Modelling the interaction of storage temperature, pH, and water activity on the growth behaviour of Listeria monocytogenes in raw and pasteurised semi-soft rind washed milk cheese during storage following ripening

An alternative approach for controlling bacterial pathogens in liquid and solid poultry waste using Calcium hypochlorite Ca(OCl)2 disinfectant-based silver nanoparticles

Inappropriate handling of poultry waste from the beginning to the end of the production cycle could lead to health and environmental hazards. The purpose of this study was to assess the current state of poultry waste management practices as well as to evaluate the efficacy of disinfectants (VIRKON S, Quaternary ammonium compound (QAC), Calcium hypochlorite [Ca(OCl)₂], and nanomaterials (nano-silver particles (Ag NPs), and Ca(OCl)₂-Ag NPs composite) on pathogenic bacteria for use in the disinfection of waste collection areas within poultry operation systems. Two hundred and ten samples were gathered from variant waste types for isolation and identification of pathogenic bacteria. Then, the efficacy of some disinfectants against fifty strains of isolated bacteria was evaluated using a broth micro-dilution assay. Results showed the most predominant bacterial isolates from wastes were E. coli (33.69%), Salmonella spp. (26.09%), followed by K. pneumonae (15.22%) and L. monocytogenes (14.13%). Ca(OCl)₂-Ag NPs had a microbial lethal effect against all pathogenic bacteria (100%) that were isolated from liquid and solid waste. In conclusion, poultry manure waste is collected and dumped on the agricultural land around those chicken farms without any treatment. The Ca(OCl)₂-Ag NPs composite was lethal to all pathogenic microbes isolated from waste and their collected areas at 1.0 mg/L concentration.

Quantitative risk assessment model to investigate the public health impact of varying Listeria monocytogenes allowable levels in different food commodities: A retrospective analysis

Invasive listeriosis is a potentially fatal foodborne disease that according to this study may affect up to 32.9 % of the US population considered as increased risk and including people with underlying conditions and co-morbidities. Listeria monocytogenes has been scrutinized in research and surveillance programs worldwide in Ready-to-Eat (RTE) food commodities (RTE salads, deli meats, soft/semi-soft cheese, seafood) and frozen vegetables in the last 30 years with an estimated overall prevalence of 1.4-9.9 % worldwide (WD) and 0.5-3.8 % in the United States (US). Current L. monocytogenes control efforts have led to a prevalence reduction in the last 5 years of 4.9-62.9 % (WD) and 12.4-92.7 % (US). A quantitative risk assessment model was developed, estimating the probability of infection in the US susceptible population to be 10-10,000× higher than general population and the total number of estimated cases in the US was 1044 and 2089 cases by using the FAO/WHO and Pouillot dose-response models. Most cases were attributed to deli meats (>90 % of cases) followed by RTE salads (3.9-4.5 %), soft and semi-soft cheese and RTE seafood (0.5-1.0 %) and frozen vegetables (0.2-0.3 %). Cases attributed to the increased risk population corresponded to 96.6-98.0 % of the total cases with the highly susceptible population responsible for 46.9-80.1 % of the cases. Removing product lots with a concentration higher than 1 CFU/g reduced the prevalence of contamination by 15.7-88.3 % and number of cases by 55.9-100 %. Introducing lot-by-lot testing and defining allowable quantitative regulatory limits for low-risk RTE commodities may reduce the public health impact of L. monocytogenes and improve the availability of enumeration data.

Modelling the Effect of Salt Concentration on the Fate of Listeria monocytogenes Isolated from Costa Rican Fresh Cheeses

“Turrialba cheese” is a Costa Rican fresh cheese highly appreciated due to its sensory characteristics and artisanal production. As a ready-to-eat dairy product, its formulation could support Listeria monocytogenes growth. L. monocytogenes was isolated from 14.06% of the samples and the pathogen was able to grow under all tested conditions. Due to the increasing demand for low-salt products, the objective of this study was to determine the effect of salt concentration on the growth of pathogen isolates obtained from local cheese. Products from retail outlets in Costa Rica were analyzed for L. monocytogenes. These isolates were used to determine growth at 4 °C for different salt concentration (0.5–5.2%). Kinetic curves were built and primary and secondary models developed. Finally, a validation study was performed using literature data. The R² and Standard Error of fit of primary models were ranked from 0.964–0.993, and 0.197–0.443, respectively. An inverse relationship was observed between growth rate and salt concentration. A secondary model was obtained, with R² = 0.962. The model was validated, and all values were B_f > 1, thus providing fail-safe estimations. These data were added to the free and easy-to-use predictive microbiology software “microHibro” which is used by food producers and regulators to assist in decision-making.

