Development and validation of an extensive growth and growth boundary model for psychrotolerant Lactobacillus spp. in seafood and meat products

Development of a quantitative microbiological spoilage risk assessment (QMSRA) model for cooked ham sliced at retail

A quantitative microbiological spoilage risk assessment model (QMSRA) for cooked ham sliced at retail was developed based on a stochastic growth model for lactic acid bacteria (LAB), which are considered as the specific spoilage organisms (SSO), and a "spoilage-response" relationship characterizing the variability in consumer's perception of spoilage. In a simulation involving 10,000 cooked ham purchases, the QMSRA model predicted a median of zero spoilage events for up to 4.5 days of storage. After storage times of 5 and 6 days, the model predicted 1,790 and 8,570 spoilage events, respectively. A sensitivity analysis showed that domestic storage temperature was the most significant factor affecting LAB concentration in cooked ham, followed by the LAB contamination level at slicing. A scenario analysis was performed testing better temperature control of consumer's refrigerators, better hygiene conditions during slicing and a combination of the two strategies. Among the tested scenarios, a 2 log reduction in the LAB contamination at slicing combined with a 2 °C decrease in domestic storage temperature resulted in zero risk of spoilage for up to 12 days of storage. The QMSRA model developed in the present study can be a useful tool for quality management decisions.

Coupling dynamics of respiration, gas exchange, and Pseudomonas fluorescens growth on fresh-cut cucumber (Cucumis sativus L.) in passive modified atmosphere packing

This study established microbial growth models for fresh-cut cucumber packaged with different O2 transmission rate (OTR) films. Biaxially oriented polyamide/low-density polyethylene (BOPA/LDPE) film (Ⅰ: OTR5, Ⅳ: OTR48) and polyethylene (PE) film (Ⅱ: OTR2058, Ⅲ: OTR3875) were used to construct a passive modified atmosphere packaging (MAP). Mathematic models have been established to account for dynamic variations in the O2/CO2 concentration and their impacts on Pseudomonas fluorescens growth. The coupling models included: 1) respiration models of cucumber and P. fluorescens based on Michaëlis-Menten equation, 2) coupling gas exchange models based on Fick's law that contained models of P. fluorescens growth and respiration, 3) coupling microbial growth models contained respiration and gas exchange models. Coupling model with Baranyi function successfully fitted variations of O2/CO2 concentration and P. fluorescens growth in the two packaging. In addition, quality properties of packed fresh-cut cucumber were determined. The film Ⅳ (OTR48) as a high barrier film showed the highest inhibition of P. fluorescens growth, adequately retained its colour, firmness and total soluble solid (TSS) concentration in contrast to the PE films packaging. The constructed coupling models can be utilized for assessing the shelf life and microbial growth of fresh-cut vegetables with spoilage dominated by pseudomonads.

Microbiological safety of aged meat

The impact of dry-ageing of beef and wet-ageing of beef, pork and lamb on microbiological hazards and spoilage bacteria was examined and current practices are described. As 'standard fresh' and wet-aged meat use similar processes these were differentiated based on duration. In addition to a description of the different stages, data were collated on key parameters (time, temperature, pH and aw) using a literature survey and questionnaires. The microbiological hazards that may be present in all aged meats included Shiga toxin-producing Escherichia coli (STEC), Salmonella spp., Staphylococcus aureus, Listeria monocytogenes, enterotoxigenic Yersinia spp., Campylobacter spp. and Clostridium spp. Moulds, such as Aspergillus spp. and Penicillium spp., may produce mycotoxins when conditions are favourable but may be prevented by ensuring a meat surface temperature of -0.5 to 3.0°C, with a relative humidity (RH) of 75-85% and an airflow of 0.2-0.5 m/s for up to 35 days. The main meat spoilage bacteria include Pseudomonas spp., Lactobacillus spp. Enterococcus spp., Weissella spp., Brochothrix spp., Leuconostoc spp., Lactobacillus spp., Shewanella spp. and Clostridium spp. Under current practices, the ageing of meat may have an impact on the load of microbiological hazards and spoilage bacteria as compared to standard fresh meat preparation. Ageing under defined and controlled conditions can achieve the same or lower loads of microbiological hazards and spoilage bacteria than the variable log10 increases predicted during standard fresh meat preparation. An approach was used to establish the conditions of time and temperature that would achieve similar or lower levels of L. monocytogenes and Yersinia enterocolitica (pork only) and lactic acid bacteria (representing spoilage bacteria) as compared to standard fresh meat. Finally, additional control activities were identified that would further assure the microbial safety of dry-aged beef, based on recommended best practice and the outputs of the equivalence assessment.

