Comparison of 1-step and 2-step methods of fitting microbiological models

The Use of Predictive Microbiology for the Prediction of the Shelf Life of Food Products

Microbial shelf life refers to the duration of time during which a food product remains safe for consumption in terms of its microbiological quality. Predictive microbiology is a field of science that focuses on using mathematical models and computational techniques to predict the growth, survival, and behaviour of microorganisms in food and other environments. This approach allows researchers, food producers, and regulatory bodies to assess the potential risks associated with microbial contamination and spoilage, enabling informed decisions to be made regarding food safety, quality, and shelf life. Two-step and one-step modelling approaches are modelling techniques with primary and secondary models being used, while the machine learning approach does not require using primary and secondary models for describing the quantitative behaviour of microorganisms, leading to the spoilage of food products. This comprehensive review delves into the various modelling techniques that have found applications in predictive food microbiology for estimating the shelf life of food products. By examining the strengths, limitations, and implications of the different approaches, this review provides an invaluable resource for researchers and practitioners seeking to enhance the accuracy and reliability of microbial shelf life predictions. Ultimately, a deeper understanding of these techniques promises to advance the domain of predictive food microbiology, fostering improved food safety practices, reduced waste, and heightened consumer confidence.

Modelling growth of Bacillus cereus in paneer by one-step parameter estimation

Bacillus cereus phylogenetic group III and IV strains are commonly associated with food products and cause toxin mediated foodborne diseases. These pathogenic strains have been identified from milk and dairy products, such as reconstituted infant formula and several cheeses. Paneer is a fresh, soft cheese originating from India that is prone to foodborne pathogen contamination, such as by Bacillus cereus. However, there are no reported studies of B. cereus toxin formation in paneer or predictive models quantifying growth of the pathogen in paneer under different environmental conditions. This study assessed enterotoxin-producing potential of B. cereus group III and IV strains, isolated from dairy farm environments, in fresh paneer. Growth of a four-strain cocktail of toxin-producing B. cereus strains was measured in freshly prepared paneer incubated at 5-55 °C and modelled using a one-step parameter estimation combined with bootstrap re-sampling to generate confidence intervals for model parameters. The pathogen grew in paneer between 10 and 50 °C and the developed model fit the observed data well (R² = 0.972, RMSE = 0.321 log₁₀ CFU/g). The cardinal parameters for B. cereus growth in paneer along with the 95% confidence intervals were: μ_opt 0.812 log₁₀ CFU/g/h (0.742, 0.917); T_opt is 44.177 °C (43.16, 45.49); T_min is 4.405 °C (3.973, 4.829); T_max is 50.676 °C (50.367, 51.144). The model developed can be used in food safety management plans and risk assessments to improve safety of paneer while also adding to limited information on B. cereus growth kinetics in dairy products.

Growth and Non-Thermal Inactivation of Staphylococcus aureus in Sliced Dry-Cured Ham in Relation to Water Activity, Packaging Type and Storage Temperature

Dry-cured ham (DCH) could support the growth of Staphylococcus aureus as a halotolerant bacterium, which may compromise the shelf-stability of the product according to the growth/no growth boundary models and the physicochemical parameters of commercial DCH. In the present study, the behavior of S. aureus is evaluated in sliced DCH with different water activity (a_w 0.861-0.925), packaged under air, vacuum, or modified atmosphere (MAP), and stored at different temperatures (2-25 °C) for up to 1 year. The Logistic and the Weibull models were fitted to data to estimate the primary kinetic parameters for the pathogen Log₁₀ increase and Log₁₀ reduction, respectively. Then, polynomial models were developed as secondary models following their integration into the primary Weibull model to obtain a global model for each packaging. Growth was observed for samples with the highest a_w stored at 20 and 25 °C in air-packaged DCH. For lower a_w, progressive inactivation of S. aureus was observed, being faster at the lowest temperature (15 °C) for air-packaged DCH. In contrast, for vacuum and MAP-packaged DCH, a higher storage temperature resulted in faster inactivation without a significant effect of the product a_w. The results of this study clearly indicate that the behavior of S. aureus is highly dependent on factors such as storage temperature, packaging conditions and product a_w. The developed models provide a management tool for evaluating the risk associated with DCH and for preventing the development of S. aureus by selecting the most appropriate packaging according to a_w range and storage temperature.