From Cheese-Making to Consumption: Exploring the Microbial Safety of Cheeses through Predictive Microbiology Models

Cheeses are traditional products widely consumed throughout the world that have been frequently implicated in foodborne outbreaks. Predictive microbiology models are relevant tools to estimate microbial behavior in these products. The objective of this study was to conduct a review on the available modeling approaches developed in cheeses, and to identify the main microbial targets of concern and the factors affecting microbial behavior in these products. Listeria monocytogenes has been identified as the main hazard evaluated in modelling studies. The pH, a_w, lactic acid concentration and temperature have been the main factors contemplated as independent variables in models. Other aspects such as the use of raw or pasteurized milk, starter cultures, and factors inherent to the contaminating pathogen have also been evaluated. In general, depending on the production process, storage conditions, and physicochemical characteristics, microorganisms can grow or die-off in cheeses. The classical two-step modeling has been the most common approach performed to develop predictive models. Other modeling approaches, including microbial interaction, growth boundary, response surface methodology, and neural networks, have also been performed. Validated models have been integrated into user-friendly software tools to be used to obtain estimates of microbial behavior in a quick and easy manner. Future studies should investigate the fate of other target bacterial pathogens, such as spore-forming bacteria, and the dynamic character of the production process of cheeses, among other aspects. The information compiled in this study helps to deepen the knowledge on the predictive microbiology field in the context of cheese production and storage.

Dynamic kinetic analysis of growth of Listeria monocytogenes in pasteurized cow milk

The objective of this study was to develop a dynamic model for predicting the growth of Listeria monocytogenes in pasteurized cow milk under fluctuating temperature conditions during storage and temperature abuse. Six dynamic temperature profiles that simulated random fluctuation patterns were designed to change arbitrarily between 4 and 30°C. The growth data collected from 3 independent temperature profiles were used to determine the kinetic parameters and construct a growth model combining the primary and secondary models using a 1-step dynamic analysis method. The results showed that the estimated minimum growth temperature and maximum cell concentration were 0.6 ± 0.2°C and 7.8 ± 0.1 log cfu/mL (mean ± standard error), with the root mean square error (RMSE) only 0.3 log cfu/mL for model development. The model and the associated kinetic parameters were validated using the data collected under both dynamic and isothermal conditions, which were not used for model development, to verify the accuracy of prediction. The RMSE of prediction was approximately 0.3 log cfu/mL for fluctuating temperature profiles, and it was between 0.2 and 1.1 log cfu/mL under certain isothermal temperatures (2-30°C). The resulting model and kinetic parameters were further validated using 3 growth curves at 4, 7, and 10°C arbitrarily selected from ComBase (www.combase.cc). The RMSE of prediction was 0.8, 0.4, and 0.5 log cfu/mL, respectively, for these curves. The validation results indicated the predictive model was reasonably accurate, with relatively small RMSE. The model was then used to simulate the growth of L. monocytogenes under a variety of continuous and square-wave temperature profiles to demonstrate its potential application. The results of this study showed that the model developed in this study can be used to predict the growth of L. monocytogenes in contaminated milk during storage.

Drug-susceptibility, biofilm-forming ability and biofilm survival on stainless steel of Listeria spp. strains isolated from cheese

The aim of the study was to analyze the contamination of mold cheese (Brie, Camembert, Gorgonzola, Munster and Roquefort) with Listeria spp. and assessment of culturable cells number recovered from the biofilm formed on the surface of stainless steel by obtained strains. Identified isolates (MALDI TOF MS technique) were subjected to susceptibility testing (disk-diffusion method) and their genetic similarity (PFGE method), ability to form biofilm (quantitative method), biofilm dry weight, and biofilm survival on stainless steel were evaluated. Out of 250 samples of cheese 26 (10.4%) were Listeria spp. positive, including 15 isolates (6.0% of samples) of L. monocytogenes, 7 isolates of L. innocua (2.8% of samples) and 4 isolates of L. welshimeri species (1.6% of samples). Of the 26 isolates tested, 22 strains were genetically different. It was shown that L. innocua and L. welshimeri strains were sensitive to all antibiotics tested, while two (16.7%) L. monocytogenes strains were resistant to penicillin and one (8.3%) to erythromycin. L. monocytogenes formed biofilm most intensively on stainless steel, while L. welshimeri the least effectively. The median of bacteria number recovered from the biofilm for L. monocytogenes was 6.81 log CFU × cm^-2, for L. innocua - 5.63 log CFU × cm^-2, and for L. welshimeri - 4.93 log CFU × cm^-2. The survival in the biofilm of Listeria spp. strains decreased along with the increase in a storage temperature of steel coupons. The longest survival time was reported at 4 °C, i.e. 47.58-124.41 days, with an elimination rate of 0.06-0.13 log CFU × day^-1. Collectively, L. monocytogenes is the most prevalent species of Listeria genus in the mold cheese. The ability of L. monocytogenes strains to form biofilm on stainless steel and survive in the food processing environment increases chance of the secondary contamination of food posing risk to the consumer health.

