Time–temperature profiles of chilled ready-to-eat foods in school catering and probabilistic analysis of Listeria monocytogenes growth

Assessing refrigerated preservation performance using Listeria predictive microbiology models and temperature data: Refrigerator performance indicator and time-temperature equivalent

Time-temperature data for queso fresco (QF) cheese varieties stored in a residential refrigerator operating at 5°C and a predictive microbiology secondary model for Listeria monocytogenes in QF were used to estimate a refrigerator performance indicator (RPI) of microbial preservation. RPI values were used to assess how compressor technology (single [SS] and variable speed [VS]), ambient temperature (21.1°C [LT] and 32.2°C [HT]), and refrigerator load (22.5 kg regular load and 39 kg higher load) affected preservation performance. All deterministic and probabilistic RPI estimations slightly exceeded the desirable 1.0 value, i.e., the variable temperatures for the QF kept in the refrigerator were worse than keeping it constantly at the temperature recommended by food safety agencies for QF. Furthermore, the mean comparison of estimates of the time-temperature equivalent indicator previously developed by French researchers showed similar behavior to those observed for RPI. Finally, statistical analysis showed that Tambient was the factor with the highest impact on refrigerator performance because of its impact on the sample temperature increase during door openings and when exposed to ambient temperature during product use. This highlights the need to reduce the time for product temperature recovery by improving the compressor operation logic. Also important are consumer behavior changes such as a reduction in product exposure to ambient temperature and in the door opening duration and frequency. PRACTICAL APPLICATION: This study demonstrated how a quantitative tool (RPI) can assess refrigerator preservation performance. Although the findings presented can be applied to any cold chain segment, the data used was collected for its weakest link, the domestic refrigerator. Surveys show that 77% of them operate above the recommended 4°C. The RPI methodology is ready for use by refrigerator designers to assess performance improvements possible by modifications of the compressor operation logic. Moreover, it can be integrated into smart-hubs monitoring the frequency and duration of refrigerator door openings to inform consumers when their habits are compromising the preservation performance of the refrigerator.

Ambient temperature, refrigerator food load, and door openings effect on a preservation performance indicator based on chicken temperature data and predictive microbiology

Home preservation depends on the food matrix, refrigerator design/technology, consumer actions, and ambient temperature. Storing different food matrices in product-relevant refrigerator locations generating different temperature histories can be used to develop an indicator of how refrigerator technology, consumer habits, and environment conditions impact the refrigerator food preservation performance. In this study, poultry, particularly prone to spoilage reflecting its pH, nutrient availability, and high aw, was used to evaluate refrigerator preservation performance as affected by compressor technology (single [SS] and variable speed [VS]), ambient temperature (21.1°C [LT] and 32.2°C [HT]), and refrigerator load (22.5 kg [RL] and 39 kg [HL]). Time-temperature values collected for chicken breast stored in a drawer independently controlled at 0°C in a refrigerator set 5°C, and a Pseudomonas predictive microbiology model, were used to estimate a normalized refrigerator performance indicator (RPI). Values <1, ∼1, and >1 described excellent, good, or poor performance, respectively. A first analysis revealed that up to 54% of chicken breast temperatures were above its recommended refrigerated storage value. When ignoring variability sources, SS technology yielded RPI values ranging 0.61-0.70, whereas the more energy efficient VS compressor yielded values ranging 0.86-1.14. The higher and wider VS RPI range reflects a compressor control logic optimized for energy efficiency compliance while disregarding effects on food preservation. When considering the variability of model parameters and temperature measurements through one-sided 95% confidence intervals yielded RPI reaching 1.16. Although the independently controlled drawer preservation performance was near optimal, it can improve by considering energy use and preservation impact when optimizing the compressor speed control protocol. PRACTICAL APPLICATION: Worldwide poultry meat consumption has reached 15 kg per person. Refrigeration is widely used for its safety and quality preservation. Efficiency regulations decreased the energy use of residential refrigerators by nearly tenfold even though their size increased by 50% in the last half century. In this study, we provide quantitative evidence that their preservation performance must be improved. This is particularly true for upper end units typically equipped with quieter and more energy-efficient variable speed compressors. The same methodology can be used to evaluate the preservation performance of the storage units, trucks, and display cases used for refrigerated products.

Food Safety and Sanitation Implementation Impasse on Adolescents in Kenyan High Schools

The ability to combat food-borne illnesses in food facilities and institutional catering units require sufficient knowledge on food safety and sanitation standards by food producers and consumers. The aim of the study was to investigate the food safety and sanitation knowledge of food handlers in Kenyan high schools. A cross-sectional study was carried out among 204 food handlers in 50 schools. Questions about knowledge and practice toward food safety and sanitation were asked. Respondents were the most knowledgeable on food contamination (93%), while participants were the least knowledgeable on the importance of protective attire when distributing foods to learners (50%). One-way ANOVA revealed a significant difference between gender and food handlers' behavior and practice (F = 19.886, ρ = 0.00 < 0.05) as well as between job tenure and practice of food safety and sanitation (F = 17.874, ρ = 0.00 < 0.05). Multiple regression analysis established that knowledge contributed to 44.1% of the behavior and practice of the food handlers. It is concluded that food handlers have a fair knowledge despite lack of training, motivation, and facilities to maintain quality standards. It is recommended that the Kenyan Government develop and implement guidelines through school feeding policy that would ensure that food safety and sanitation practices are implemented and utilized by Kenyan high schools.