Formulation of Chemically Defined Media and Growth Evaluation of Ligilactobacillus salivarius ZJ614 and Limosilactobacillus reuteri ZJ625

Lactic acid bacteria are increasingly becoming important dietary supplements due to their health benefits when consumed in adequate quantity. The increasing attention on these important microbes has necessitated an in-depth understanding of their physiological processes, such as nutritional requirements and growth patterns, to better harness their probiotic potentials. This study was carried out to determine the nutritional requirements for the growth of L. salivarius ZJ614 and L. reuteri ZJ625 from a chemically defined medium and evaluate growth kinetics by fitting different sigmoidal growth models. The complete CDM contains 49 nutritional ingredients such as glucose, Tween 80^®, mineral salts, buffers, amino acids, vitamins, and nucleotides at defined concentrations. In addition, the minimal nutritional requirements of the isolates were determined in a series of single-omission experiments (SOEs) to compose the MDM. Growth curve data were generated by culturing in an automated 96-well micro-plate reader at 37°C for 36 h, and photometric readings (optical density: OD₆₀₀) were taken. The data were summarized in tables and charts using Microsoft Excel, while growth evaluation was carried out using open-source software (Curveball) on Python. The results revealed that omission of the amino acids, vitamins, and nucleotides groups resulted in 2.0, 20.17, and 60.24% (for L. salivarius ZJ614) and 0.95, 42.7, and 70.5% (for L. reuteri ZJ625) relative growths, respectively. Elimination of the individual CDM components also indicates varying levels of growth by the strains. The growth curve data revealed LogisticLag2 and Baranyi–Roberts models as the best fits for L. reuteri ZJ625 and L. salivarius ZJ614, respectively. All the strains showed appreciable growth on the CDM and MDM as observed in de Man–Rogosa–Sharpe (MRS) broth. We also described the growth kinetics of L. reuteri ZJ625 and L. salivarius ZJ614 in the CDM, and the best models revealed the estimated growth parameters.

Analysis and mathematical modeling of the survival kinetics of Staphylococcus aureus in raw pork under dynamic and static temperature conditions

The incidence of frequent foodborne disease outbreaks due to Staphylococcus aureus contamination necessitates the urgent searching for effective methods to monitor S. aureus. This research aims to construct model with a dynamic survival curve and some static growth curves to predict the behavior of S. aureus in raw pork. Lack of research about S. aureus kinetics in pork under fluctuating temperature conditions across freezing and thawing necessitates this study. One-step analysis was used to determine the model parameters, which was more efficient than conventional model analysis with two steps. The results of kinetic analysis showed that Tmin (minimum growth temperature) was 6.85°C, which is close to the estimated values in previous reports. Subsequently, validation results indicated the integrated model can accurately predict the behavior of S. aureus regardless of isothermal or nonisothermal conditions with the root-mean-square errors (RMSE < 0.44 log CFU/g, 73.9% of the errors of prediction falls within ±0.5 log CFU/g), accuracy factors Af and bias factors Bf were both close to 1. This work may offer an effective method for the assessment of microbial security related to S. aureus in pork.

Development and validation of a one-step modelling approach for the determination of chicken meat shelf-life based on the growth kinetics of Pseudomonas spp

The main objective of the present study was to investigate the effect of storage temperature on aerobically stored chicken meat spoilage using the two-step and one-step modelling approaches involving different primary models namely the modified Gompertz, logistic, Baranyi and Huang models. For this purpose, growth data points of Pseudomonas spp. were collected from published studies conducted in aerobically stored chicken meat product. Temperature-dependent kinetic parameters (maximum specific growth rate 'µ_max' and lag phase duration 'λ') were described as a function of storage temperature through the Ratkowsky model based on the different primary models. Then, the fitting capability of both modelling approaches was compared taking into account root mean square error, adjusted coefficient of determination (adjusted-R²) and corrected Akaike information criterion. The one-step modelling approach showed considerably improved fitting capability regardless of the used primary model. Finally, models developed from the one-step modelling approach were validated for the maximum growth rate data extracted from independent published literature using the statistical indexes Bias (B_f) and Accuracy (A_f) factors. The best prediction capability was obtained for the Baranyi model with B_f and A_f being very close to 1. The shelf-life of chicken meat as a function of storage temperature was predicted using both modelling approaches for the Baranyi model.