Novel approach for controlling resistant Listeria monocytogenes to antimicrobials using different disinfectants types loaded on silver nanoparticles (AgNPs)

A combined use of silver nanoparticles (AgNPs) with different types of disinfectants as antimicrobial might be useful in mitigating the problem of development of bacterial resistance with a strong enhancement of the biocidal effect of disinfectants. To evaluate the biocidal activity of silver nanoparticles and its loaded forms, five commercial disinfectants (quaternary ammonium compounds (benzalkonium chloride (BC) and TH⁴⁺), Virkon®S, sodium hypochlorite, and hydrogen peroxide (H₂O₂)) were used against Listeria monocytogenes (L. monocytogenes) isolates at different concentrations and exposure times to reveal intra-species variability and the percentage of resistance to antimicrobial agents used. Therefore, a total of 260 specimens from animal and human stool as well as environmental samples from dairy cattle farms were cultured for isolation of L. monocytogenes. Thereafter, bacterial isolates were identified using PCR. Silver nanoparticle was synthesized using chemical reduction. Both silver nanoparticles and its loaded forms were characterized by transmission electron microscopy (TEM). The sensitivity test of 60 strains of L. monocytogenes bacteria to AgNPs and its loaded forms was evaluated using broth macrodilution method. Virkon®S/AgNPs 2.0% exhibited the highest bactericidal effect (100%) against L. monocytogenes strains followed by H₂O₂/AgNPs 5.0% and TH⁴⁺/AgNPs 1.0% (90% each). Furthermore, the percentage of resistance of L. monocytogenes was 0.0% to both H₂O₂/AgNPs 5.0% and Virkon®S/AgNPs 2.0%. In conclusion, monitoring the main source of contamination with Listeria monocytogenes in dairy cattle farms is an essential factor to achieve an efficient control. Moreover, the use of the disinfectants, Virkon®S 2.0%, H₂O₂ 5.0%, and TH⁴⁺1.0%, loaded on silver nanoparticles composite had the strong bactericidal effect against L. monocytogenes.

Growth behavior comparison of Listeria monocytogenes between Type strains and beef isolates in raw beef

This study was conducted to compare the growth parameters of Listeria monocytogenes between beef isolates and Type strains in raw beef. Beef was artificially inoculated with 3 Log CFU/g levels and growth was measured during storage at various temperatures (5-25 °C) using conventional plating methods. The R2 value for lag time (λ) and specific growth rate (μ) were determined using modified-Gompertz model, which were greater than 0.98 at all storage temperature except at 5 °C. B f , A f , and RMSE showed acceptable ranges, showed that the models are suitable for the modeling the growth of L. monocytogenes. At all temperatures, the λ of L. monocytogenes beef isolates was shorter than that of the L. monocytogenes Type strains, and the μ of beef isolates was higher than that of Type strains. These results showed that growth pattern prediction of beef inoculated with L. monocytogenes beef isolates gives more actual results than with Type strains.

Predicting and Modelling the Growth of Potentially Pathogenic Bacteria in Coalho Cheese

Coalho is a semihard medium- to high-moisture cheese produced in various states in the northeastern region of Brazil. This study was conducted to predict the growth kinetics (maximum growth rate, Grmax) of Escherichia coli, Listeria monocytogenes, Salmonella, and Staphylococcus aureus using the ComBase predictor with various combinations of temperature, pH, and water activity (a_w) in commercial Coalho cheese samples. The growth of two antibiotic-resistant derivative strains of L. monocytogenes (parental strains ATCC 19115 and ATCC 7644) and S. aureus (parental strains ATCC 13565 and ATCC 19095) was measured in commercial Coalho cheese samples during 14 days of storage as a function of the initial contamination level (3 and 5 log CFU/g) and storage temperature (7.5 and 12°C). The highest Grmax values predicted by ComBase under the various conditions of temperature, pH, and a_w were for L. monocytogenes (0.006 to 0.065 log CFU/g/h) and S. aureus (0.003 to 0.048 log CFU/g/h). The Grmax values predicted by ComBase for E. coli and Salmonella were 0.007 to 0.026 and 0.008 to 0.041 log CFU/g/h, respectively. An experimental challenge in Coalho cheese revealed that the populations of all tested antibiotic-resistant derivative strains of L. monocytogenes and S. aureus increased (>0.5 log CFU/g) by day 14 of storage at 7.5 or 12°C. L. monocytogenes and S. aureus had higher Grmax values in cheese samples stored at 12°C than those stored at 7.5°C. The ComBase growth predictions under the temperature, pH, and a_w conditions in commercial Coalho cheese samples were generally fail-safe for predicting the growth of L. monocytogenes and S. aureus in the actual product. These results indicate that Coalho cheese has pH and a_w characteristics that allow the growth of E. coli, L. monocytogenes, Salmonella, and S. aureus. These cheeses are typically stored at temperatures that do not prevent the growth of these bacteria.

