Use of simulation tools to illustrate the effect of data management practices for low and negative plate counts on the estimated parameters of microbial reduction models

Fate of foodborne pathogens during soaking and drying of walnuts

Walnuts are among the most popular tree nuts that are soaked at home. Recipes for preparing soaked walnut kernels from online blogs (n = 71) and YouTube videos (n = 29) were reviewed to identify typical consumer handling practices that were then used to determine the fate of foodborne pathogens during soaking and subsequent drying of walnut kernels. Individual five-strain cocktails of rifampin-resistant Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella, grown on agar plates and diluted in water, were inoculated onto walnuts and then dried. Inoculated walnuts were added to sterile water at a ratio of 1:4 (w/v), held at 15, 18, or 22°C for up to 24 h, and then dried at 64°C for up to 24 h (for Salmonella-inoculated walnuts). Pathogen populations during soaking and drying were enumerated on tryptic soy agar with rifampin and on CHROM agar. Initial walnut moisture was ∼4%, increased to ∼30% at 8 and 24 h of soaking and then decreased during drying to ∼4% at 6 h and <1% after 24 h. Initial E. coli, L. monocytogenes, and Salmonella populations were ∼1.0, ∼1.5, and 1.0-2.5 log CFU/g, respectively, after inoculation and drying. No significant (P > 0.05) increase in populations was observed after 24 h at 15 and 18°C or after 12 h at 22°C. Significant increases of 1.9-3.0, 1.2-2.1, and 1.8 log CFU/g for E. coli, L. monocytogenes, and Salmonella, respectively, were observed after 24 h of soaking at 22°C. Growth rates of 0.19, 0.093, and 0.16 log CFU/sample per h, respectively, were observed. Lag times of 8.8 and 11 h at 22°C were determined for E. coli and Salmonella, respectively. Populations of Salmonella declined by 1.04 log CFU/g over 12 h of drying; further significant (P < 0.05) decreases were not observed at 24 h. To limit food safety risks in soaked walnuts, educational materials should emphasize sourcing treated walnuts, kitchen sanitation, hygiene measures, and soaking at cooler temperatures or for shorter times at ambient temperatures.

Interlaboratory Evaluation of Enterococcus faecium NRRL B-2354 as a Salmonella Surrogate for Validating Thermal Treatment of Multiple Low-Moisture Foods

ABSTRACT

This multi-institutional study assessed the efficacy of Enterococcus faecium NRRL B-2354 as a nonpathogenic Salmonella surrogate for thermal processing of nonfat dry milk powder, peanut butter, almond meal, wheat flour, ground black pepper, and date paste. Each product was analyzed by two laboratories (five independent laboratories total), with the lead laboratory inoculating (E. faecium or a five-strain Salmonella enterica serovar cocktail of Agona, Reading, Tennessee, Mbandaka, and Montevideo) and equilibrating the product to the target water activity before shipping. Both laboratories subjected samples to three isothermal treatments (between 65 and 100°C). A log-linear and Bigelow model was fit to survivor data via one-step regression. On the basis of D80°C values estimated from the combined model, E. faecium was more thermally resistant (P < 0.05) than Salmonella in nonfat dry milk powder (DEf-80°C, 100.2 ± 5.8 min; DSal-80°C, 28.9 ± 1.0 min), peanut butter (DEf-80°C, 133.5 ± 3.1 min; DSal-80°C, 57.6 ± 1.5 min), almond meal (DEf-80°C, 34.2 ± 0.4 min; DSal-80°C, 26.1 ± 0.2 min), ground black pepper (DEf-80°C, 3.2 ± 0.8 min; DSal-80°C, 1.5 ± 0.1 min), and date paste (DEf-80°C, 1.5 ± 0.0 min; DSal-80°C, 0.5 ± 0.0 min). Although the combined laboratory D80°C for E. faecium was lower (P < 0.05) than for Salmonella in wheat flour (DEf-80°C, 9.4 ± 0.1 min; DSal-80°C, 10.1 ± 0.2 min), the difference was ∼7%. The zT values for Salmonella in all products and for E. faecium in milk powder, almond meal, and date paste were not different (P > 0.05) between laboratories. Therefore, this study demonstrated the impact of standardized methodologies on repeatability of microbial inactivation results. Overall, E. faecium NRRL B-2354 was more thermally resistant than Salmonella, which provides support for utilizing E. faecium as a surrogate for validating thermal processing of multiple low-moisture products. However, product composition should always be considered before making that decision.