One- and Two-Step Kinetic Data Analysis Applied for Single and Co-Culture Growth of Staphylococcus aureus, Escherichia coli, and Lactic Acid Bacteria in Milk

The objective of this study was to compare one- and two-step kinetic data analysis approaches to describe the growth of Staphylococcus aureus, Escherichia coli, and lactic acid bacteria Fresco 1010 starter culture in milk under isothermal conditions between 10 and 37 °C. The primary Huang model (HM) and secondary square root model were applied to lag times and growth rates of each of the population. The one-step approach for single cultures data enabled the direct construction of a tertiary model combining primary and secondary models to determine parameters from all growth data, thus minimizing the transfer of errors from one model to another. The statistical indices showed a significant improvement in the prediction capability provided by this approach. Then, a one-step approach combining the primary Huang, Giménez, and Dalgaard model (H-GD) with the secondary square root model was used to simultaneously model the growth of the populations mentioned above in co-culture under the same conditions. Independent isothermal data sets were chosen for validation of the growth description of single cultures (HM) and co-culture (H-GD) using validation factors, including the bias (Bf) and accuracy (Af). For example, the values of Af for the one-step approach range from 1.17 to 1.20 and 1.04 to 1.08 for single cultures and co-culture, respectively, demonstrating high accuracy. Thus, this approach may be used for co-culture growth description in general or specifically, e.g., in various types of lactic acid fermentation, including artisanal cheese-making technology.

Thermal Inactivation Kinetics of Salmonella and Enterococcus faecium NRRL-B2354 on Whole Chia Seeds (Salvia hispanica L.)

ABSTRACT

Intervention technologies for inactivating Salmonella on whole chia seeds are currently limited. Determination of the thermal inactivation kinetics of Salmonella on chia seeds and selection of an appropriate nonpathogenic surrogate will provide a foundation for selecting and optimizing thermal pasteurization processes for chia seeds. In this study, chia seed samples from three separate production lots were inoculated with a five-strain Salmonella cocktail or Enterococcus faecium NRRL-B2354 and equilibrated to a water activity of 0.53 at room temperature (25°C). After equilibration for at least 3 days, the inoculated seeds were subjected to isothermal treatments at 80, 85, or 90°C. Samples were removed at six time points, and surviving bacteria were enumerated. Whole chia seeds were diluted in a filter bag at 1:30 because bacterial recovery with this method was similar to that obtained from ground seeds. Survivor data were fitted to consolidated models: one primary model (log linear or Weibull) and one secondary model (Bigelow). E. faecium had higher thermal resistance than did Salmonella, suggesting that E. faecium may be a suitable conservative nonpathogenic surrogate for Salmonella. The Weibull model was a better fit for the survivor data than was the log-linear model for both bacteria based on the lower root mean square error and corrected Akaike's information criterion values. Lipid oxidation measurements and fatty acid concentrations were significantly different from those of the control samples, but the overall magnitude of the differences was relatively small. The thermal inactivation kinetics of Salmonella and E. faecium on chia seeds may be used as a basis for developing thermal pasteurization processes for chia seeds.

HIGHLIGHTS

Mathematical Models for the Biofilm Formation of Geobacillus and Anoxybacillus on Stainless Steel Surface in Whole Milk

Biofilm formation of Geobacillus thermodenitrificans, Geobacillus thermoglucosidans and Anoxybacillus flavithermus in milk on stainless steel were monitored at 55°C, 60°C, and 65°C for various incubation times. Although species of Geobacillus showed a rapid response and produced biofilm within 4 h on stainless steel, a delay (lag time) was observed for Anoxybacillus. A hyperbolic equation and a hyperbolic equation with lag could be used to describe the biofilm formation of Geobacillus and Anoxybacillus, respectively. The highest biofilm formation amount was obtained at 60°C for both Geobacillus and Anoxybacillus. However, the biofilm formation rates indicated that the lowest rates of formation were obtained at 60°C for Geobacillus. Moreover, biofilm formation rates of G. thermodenitrificans (1.2–1.6 Log₁₀CFU/mL·h) were higher than G. thermoglucosidans (0.4–0.7 Log₁₀CFU/mL·h). Although A. flavithermus had the highest formation rate values (2.7–3.6 Log₁₀CFU/mL·h), this was attained after the lag period (4 or 5 h). This study revealed that modeling could be used to describe the biofilm formation of thermophilic bacilli in milk.