Use of Food Spoilage and Safety Predictor for an “A Priori” Modeling of the Growth of Lactic Acid Bacteria in Fermented Smoked Fish Products

Fermentation is one of the oldest methods to assure the safety and quality of foods, and to prolong their shelf life. However, a successful fermentation relies on the correct kinetics depending on some factors (i.e., ingredients, preservatives, temperature, inoculum of starter cultures). Predictive microbiology is a precious tool in modern food safety and quality management; based on the product characteristics and the conditions occurring in food processing, the inactivation of or increase in microbial populations could be accurately predicted as a function of the relevant intrinsic or extrinsic variables. The main aim of this study was the optimization of the formula of a smoked fermented fish product using predictive modeling tools (tertiary and secondary models) in order to define the role of each factor involved in the formulation and assure a correct course of fermentation. Product optimization was conducted through the software Food Spoilage and Safety Predictor (FSSP), by modeling the growth of lactic acid bacteria (LAB) as a function of some key parameters such as temperature, pH, salt, liquid smoke, carbon dioxide, and nitrites. The variables were combined through a fractional design of experiments (DoE) (3^k-p), and the outputs of the software, i.e., the maximal growth rate (μ_max) and the time to attain the critical threshold (t_crit), were modeled through a multiple regression procedure. The simulation, through FSSP and DoE, showed that liquid smoke is the most critical factor affecting fermentation, followed by temperature and salt. Concerning temperature, fermentation at 20–25 °C is advisable, although a low fermentation temperature is also possible. Other parameters are not significant.

Extensive growth and growth boundary model for non-proteolytic Clostridium botulinum - Evaluation and validation with MAP and smoked foods

The growth inhibiting effect of lactic acid bacteria (LAB) on non-proteolytic Clostridium botulinum was studied. LAB had no significant effect on growth of C. botulinum and their effect was not included in the model to be evaluated. An available cardinal parameter growth and growth boundary model for non-proteolytic C. botulinum (Koukou et al., 2021; https://doi.org/10.1016/j.ijfoodmicro.2021.109162) was evaluated using a total of 822 time-to-toxin (TTT) formation data extracted from the scientific literature for seafood, poultry, vegetables and meat products. These data included smoked products and food stored in air, vacuum or modified atmosphere packaging (MAP) with added CO₂. The available extensive model predicted TTT formation without bias (B_f-TTT value = 0.99) and with a reasonable accuracy (A_f-TTT value = 1.76). The model was successfully validated for seafood and poultry products. This study substantially increased the range of applicability of the available growth and growth boundary model for non-proteolytic C. botulinum. The performed evaluation showed this model can be used to predict environmental conditions to prevent growth in seafood and poultry products including smoked fish and MAP foods. It is expected that this validated model will contribute to product development and innovation including new sodium reduced foods.

Guidance on date marking and related food information: part 2 (food information)