Growth and Modeling of Staphylococcus aureus in Flour Products under Isothermal and Nonisothermal Conditions

This study was conducted to investigate the growth of Staphylococcus aureus in traditional Chinese flour products under isothermal (10, 15, 20, 25, 30, and 37°C) and nonisothermal (10 to 20, 20 to 30, and 25 to 37°C) conditions. Then, models for the growth of S. aureus in flour products as a function of storage temperature, pH, and water activity (a_w) were developed, and the goodness of fit of models was evaluated using the determination coefficient (R²), root mean square error (RMSE), bias factor (B_f), and accuracy factor (A_f). Based on the above information, S. aureus growth in steamed bread under nonisothermal conditions was predicted from experiments performed under isothermal conditions. It was shown that different combinations of temperature and a_w in flour products have a strong influence on the growth of S. aureus . The modified Gompertz model was found to be more suitable for describing the growth data of S. aureus in flour products, with an R² of >0.99 and an RMSE of <0.37. The newly developed secondary models were validated, and for the specific growth rate and the lag time, the R² values were 0.96 and 0.97, A_f was 1.12 and 1.06, and B_f was 1.13 and 1.05, respectively. The predicted nonisothermal growth curves of S. aureus were in agreement with the reported experimental ones, with RMSE <0.29, A_f value 1.02 to 1.09, and B_f value 0.92 to 0.99. These results indicated that the predictive models provided useful information for the establishment of safety standards and a risk assessment for S. aureus in flour products.

Modeling the Effect of Storage Temperatures on the Growth of Listeria monocytogenes on Ready-to-Eat Ham and Sausage

The aim of this study was to model the growth kinetics of Listeria monocytogenes on ready-to-eat ham and sausage at different temperatures (4 to 35°C). The observed data fitted well with four primary models (Baranyi, modified Gompertz, logistic, and Huang) with high coefficients of determination (R(2) > 0.98) at all measured temperatures. After the mean square error (0.009 to 0.051), bias factors (0.99 to1.06), and accuracy factors (1.01 to 1.09) were obtained in all models, the square root and the natural logarithm model were employed to describe the relation between temperature and specific growth rate (SGR) and lag time (LT) derived from the primary models. These models were validated against the independent data observed from additional experiments using the acceptable prediction zone method and the proportion of the standard error of prediction. All secondary models based on each of the four primary models were acceptable to describe the growth of the pathogen in the two samples. The validation results indicate that the optimal primary model for estimating the SGR was the Baranyi model, and the optimal primary model for estimating LT was the logistic model in ready-to-eat (RTE) ham. The Baranyi model was also the optimal model to estimate the SGR and LT in RTE sausage. These results could be used to standardize predictive models, which are commonly used to identify critical control points in hazard analysis and critical control point systems or for the quantitative microbial risk assessment to improve the food safety of RTE meat products.

Farm to Fork Quantitative Risk Assessment of Listeria monocytogenes Contamination in Raw and Pasteurized Milk Cheese in Ireland

The objective of this study was to model and quantify the level of Listeria monocytogenes in raw milk cheese (RMc) and pasteurized milk cheese (PMc) from farm to fork using a Bayesian inference approach combined with a quantitative risk assessment. The modeling approach included a prediction of contamination arising from the farm environment as well from cross-contamination within the cheese-processing facility through storage and subsequent human exposure. The model predicted a high concentration of L. monocytogenes in contaminated RMc (mean 2.19 log10 CFU/g) compared to PMc (mean -1.73 log10 CFU/g). The mean probability of illness (P1 for low-risk population, LR) and (P2 for high-risk population, HR, e.g., immunocompromised) adult Irish consumers following exposure to contaminated cheese was 7 × 10(-8) (P1 ) and 9 × 10(-4) (P2 ) for RMc and 7 × 10(-10) (P1 ) and 8 × 10(-6) (P2 ) for PMc, respectively. In addition, the model was used to evaluate performance objectives at various stages, namely, the cheese making and ripening stages, and to set a food safety objective at the time of consumption. A scenario analysis predicted various probabilities of L. monocytogenes contamination along the cheese-processing chain for both RMc and PMc. The sensitivity analysis showed the critical factors for both cheeses were the serving size of the cheese, storage time, and temperature at the distribution stage. The developed model will allow food processors and policymakers to identify the possible routes of contamination along the cheese-processing chain and to reduce the risk posed to human health.