Risk management tool to define a corrective storage to enhance Salmonella inactivation in dry fermented sausages

The resistance of Salmonella to the harsh conditions occurring in shelf-stable dry fermented sausages (DFS) poses a food safety challenge for producers. The present study aimed to model the behaviour of Salmonella in acid (with starter culture) and low-acid (without starter culture) DFS as a function of a_w and storage temperature in order to build a decision supporting tool supporting the design of a corrective storage strategy to enhance the safety of DFS. Salmonella spp. were inoculated in the raw meat batter at ca. 6 Log cfu/g with a cocktail of 3 strains (CTC1003, CTC1022 and CTC1754) just before mixing with the other ingredients and additives. After stuffing, sausages were fermented and ripened following industrial processing conditions. Different drying-times were applied to obtain three batches with different a_w (0.88, 0.90 and 0.93). Afterwards, DFS were stored at 4, 8, 15 and 25 °C for a maximum of three months and Salmonella spp. were periodically enumerated. The Weibull model was fitted to Log counts data to estimate inactivation kinetic parameters. The impact of temperature and a_w on the primary inactivation parameters was evaluated using a polynomial equation. The results of the challenge tests showed that Salmonella spp. levels decreased during storage at all the assayed conditions, from 0.8 Log (in low-acid DFS at 4 °C) up to 6.5 Log (in acid DFS at 25 °C). The effect of both a_w and temperature was statistically significant. Delta (δ) parameter decreased by decreasing a_w and increasing temperature, while the shape (p) parameter ranged from above 1 (concave) at 10 °C to below 1 at 25 °C (convex). A common secondary model for the p parameter was obtained for each type of DFS, acid and low-acid, indicating that acidification during the production of DFS affected the time for the first Log reduction (δ) during the subsequent storage, but not the overall shape (p parameter) of the inactivation. The developed models covered representative of real conditions, such as Salmonella contamination in the raw materials and its adaptation to the harsh processing conditions. The good predictive performance shown when applying the models to independent data (i.e. up to 80% of the predictions within the 'Acceptable Simulation Zone' for acid sausages) makes them a suitable and reliable risk management tool to support manufacturers to assess and design a lethality treatment (i.e. corrective storage) to enhance the Salmonella inactivation in the product before DFS are released to the market.

A Comparison of Three Methods for Determining Thermal Inactivation Kinetics: A Case Study on Salmonella enterica in Whole Milk Powder

ABSTRACT

Different methods for determining the thermal inactivation kinetics of microorganisms can result in discrepancies in thermal resistance values. In this study, thermal resistance of Salmonella in whole milk powder was determined with three methods: thermal death time (TDT) disk in water bath, pouches in water bath, and the TDT Sandwich system. Samples from three production lots of whole milk powder were inoculated with a five-strain Salmonella cocktail and equilibrated to a water activity of 0.20. The samples were then subjected to three isothermal treatments at 75, 80, or 85°C. Samples were removed at six time points and cultures were enumerated for survivors. The inactivation data were fitted to two consolidated models: two primary models (log linear and Weibull) and one secondary model (Bigelow). Normality testing indicated that all the model parameters were normally distributed. None of the model parameters for both consolidated models were significantly different (α = 0.05). The amount of inactivation during the come-up time was also not significantly different among the methods (α = 0.05). However, the TDT Sandwich resulted in less inactivation during the come-up time and overall less variation in model parameters. The survivor data from all three methods were combined and fitted to both consolidated models. The Weibull had a lower root mean square error and a better fit, according to the corrected Akaike's information criterion. The three thermal treatment methods produced results that were not significantly different; thus, the methods are interchangeable, at least for Salmonella in whole milk powder. Comparisons with more methods, other microorganisms, and larger varieties of food products using the same framework presented in this study could provide guidance for standardizing thermal inactivation kinetics studies for microorganisms in foods.