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The Most Probable Curve method - A robust approach to estimate kinetic models from low plate count data resulting in reduced uncertainty

A novel method is proposed for fitting microbial inactivation models to data on liquid media: the Most Probable Curve (MPC) method. It is a multilevel model that makes a separation between the "true" microbial concentration according to the model, the "actual" concentration in the media considering chance, and the actual counts on the plate. It is based on the assumptions that stress resistance is homogeneous within a microbial population, and that there is no aggregation of microbial cells. Under these assumptions, the number of colonies in/on a plate follows a Poisson distribution with expected value depending on the proposed kinetic model, the number of dilutions and the plated volume. The novel method is compared against (non)linear regression based on a normal likelihood distribution (traditional method), Poisson regression and gamma-Poisson regression using data on the inactivation of Listeria monocytogenes. The conclusion is that the traditional method has limitations when the data includes plates with low (or zero) cell counts, which can be mitigated using more complex (discrete) likelihoods. However, Poisson regression uses an unrealistic likelihood function, making it unsuitable for survivor curves with several log-reductions. Gamma-Poisson regression uses a more realistic likelihood function, even though it is based mostly on empirical hypotheses. We conclude that the MPC method can be used reliably, especially when the data includes plates with low or zero counts. Furthermore, it generates a more realistic description of uncertainty, integrating the contribution of the plating error and reducing the uncertainty of the primary model parameters. Consequently, although it increases modelling complexity, the MPC method can be of great interest in predictive microbiology, especially in studies focused on variability analysis.

Guidance on validation of lethal control measures for foodborne pathogens in foods

Food manufacturers are required to obtain scientific and technical evidence that a control measure or combination of control measures is capable of reducing a significant hazard to an acceptable level that does not pose a public health risk under normal conditions of distribution and storage. A validation study provides evidence that a control measure is capable of controlling the identified hazard under a worst-case scenario for process and product parameters tested. It also defines the critical parameters that must be controlled, monitored, and verified during processing. This review document is intended as guidance for the food industry to support appropriate validation studies, and aims to limit methodological discrepancies in validation studies that can occur among food safety professionals, consultants, and third-party laboratories. The document describes product and process factors that are essential when designing a validation study, and gives selection criteria for identifying an appropriate target pathogen or surrogate organism for a food product and process validation. Guidance is provided for approaches to evaluate available microbiological data for the target pathogen or surrogate organism in the product type of interest that can serve as part of the weight of evidence to support a validation study. The document intends to help food manufacturers, processors, and food safety professionals to better understand, plan, and perform validation studies by offering an overview of the choices and key technical elements of a validation plan, the necessary preparations including assembling the validation team and establishing prerequisite programs, and the elements of a validation report.

Thermal Resistance of Foodborne Pathogens and Enterococcus faecium NRRL B-2354 on Inoculated Pistachios

ABSTRACT

Process control validations require knowledge of the resistance of the pathogen(s) of concern to the target treatment and, in some cases, the relative resistance of surrogate organisms. Selected strains of Escherichia coli O157:H7 (five strains), Listeria monocytogenes (five strains), and Salmonella enterica (five strains) as well as Salmonella Enteritidis phage type (PT) 30 and nonpathogenic Enterococcus faecium NRRL B-2354 were inoculated separately (as individual strains) onto inshell pistachios. The thermal tolerance of each strain was compared via treatment of inoculated pistachios to hot oil (121°C) or hot water (80°C) for 1 min. Survivor curves in hot oil or hot water (0.5 to 6 min, n = 6 to 15) were determined for one or two of the most resistant strains of each pathogen, as well as E. faecium NRRL B-2354 and Salmonella Enteritidis PT 30, and the Weibull model was fit to the data. A pilot-scale air-impingement oven was used to compare the thermal tolerance of E. faecium NRRL B-2354 and Salmonella Enteritidis PT 30 on pistachios with or without a brining pretreatment and at either dry (no steam) or 30% humidity (v/v) oven conditions. No significant difference in the time to a 4-log reduction in hot oil or hot water was predicted for any of the strains evaluated, on the basis of the 95% confidence interval. In the pilot-scale oven, E. faecium NRRL B-2354 was more thermally resistant than Salmonella in a broad set of differing treatments, treatment times, and temperatures. Salmonella is a suitable target pathogen of concern in pistachios for thermal processes because no other pathogen tested was more thermally resistant under the conditions evaluated. E. faecium NRRL B-2354 was at least as thermally resistant as Salmonella under all conditions evaluated, making it a good potential surrogate for Salmonella on pistachios.