A risk‐based approach was used to develop guidance to be followed by food business operators (FBOs) when deciding on food information relating to storage conditions and/or time limits for consumption after opening a food package and thawing of frozen foods. After opening the package, contamination may occur, introducing new pathogens into the food and the intrinsic (e.g. pH and a_w), extrinsic (e.g. temperature and gas atmosphere) and implicit (e.g. interactions with competing background microbiota) factors may change, affecting microbiological food safety. Setting a time limit for consumption after opening the package (secondary shelf‐life) is complex in view of the many influencing factors and information gaps. A decision tree (DT) was developed to assist FBOs in deciding whether the time limit for consumption after opening, due to safety reasons, is potentially shorter than the initial ‘best before’ or ‘use by’ date of the product in its unopened package. For products where opening the package leads to a change of the type of pathogenic microorganisms present in the food and/or factors increasing their growth compared to the unopened product, a shorter time limit for consumption after opening would be appropriate. Freezing prevents the growth of pathogens, however, most pathogenic microorganisms may survive frozen storage, recover during thawing and then grow and/or produce toxins in the food, if conditions are favourable. Moreover, additional contamination may occur from hands, contact surfaces or contamination from other foods and utensils. Good practices for thawing should, from a food safety point of view, minimise growth of and contamination by pathogens between the food being thawed and other foods and/or contact surfaces, especially when removing the food from the package during thawing. Best practices for thawing foods are presented to support FBOs.

Cardinal parameter growth and growth boundary model for non-proteolytic Clostridium botulinum - Effect of eight environmental factors

A new cardinal parameter growth and growth boundary model for non-proteolytic C. botulinum was developed and validated for fresh and lightly preserved seafood and poultry products. 523 growth rates in broth were used to determine cardinal parameter values and terms for temperature, pH, NaCl/water activity, acetic, benzoic, citric, lactic and sorbic acids. The new growth and growth boundary model included the inhibiting interactive effect between these factors and it was calibrated using growth curves from 10 challenge tests with unprocessed seafood. For model evaluation, 40 challenge tests with well characterized fresh and lightly preserved seafood were performed. Comparison of these observed growth curves and growth rates (μ_max-values) predicted by the new model resulted in a bias factor (B_f) of 1.12 and an accuracy factor (A_f) of 1.40. Furthermore, the new model was evaluated with 94 growth rates and 432 time to toxin formation data extracted from the scientific literature for seafood, poultry, meat, pasta and prepared meals. These data included responses for 36 different toxigenic strains of non-proteolytic C. botulinum. The obtained B_f-/A_f-values were 0.97/2.04 for μ_max-values and 0.96/1.80 for time to toxin formation. The model correctly predicted 93.8% of the growth responses with 5.6% being fail-safe and <1% fail-dangerous. A cocktail of four non-toxin producing Clostridium spp. isolates was used to develop the new model and these isolates had more than 99.8% 16S rRNA gene similarity to non-proteolytic C. botulinum (Group II). The high number of environmental factors included in the new model makes it a flexible tool to facilitate development or reformulation of seafood and poultry products that do not support the growth of non-proteolytic C. botulinum. Further, evaluation of the new model with well characterized products is desirable particularly for meat, vegetables, pasta and prepared meals as well as for dairy products that was not included in the present study.

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

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

Modeling the Growth and Interaction Between Brochothrix thermosphacta, Pseudomonas spp., and Leuconostoc gelidum in Minced Pork Samples

The aim of this study was to obtain the growth parameters of specific spoilage micro-organisms previously isolated in minced pork (MP) samples and to develop a three-spoilage species interaction model under different storage conditions. Naturally contaminated samples were used to validate this approach by considering the effect of the food microbiota. Three groups of bacteria were inoculated on irradiated samples, in mono- and in co-culture experiments (n = 1152): Brochothrix thermosphacta, Leuconostoc gelidum, and Pseudomonas spp. (Pseudomonas fluorescens and Pseudomonas fragi). Samples were stored in two food packaging [food wrap and modified atmosphere packaging (CO2 30%/O2 70%)] at three isothermal conditions (4, 8, and 12°C). Analysis was carried out by using both 16S rRNA gene amplicon sequencing and classical microbiology in order to estimate bacterial counts during the storage period. Growth parameters were obtained by fitting primary (Baranyi) and secondary (square root) models. The food packaging shows the highest impact on bacterial growth rates, which in turn have the strongest influence on the shelf life of food products. Based on these results, a three-spoilage species interaction model was developed by using the modified Jameson-effect model and the Lotka Volterra (prey-predator) model. The modified Jameson-effect model showed slightly better performances, with 40-86% out of the observed counts falling into the Acceptable Simulation Zone (ASZ). It only concerns 14-48% for the prey-predator approach. These results can be explained by the fact that the dynamics of experimental and validation datasets seems to follow a Jameson behavior. On the other hand, the Lotka Volterra model is based on complex interaction factors, which are included in highly variable intervals. More datasets are probably needed to obtained reliable factors, and so better model fittings, especially for three- or more-spoilage species interaction models. Further studies are also needed to better understand the interaction of spoilage bacteria between them and in the presence of natural microbiota.