HIGHLIGHTS

Stability of hepatitis E virus at high hydrostatic pressure processing

Hepatitis E virus (HEV) is the causative agent of acute and chronic hepatitis in humans. The zoonotic HEV genotype 3 is the main genotype in Europe. The foodborne transmission via consumption of meat and meat products prepared from infected pigs or wild boars is considered the major transmission route of this genotype. High hydrostatic pressure processing (HPP) is a technique, which can be used for inactivation of pathogens in food. Here, preparations of a cell culture-adapted HEV genotype 3 strain in phosphate-buffered saline (PBS) were subjected to HPP and the remaining infectivity was titrated in cell culture by counting fluorescent foci of replicating virus. A gradual decrease in infectivity was found by application of 100 to 600 MPa for 2 min. At 20 °C, infectivity reduction of 0.5 log₁₀ at 200 MPa and 1 log₁₀ at 400 MPa were observed. Slightly higher infectivity reduction of 1 log₁₀ at 200 MPa and 2 log₁₀ at 400 MPa were found by application of the pressure at 4 °C. At both temperatures, the virus was nearly completely inactivated (>3.5 log₁₀ infectivity decrease) at 600 MPa; however, low amounts of remaining infectious virus were observed in one of three replicates in both cases. Transmission electron microscopy showed disassembled and distorted particles in the preparations treated with 600 MPa. Time-course experiments at 400 MPa showed a continuous decline of infectivity from 30 s to 10 min, leading to a 2 log₁₀ infectivity decrease at 20 °C and to a 2.5 log₁₀ infectivity decrease at 4 °C for a 10 min pressure application each. Predictive models for inactivation of HEV by HPP were generated on the basis of the generated data. The results show that HPP treatment can reduce HEV infectivity, which is mainly dependent on pressure height and duration of the HPP treatment. Compared to other viruses, HEV appears to be relatively stable against HPP and high pressure/long time combinations have to be applied for significant reduction of infectivity.

Antimicrobial effect of nisin in processed cheese - Quantification of residual nisin by LC-MS/MS and development of new growth and growth boundary model for Listeria monocytogenes

This study tested the hypothesis that growth of Listeria monocytogenes in processed cheese with added nisin can be predicted from residual nisin A concentrations in the final product after processing. A LC-MS/MS method and a bioassay were studied to quantify residual nisin A concentrations and a growth and growth boundary model was developed to predict the antilisterial effect in processed cheese. 278 growth rates were determined in broth for 11 L. monocytogenes isolates and used to determine 13 minimum inhibitory concentration (MIC) values for nisin between pH 5.5 and 6.5. To supplement these data, 67 MIC-values at different pH-values were collected from the scientific literature. A MIC-term was developed to describe the effect of pH on nisin MIC-values. An available growth and growth boundary model (doi: https://doi.org/10.1016/j.fm.2019.103255) was expanded with the new MIC-term for nisin to predict growth in processed cheese. To generate data for model evaluation and further model development, challenge tests with a total of 45 growth curves, were performed using processed cheese. Cheeses were formulated with 11.2 or 12.0 ppm of nisin A and heat treated to obtain residual nisin A concentrations ranging from 0.56 to 5.28 ppm. Below 15 °C, nisin resulted in extended lag times. A global regression approach was used to fit all growth curves determined in challenge tests. This was obtained by combining the secondary growth and growth boundary model including the new term for the inhibiting effect of nisin on μ_max with the primary logistic growth model with delay. This model appropriately described the growth inhibiting effect of residual nisin A and showed that relative lag times depended on storage temperatures. With residual nisin A concentrations, other product characteristics and storage temperature as input the new model correctly predicted all observed growth and no-growth responses for L. monocytogenes. This model can support development of nisin A containing recipes for processed cheese that prevent growth of L. monocytogenes. Residual nisin A concentrations in processed cheese were accurately quantified by the developed LC-MS/MS method with recoveries of 83 to 110% and limits of detection and quantification being 0.04 and 0.13 ppm, respectively. The tested bioassay was less precise and nisin A recoveries varied for 53% to 94%.