HIGHLIGHTS

Multilevel modelling as a tool to include variability and uncertainty in quantitative microbiology and risk assessment. Thermal inactivation of Listeria monocytogenes as proof of concept

Variability is inherent in biology and also substantial for microbial populations. In the context of food safety risk assessment, it refers to differences in the response of different bacterial strains (between-strain variability) and different cells (within-strain variability) to the same condition (e.g. inactivation treatment). However, its quantification based on empirical observations and its incorporation in predictive models is a challenge for both experimental design and (statistical) analysis. In this article we propose the use of multilevel models to quantify (different levels of) variability and uncertainty and include them in the predictions. As proof of concept, we analyse the microbial inactivation of Listeria monocytogenes to thermal treatments including different levels of variability (between-strain and within-strain) and uncertainty. The relationship between the microbial count and time was expressed using a (non-linear) Weibullian model. Moreover, we defined stochastic hypotheses to describe the different types of variation at the level of the kinetic parameters, as well as in the observations (microbial counts). The model parameters (kinetic parameters and variances) are estimated using Bayesian statistics. The multilevel approach was compared against an analogous, single-level model. The multilevel methodology shrinks extreme parameter estimates towards the mean according to uncertainty, thus mitigating overfitting. In addition, this approach enables to easily incorporate different levels of variation (between-strain and/or within-strain variability and/or uncertainty) in the predictions. On the other hand, multilevel (Bayesian) models are more complex to define, implement, analyse and communicate than single-level models. Nevertheless, their ability to incorporate different sources of variability in predictions make them very suitable for Quantitative Microbial Risk Assessment.

Guidelines for the design of (optimal) isothermal inactivation experiments

Kinetic models are nowadays a basic tool to ensure food safety. Most models used in predictive microbiology have model parameters, whose precision is crucial to provide meaningful predictions. Kinetic parameters are usually estimated based on experimental data, where the experimental design can have a great impact on the precision of the estimates. In this sense, Optimal Experiment Design (OED) applies tools from optimization and information theory to identify the most informative experiment under a set of constrains (e.g. mathematical model, number of samples, etc). In this work, we develop a methodology for the design of optimal isothermal inactivation experiments. We consider the two dimensions of the design space (time and temperature), as well as a temperature-dependent maximum duration of the experiment. Functions for its application have been included in the bioOED R package. We identify design patterns that remain optimum regardless of the number of sampling points for three inactivation models (Bigelow, Mafart and Peleg) and three model microorganisms (Escherichia coli, Salmonella Senftenberg and Bacillus coagulans). Samples at extreme temperatures and close to the maximum duration of the experiment are the most informative. Moreover, the Mafart and Peleg models require some samples at intermediate time points due to the non-linearity of the survivor curve. The impact of the reference temperature on the precision of the parameter estimates is also analysed. Based on numerical simulations we recommend fixing it to the mean of the maximum and minimum temperatures used for the experiments. The article ends with a discussion presenting guidelines for the design of isothermal inactivation experiments. They combine these optimum results based on information theory with several practical limitations related to isothermal inactivation experiments. The application of these guidelines would reduce the experimental burden required to characterize thermal inactivation.

Relationships of Water Activity and Moisture Content to the Thermal Inactivation Kinetics of Salmonella in Low-Moisture Foods

HIGHLIGHTS

Water affects thermal inactivation kinetics of Salmonella in low-moisture foods. Water activity and moisture content are both feasible predictors of heat resistance. Sorption state of food materials may affect Salmonella heat resistance.

Cross-Laboratory Comparative Study of the Impact of Experimental and Regression Methodologies on Salmonella Thermal Inactivation Parameters in Ground Beef

Isothermal inactivation studies are commonly used to quantify thermal inactivation kinetics of bacteria. Meta-analyses and comparisons utilizing results from multiple sources have revealed large variations in reported thermal resistance parameters for Salmonella, even when in similar food materials. Different laboratory or regression methodologies likely are the source of methodology-specific artifacts influencing the estimated parameters; however, such effects have not been quantified. The objective of this study was to evaluate the effects of laboratory and regression methodologies on thermal inactivation data generation, interpretation, modeling, and inherent error, based on data generated in two independent laboratories. The overall experimental design consisted of a cross-laboratory comparison using two independent laboratories (Michigan State University and U.S. Department of Agriculture, Agricultural Research Service, Eastern Regional Research Center [ERRC] laboratories), both conducting isothermal Salmonella inactivation studies (55, 60, 62°C) in ground beef, and each using two methodologies reported in prior studies. Two primary models (log-linear and Weibull) with one secondary model (Bigelow) were fitted to the resultant data using three regression methodologies (two two-step regressions and a one-step regression). Results indicated that laboratory methodology impacted the estimated D60°C- and z-values (α = 0.05), with the ERRC methodology yielding parameter estimates ∼25% larger than the Michigan State University methodology, regardless of the laboratory. Regression methodology also impacted the model and parameter error estimates. Two-step regressions yielded root mean square error values on average 40% larger than the one-step regressions. The Akaike Information Criterion indicated the Weibull as the more correct model in most cases; however, caution should be used to confirm model robustness in application to real-world data. Overall, the results suggested that laboratory and regression methodologies have a large influence on resultant data and the subsequent estimation of thermal resistance parameters.