Growth and growth boundary model with terms for melting salts to predict growth responses of Listeria monocytogenes in spreadable processed cheese

The aim of this study was to develop and validate a growth and growth boundary model with terms for melting salts to predict growth of Listeria monocytogenes in spreadable processed cheese. Cardinal parameter terms for phosphate salts and citric acid were developed in broth studies and used to expand an available growth and growth boundary model. The expanded model includes the effect of nine environmental factors (temperature, pH, a_w, lactic acid, acetic acid, citric acid, orthophosphate, di-phosphate and tri-phosphate). To generate growth data for model evaluation challenge tests with inoculated commercial (n = 10) and customized (n = 10) spreadable processed cheeses were performed. Evaluation of the new model by comparison of observed and predicted μ_max-values resulted in a bias factor of 1.12 and an accuracy factor of 1.33 (n = 42). Prediction of growth and no-growth responses in processed cheese (n = 60) were 89% correct with 11% fail-safe and 0% fail-dangerous predictions. The developed model can be used to support product development, reformulation or risk assessment for spreadable processed cheese.

New Term to Quantify the Effect of Temperature on pH min -Values Used in Cardinal Parameter Growth Models for Listeria monocytogenes

The aim of this study was to quantify the influence of temperature on pH_min-values of Listeria monocytogenes as used in cardinal parameter growth models and thereby improve the prediction of growth for this pathogen in food with low pH. Experimental data for L. monocytogenes growth in broth at different pH-values and at different constant temperatures were generated and used to determined pH_min-values. Additionally, pH_min-values for L. monocytogenes available from literature were collected. A new pH_min-function was developed to describe the effect of temperatures on pH_min-values obtained experimentally and from literature data. A growth and growth boundary model was developed by substituting the constant pH_min-value present in the Mejlholm and Dalgaard (2009) model (J. Food. Prot. 72, 2132–2143) by the new pH_min-function. To obtain data for low pH food, challenge tests were performed with L. monocytogenes in commercial and laboratory-produced chemically acidified cheese including glucono-delta-lactone (GDL) and in commercial cream cheese. Furthermore, literature data for growth of L. monocytogenes in products with or without GDL were collected. Evaluation of the new and expanded model by comparison of observed and predicted μ_max-values resulted in a bias factor of 1.01 and an accuracy factor of 1.48 for a total of 1,129 growth responses from challenge tests and literature data. Growth and no-growth responses of L. monocytogenes in seafood, meat, non-fermented dairy products, and fermented cream cheese were 90.3% correctly predicted with incorrect predictions being 5.3% fail-safe and 4.4% fail-dangerous. The new pH_min-function markedly extended the range of applicability of the Mejlholm and Dalgaard (2009) model from pH 5.4 to pH 4.6 and therefore the model can now support product development, reformulation or risk assessment of food with low pH including chemically acidified cheese and cream cheese.

Combining Quantitative Risk Assessment of Human Health, Food Waste, and Energy Consumption: The Next Step in the Development of the Food Cold Chain?

