior is negligible for concentrations down to 100 cells. As the concentration decreases below 100 cells, however, the variability increases significantly. The results also indicated that the D-value used in deterministic first order kinetic models is valid only for large populations. For small populations, D-value shows a high variability, originating from individual cell heterogeneity, and, thus, can be better characterized by a probability distribution rather than a uniform value. Validation experiments with small populations confirmed the variability predicted by the statistical model. The use of the proposed approach to quantify the variability in the inactivation of mixed microbial populations, consisting of subpopulations with different probability distributions of ti, was also demonstrated.