Holistic Approach to the Uncertainty in Shelf Life Prediction of Frozen Foods at Dynamic Cold Chain Conditions

Systematic kinetic modeling is required to predict frozen systems behavior in cold dynamic conditions. A one-step procedure, where all data are used simultaneously in a non-linear algorithm, is implemented to estimate the kinetic parameters of both primary and secondary models. Compared to the traditional two-step methodology, more precise estimates are obtained, and the calculated parameter uncertainty can be introduced in realistic shelf life predictions, as a tool for cold chain optimization. Additionally, significant variability of the real distribution/storage conditions is recorded, and must be also incorporated in a kinetic prediction scheme. The applicability of the approach is theoretically demonstrated in an analysis of data on frozen green peas Vitamin C content, for the calculation of joint confidence intervals of kinetic parameters. A stochastic algorithm is implemented, through a double Monte Carlo scheme incorporating the temperature variability during distribution, drawn from cold chain databases. Assuming a distribution scenario of 130 days in the cold chain, 93 ± 110 days remaining shelf life was predicted compared to 180 days assumed based on the use by date. Overall, through the theoretical case study investigated, the uncertainty of models’ parameters and cold chain dynamics were incorporated into shelf life assessment, leading to more realistic predictions.

One-Step Analysis for Listeria monocytogenes Growth in Ready-to-Eat Braised Beef at Dynamic and Static Conditions

This study aimed to estimate the growth parameters of Listeria monocytogenes growth in ready-to-eat (RTE) braised beef by one-step dynamic and static kinetic analysis. The Baranyi model and cardinal parameters model were integrated into a dynamic and static model to estimate the kinetic parameters under one dynamic condition (-20 to 40.0°C) and eight static conditions (4, 8, 15, 20, 30, 35, 37, and 40°C). Based on the dynamic and static methods, the respective dynamic and static results for estimated growth boundaries of L. monocytogenes in RTE braised beef were from -2.5 and -2.7°C to 40.5 and 40.7°C with optimal specific growth rates of 1.078 and 0.913 per h at temperatures of 35.7 and 35.0°C. Temperature effects on the specific growth rate and lag period were developed and used to simulate the change of the physiological state of inocula during the bacterial growth. Subsequently, three additional dynamic temperature profiles were implemented for external validation. The root mean square error of the model developed by dynamic regression (0.19 log CFU/g) is slightly better than that of the model developed by static regression (0.23 log CFU/g). Comparing the validation results, one-step dynamic analysis might be a preferable method for prediction, especially when the growth approaches the stationary phase. Generally, both one-step dynamic and static analyses could be used to accurately predict L. monocytogenes growth in RTE braised beef under fluctuating temperatures.

An interaction model for the combined effect of temperature, pH and water activity on the growth rate of E. coli K12

Previous research has indicated that more complex model structures than the commonly used gamma model are needed to obtain an accurate prediction of the effect of multiple environmental conditions on the microbial growth rate. Due to the complexity associated with the development of such model structures, it is recommended that the model structure is compatible with a modular model building method. In this research, a gamma-interaction model was built to describe the combined effect of temperature, pH and water activity on the microbial growth rate of E. coli K12 based on a dataset of 68 bioreactor experiments. This novel interaction model was compared with the standard gamma model. The model structures were tested separately for the combined effects of (i) temperature and pH, (ii) pH and water activity, (iii) temperature and water activity and (iv) temperature, pH and water activity. Based on the results of this research, it was concluded that models for the combined effect of environmental conditions need to allow for sufficient flexibility for the description of combined effects of environmental conditions to obtain accurate model predictions. In the current study, this flexibility was successfully introduced by using the gamma-interaction model. A cross-validation study also demonstrated that the predictions of the interaction model are more robust with respect to the specific data used than the gamma model. As such, the gamma-interaction model provides food producers and food safety authorities with a more accurate and reliable tool for the prediction of the microbial growth rate as a function of multiple environmental conditions.

One-step global parameter estimation of kinetic inactivation parameters for Bacillus sporothermodurans spores under static and dynamic thermal processes

Bacillus sporothermodurans produces highly heat-resistant endospores, that can survive under ultra-high temperature. High heat-resistant sporeforming bacteria are one of the main causes for spoilage and safety of low-acid foods. They can be used as indicators or surrogates to establish the minimum requirements for heat processes, but it is necessary to understand their thermal inactivation kinetics. The aim of the present work was to study the inactivation kinetics under both static and dynamic conditions in a vegetable soup. Ordinary least squares one-step regression and sequential procedures were applied for estimating these parameters. Results showed that multiple dynamic heating profiles, when analyzed simultaneously, can be used to accurately estimate the kinetic parameters while significantly reducing estimation errors and data collection.

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.