The preservation of perishable food via refrigeration in the supply chain is essential to extend shelf life and provide consumers with safe food. However, electricity consumed in refrigeration processes has an economical and an environmental impact. This study focuses on the cold chain of cooked ham, including transport, cold room in supermarket, display cabinet, transport by consumer, and domestic refrigerator, and aims to predict the risk for human health associated with Listeria monocytogenes, the amount of food wasted due to the growth of spoilage bacteria, and the electrical consumption to maintain product temperature through the cold chain. A set of eight intervention actions were tested to evaluate their impact on the three criteria. Results show that the modification of the thermostat of the domestic refrigerator has a high impact on food safety and food waste and a limited impact on the electrical consumption. Inversely, the modification of the airflow rate in the display cabinet has a high impact on electrical consumption and a limited impact on food safety and food waste. A cost-benefit analysis approach and two multicriteria decision analysis methods were used to rank the intervention actions. These three methodologies show that setting the thermostat of the domestic refrigerator to 4 °C presents the best compromise between the three criteria. The impact of decisionmaker preferences (criteria weight) and limitations of these three approaches are discussed. The approaches proposed by this study may be useful in decision making to evaluate global impact of intervention actions in issues involving conflicting outputs.

Modeling Growth of Listeria and Lactic Acid Bacteria in Food Environments

Predictive food microbiology models can facilitate the assessment and management of microbial food safety. Importantly, the combined effect of storage conditions and product characteristics can be predicted by successfully validated models. This makes it easier and faster to develop or reformulation safe food recipes and predictions can be used to documents safety of available foods. The effect of various product characteristics and storage conditions must be taken into account and extensive mathematical models including the effect of these environmental factors are needed. Here the development, evaluation and application of an extensive growth and growth boundary model for Listeria monocytogenes including the effect of 12 environmental factors as well as the growth dampening effect of lactic acid bacteria is described. The Food Spoilage and Safety Predictor software is used to illustrate how predictions can be applied.

New insights on Listeria monocytogenes growth in pressurised cooked ham: A piezo-stimulation effect enhanced by organic acids during storage

The aim of the present study was to understand growth and survival responses of Listeria monocytogenes during the storage of high pressure processed (HPP) cooked ham formulated with organic acids to inhibit growth of the pathogen. Cooked ham batches were manufactured without organic acids (control), with potassium lactate (2.8% or 4%) or with potassium lactate and sodium diacetate (2.0% + 0.11% or 2.0% + 0.45%). Products were aseptically sliced and inoculated with 10⁷ cfu/g or 10² cfu/g of either L. monocytogenes CTC1034 (a meat isolate) or a cocktail of three isolates (12MOB045Lm, 12MOB089Lm and Scott A). Vacuum-packed samples with 10⁷ cfu/g were HPP at 600 MPa for 3 min, whereas samples with 10² cfu/g were not HPP. Growth or survival of L. monocytogenes was determined during subsequent storage at 8, 12 and 20 °C. Growth or survival was characterized by fitting the experimental data using the primary logistic model and the log-linear with shoulder model, respectively. Secondary models were fitted to characterize the effect of temperature on growth kinetic parameters without or with HPP. For cooked ham without organic acids, growth rates of L. monocytogenes were slightly increased by HPP and lag times were longer. Interestingly, for cooked ham with organic acids, the HPP had a significant stimulating effect on subsequent growth of L. monocytogenes (piezo-stimulation). At 20 °C, the growth rates of L. monocytogenes in cooked ham with lactate were up to 4-fold higher than those of the same product without HPP. The observed enhancement of the piezo-stimulating effect of organic acids on growth rates during storage of HPP cooked ham represents a challenge for the use of organic acids as antimicrobials in these products. A predictive model available as part of the Food Spoilage and Safety Predictor (FSSP) software seemed useful to predict growth and growth boundary of L. monocytogenes in non-pressurised cooked ham. This model was calibrated to take into account the observed piezo-stimulating effect and to predict growth of L. monocytogenes in HPP cooked ham with organic acids.