Growth of Staphylococcus aureus in Cooked Potato and Potato Salad--A One-Step Kinetic Analysis

Staphylococcus aureus is a Gram-positive spherically-shaped bacterium capable of producing heat-stable enterotoxins that cause acute gastrointestinal diseases. The growth of this pathogen in food is a major threat to public health worldwide. Potato salad is a frequent vehicle for infection and food poisoning caused by S. aureus. Therefore, the objective of this study was to investigate the growth kinetics of S. aureus in cooked potato and potato salad. Samples of potato cubes and potato salad inoculated with S. aureus were incubated at temperatures between 8 and 43 °C to observe its growth for developing growth models. No growth was observed at 8 °C. The experimental results showed that the growth curves did not exhibit lag phases, and can be described by a 3-parameter logistic model. A one-step kinetic analysis approach was used to simultaneously analyze all growth curves by direct construction of both the primary and secondary (Ratkowsky square root) models using nonlinear regression to minimize the global residual error. The estimated nominal minimum growth temperature of S. aureus was 6.12 °C in potato cubes and 8.80 °C in potato salad. The estimated maximum growth temperatures of S. aureus in potato cubes and potato salad were very close to each other (46.3 and 46.8 °C, respectively). On the average, the specific growth rates of S. aureus in potato cubes were approximately 70% higher than those in potato salad. This study suggests that cooked potato and potato salad should be stored below 6 °C or above 47 °C to prevent the growth of S. aureus. The mathematical models and kinetic parameters can be used to accurately evaluate the effect of temperature abuse on the growth of S. aureus and conduct risk assessments of S. aureus in cooked potato and potato salad.

Comparison of Primary Models to Predict Microbial Growth by the Plate Count and Absorbance Methods

The selection of a primary model to describe microbial growth in predictive food microbiology often appears to be subjective. The objective of this research was to check the performance of different mathematical models in predicting growth parameters, both by absorbance and plate count methods. For this purpose, growth curves of three different microorganisms (Bacillus cereus, Listeria monocytogenes, and Escherichia coli) grown under the same conditions, but with different initial concentrations each, were analysed. When measuring the microbial growth of each microorganism by optical density, almost all models provided quite high goodness of fit (r² > 0.93) for all growth curves. The growth rate remained approximately constant for all growth curves of each microorganism, when considering one growth model, but differences were found among models. Three-phase linear model provided the lowest variation for growth rate values for all three microorganisms. Baranyi model gave a variation marginally higher, despite a much better overall fitting. When measuring the microbial growth by plate count, similar results were obtained. These results provide insight into predictive microbiology and will help food microbiologists and researchers to choose the proper primary growth predictive model.

Analysing and modelling the growth behaviour of Listeria monocytogenes on RTE cooked meat products after a high pressure treatment at 400 MPa

Various predictive models are available for high pressure inactivation of Listeria monocytogenes in food, but currently available models do not consider the growth kinetics of surviving cells during the subsequent storage of products. Therefore, we characterised the growth of L. monocytogenes in sliced cooked meat products after a pressurization treatment. Two inoculum levels (10(7) or 10(4) CFU/g) and two physiological states before pressurization (freeze-stressed or cold-adapted) were evaluated. Samples of cooked ham and mortadella were inoculated, high pressure processed (400 MPa, 5 min) and subsequently stored at 4, 8 and 12 °C. The Logistic model with delay was used to estimate lag phase (λ) and maximum specific growth rate (μmax) values from the obtained growth curves. The effect of storage temperature on μmax and λ was modelled using the Ratkowsky square root model and the relative lag time (RLT) concept. Compared with cold-adapted cells the freeze-stressed cells were more pressure-resistant and showed a much longer lag phase during growth after the pressure treatment. Interestingly, for high-pressure inactivation and subsequent growth, the time to achieve a concentration of L. monocytogenes 100-fold (2-log) higher than the cell concentration prior to the pressure treatment was similar for the two studied physiological states of the inoculum. Two secondary models were necessary to describe the different growth behaviour of L. monocytogenes on ready-to-eat cooked ham (lean product) and mortadella (fatty product). This supported the need of a product-oriented approach to assess growth after high pressure processing. The performance of the developed predictive models for the growth of L. monocytogenes in high-pressure processed cooked ham and mortadella was evaluated by comparison with available data from the literature and by using the Acceptable Simulation Zone approach. Overall, 91% of the relative errors fell into the Acceptable Simulation Zone.