Modeling the Combined Effects of Temperature, pH, and Sodium Chloride and Sodium Lactate Concentrations on the Growth Rate of Lactobacillus plantarum ATCC 8014

Nowadays, microorganisms with probiotic or antimicrobial properties are receiving major attention as alternative resources for food preservation. Lactic acid bacteria are able to synthetize compounds with antimicrobial activity against pathogenic and spoilage flora. Among them, Lactobacillus plantarum ATCC 8014 has exhibited this capacity, and further studies reveal that the microorganism is able to produce bacteriocins. An assessment of the growth of L. plantarum ATCC 8014 at different conditions becomes crucial to predict its development in foods. A response surface model of the growth rate of L. plantarum was built in this study as a function of temperature (4, 7, 10, 13, and 16°C), pH (5.5, 6.0, 6.5, 7.0, and 7.5), and sodium chloride (0, 1.5, 3.0, 4.5, and 6.0%) and sodium lactate (0, 1, 2, 3, and 4%) concentrations. All the factors were statistically significant at a confidence level of 90%  (p<0.10). When temperature and pH increased, there was a corresponding increase in the growth rate, while a negative relationship was observed between NaCl and Na-lactate concentrations and the growth parameter. A mathematical validation was carried out with additional conditions, demonstrating an excellent performance of the model. The developed model could be useful for designing foods with L. plantarum ATCC 8014 added as a probiotic.

Reducing Salt in Raw Pork Sausages Increases Spoilage and Correlates with Reduced Bacterial Diversity

Raw sausages are perishable foodstuffs; reducing their salt content raises questions about a possible increased spoilage of these products. In this study, we evaluated the influence of salt reduction (from 2.0% to 1.5% [wt/wt]), in combination with two types of packaging (modified atmosphere [50% mix of CO2-N2] and vacuum packaging), on the onset of spoilage and on the diversity of spoilage-associated bacteria. After 21 days of storage at 8°C, spoilage was easily observed, characterized by noticeable graying of the products and the production of gas and off-odors defined as rancid, sulfurous, or sour. At least one of these types of spoilage occurred in each sample, and the global spoilage intensity was more pronounced in samples stored under modified atmosphere than under vacuum packaging and in samples with the lower salt content. Metagenetic 16S rRNA pyrosequencing revealed that vacuum-packaged samples contained a higher total bacterial richness (n = 69 operational taxonomic units [OTUs]) than samples under the other packaging condition (n = 46 OTUs). The core community was composed of 6 OTUs (Lactobacillus sakei, Lactococcus piscium, Carnobacterium divergens, Carnobacterium maltaromaticum, Serratia proteamaculans, and Brochothrix thermosphacta), whereas 13 OTUs taxonomically assigned to the Enterobacteriaceae, Enterococcaceae, and Leuconostocaceae families comprised a less-abundant subpopulation. This subdominant community was significantly more abundant when 2.0% salt and vacuum packaging were used, and this correlated with a lower degree of spoilage. Our results demonstrate that salt reduction, particularly when it is combined with CO2-enriched packaging, promotes faster spoilage of raw sausages by lowering the overall bacterial diversity (both richness and evenness).

IMPORTANCE

Our study takes place in the context of raw meat product manufacturing and is linked to a requirement for salt reduction. Health guidelines are calling for a reduction in dietary salt intake. However, salt has been used for a very long time as a hurdle technology, and salt reduction in meat products raises the question of spoilage and waste of food. The study was conceived to assess the role of sodium chloride reduction in meat products, both at the level of spoilage development and at the level of bacterial diversity, using 16S rRNA amplicon sequencing and raw pork sausage as a meat model.

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

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

Modelling the effect of lactic acid bacteria from starter- and aroma culture on growth of Listeria monocytogenes in cottage cheese

Four mathematical models were developed and validated for simultaneous growth of mesophilic lactic acid bacteria from added cultures and Listeria monocytogenes, during chilled storage of cottage cheese with fresh- or cultured cream dressing. The mathematical models include the effect of temperature, pH, NaCl, lactic- and sorbic acid and the interaction between these environmental factors. Growth models were developed by combining new and existing cardinal parameter values. Subsequently, the reference growth rate parameters (μref at 25°C) were fitted to a total of 52 growth rates from cottage cheese to improve model performance. The inhibiting effect of mesophilic lactic acid bacteria from added cultures on growth of L. monocytogenes was efficiently modelled using the Jameson approach. The new models appropriately predicted the maximum population density of L. monocytogenes in cottage cheese. The developed models were successfully validated by using 25 growth rates for L. monocytogenes, 17 growth rates for lactic acid bacteria and a total of 26 growth curves for simultaneous growth of L. monocytogenes and lactic acid bacteria in cottage cheese. These data were used in combination with bias- and accuracy factors and with the concept of acceptable simulation zone. Evaluation of predicted growth rates of L. monocytogenes in cottage cheese with fresh- or cultured cream dressing resulted in bias-factors (Bf) of 1.07-1.10 with corresponding accuracy factor (Af) values of 1.11 to 1.22. Lactic acid bacteria from added starter culture were on average predicted to grow 16% faster than observed (Bf of 1.16 and Af of 1.32) and growth of the diacetyl producing aroma culture was on average predicted 9% slower than observed (Bf of 0.91 and Af of 1.17). The acceptable simulation zone method showed the new models to successfully predict maximum population density of L. monocytogenes when growing together with lactic acid bacteria in cottage cheese. 11 of 13 simulations of L. monocytogenes growth were within the acceptable simulation zone, which demonstrated good performance of the empirical inter-bacterial interaction model. The new set of models can be used to predict simultaneous growth of mesophilic lactic acid bacteria and L. monocytogenes in cottage cheese during chilled storage at constant and dynamic temperatures. The applied methodology is likely to be applicable for safety prediction of other types of fermented and unripened dairy products where inhibition by lactic acid bacteria is important for growth of pathogenic microorganisms.

Development and validation of a stochastic model for potential growth of Listeria monocytogenes in naturally contaminated lightly preserved seafood

A new stochastic model for the simultaneous growth of Listeria monocytogenes and lactic acid bacteria (LAB) was developed and validated on data from naturally contaminated samples of cold-smoked Greenland halibut (CSGH) and cold-smoked salmon (CSS). During industrial processing these samples were added acetic and/or lactic acids. The stochastic model was developed from an existing deterministic model including the effect of 12 environmental parameters and microbial interaction (O. Mejlholm and P. Dalgaard, Food Microbiology, submitted for publication). Observed maximum population density (MPD) values of L. monocytogenes in naturally contaminated samples of CSGH and CSS were accurately predicted by the stochastic model based on measured variability in product characteristics and storage conditions. Results comparable to those from the stochastic model were obtained, when product characteristics of the least and most preserved sample of CSGH and CSS were used as input for the existing deterministic model. For both modelling approaches, it was shown that lag time and the effect of microbial interaction needs to be included to accurately predict MPD values of L. monocytogenes. Addition of organic acids to CSGH and CSS was confirmed as a suitable mitigation strategy against the risk of growth by L. monocytogenes as both types of products were in compliance with the EU regulation on ready-to-eat foods.

Identification of the significant factors in food safety using global sensitivity analysis and the accept-and-reject algorithm: application to the cold chain of ham

Deterministic models describing heat transfer and microbial growth in the cold chain are widely studied. However, it is difficult to apply them in practice because of several variable parameters in the logistic supply chain (e.g., ambient temperature varying due to season and product residence time in refrigeration equipment), the product's characteristics (e.g., pH and water activity) and the microbial characteristics (e.g., initial microbial load and lag time). This variability can lead to different bacterial growth rates in food products and has to be considered to properly predict the consumer's exposure and identify the key parameters of the cold chain. This study proposes a new approach that combines deterministic (heat transfer) and stochastic (Monte Carlo) modeling to account for the variability in the logistic supply chain and the product's characteristics. The model generates a realistic time-temperature product history , contrary to existing modeling whose describe time-temperature profile Contrary to existing approaches that use directly a time-temperature profile, the proposed model predicts product temperature evolution from the thermostat setting and the ambient temperature. The developed methodology was applied to the cold chain of cooked ham including, the display cabinet, transport by the consumer and the domestic refrigerator, to predict the evolution of state variables, such as the temperature and the growth of Listeria monocytogenes. The impacts of the input factors were calculated and ranked. It was found that the product's time-temperature history and the initial contamination level are the main causes of consumers' exposure. Then, a refined analysis was applied, revealing the importance of consumer behaviors on Listeria monocytogenes exposure.