Determination of Temperature Dependent Growth Parameters in Psychrotrophic Pathogen Bacteria and Tentative Use of Mean Kinetic Temperature for the Microbiological Control of Food

Temperature is the main factor to control the microbial growth in perishable foods. The psychrotrophic pathogen bacteria are microorganisms of concern for food products with extended shelf life in chilling conditions. The aims of this work were two. Firstly, to evaluate growth behavior of Aeromonas hydrophila DSM-30187, Listeria monocytogenes DSM-20600, and Yersinia enterocolitica DSM-27689 strains, at different temperatures (4, 7, 10, 15, 25, and 30°C) and starting cell concentrations (10 and 106 CFU/mL), in order to determine the activation energies (E a) of the relevant lag phases and growth rates. Secondly, to investigate if Mean Kinetic Temperature (MKT) might be applied in recording temperature devices to alert a thermal abuse in a management control system for food safety. As expected, lag phase and growth rate proved to be heavily affected by temperature whereas the inoculum size did not. The E a values involved in the duration of latent periods, calculated on the basis of the Arrhenius model, were comparable for A. hydrophila and L. monocytogenes strains (from 21.3 to 24.4 kcal/mol), while significantly differed for Y. enterocolitica (16.6 kcal/mol). The E a values of growth rates were similar for A. hydrophila and L. monocytogenes strains (from 20.9 to 21.1 kcal/mol), while were considerably lower for Y. enterocolitica (from 14.2 to 16.7 kcal/mol). The use of MKT is widespread and well-accepted in pharmaceutical field as convenient method for estimating drugs degradation in relation to storage temperature. The E a value of the lag phase found for L. monocytogenes (23.9 ± 1.2 kcal/mol) was included in the MKT formula. In this work, the air temperature of two chilling rooms was monitored during the normal operating activity in a catering company for a period of 8 months. The MKT profiles were then compared with those of mean temperatures in different conditions (short or prolonged events of thermal abuse) with the purpose to evaluate if it may be applicable to reduce false alarms without lowering the safety level of stored food.

Is the exoproteome important for bacterial pathogenesis? Lessons learned from interstrain exoprotein diversity in Listeria monocytogenes grown at different temperatures

Bacterial exoproteomes vary in composition and quantity among species and within each species, depending on the environmental conditions to which the cells are exposed. This article critically reviews the literature available on exoproteins synthesized by the foodborne pathogenic bacterium Listeria monocytogenes grown at different temperatures. The main challenges posed for exoproteome analyses and the strategies that are being used to overcome these constraints are discussed. Over thirty exoproteins from L. monocytogenes are considered, and the multifunctionality of some of them is discussed. Thus, at the host temperature of 37°C, good examples are provided by Lmo0443, a potential marker for low virulence, and by the virulence factors internalin C (InlC) and listeriolysin O (LLO). Based on the reported LLO-induced mucin exocytosis, a model is proposed for the involvement of extracellular LLO in optimizing the conditions for InlC intervention in the invasion of intestinal epithelial cells. At lower growth temperatures, exoproteins such as flagellin (FlaA) and oligopeptide permease (OppA) may explain the persistence of particular strains in the food industry environment, eventually allowing the development of new tools to eradicate L. monocytogenes, a major concern for public health.

Development of a risk-based methodology for estimating survival and growth of enteropathogenic Escherichia coli on iceberg-lettuce exposed at short-term storage in foodservice centers

Ready-to-eat lettuce is a food commodity prone to contamination by pathogenic microorganisms if processing and distribution conditions as well as handling practices are not effective. A challenge testing protocol was applied to ready-to-eat iceberg-lettuce samples by inoculating different initial contamination levels (4.5, 3.5 and 2.5 log cfu/g) of Escherichia coli strain (serotype O158:H23) subsequently stored at 8, 12, 16, 20 and 24°C for 6h. A polynomial regression model for log difference (log(diff)) was developed at each inoculum level studied through the calculation of the effective static temperature (T(eff)). Furthermore, the developed model was integrated within a risk-based approach with real time/Temperature (t/T) data collected in three Spanish foodservice centers: school canteens, long-term care facilities (LTCF) and hospitals. Statistical distributions were fitted to t/T data and estimated log(diff) values were obtained as model outputs through a Monte Carlo simulation (10,000 iterations). The results obtained at static conditions indicated that the maintenance of the lettuce at 8°C slightly reduced the E. coli population from -0.4 to -0.5 log cfu/g. However, if chill chain is not maintained, E. coli can grow up to 1.1 log cfu/g at temperatures above 16°C, even at low contamination levels. Regarding log(diff) estimated in foodservice centers, very low risk was obtained (log(diff)<1.0 log cfu in all cases). Mean T(eff) values obtained in hospitals were the lowest ones (11.1°C) and no growth of E. coli was predicted in >92% of simulated cases. The results presented in this study could serve food operators to set time/Temperature requirements for ready-to-eat foods in foodservice centers, providing a scientific basis through the use of predictive modeling. These findings may also serve to food safety managers to better define the control measures to be adopted in foodservice centers in order to prevent food-borne infections.

Transcriptome analysis of Listeria monocytogenes grown on a ready-to-eat meat matrix

The contamination of ready-to-eat (RTE) meat products with Listeria monocytogenes is a major concern for the food industry. For a better understanding of the adaptation and survival ability of L. monocytogenes grown on turkey deli meat, the transcriptome of L. monocytogenes strain F2365 was determined with a microarray. Microarray data were validated with a quantitative real-time reverse transcription PCR assay. Based on the microarray data, 39 and 45 genes from L. monocytogenes were transcriptionally upregulated and down-regulated, respectively. The genes regulated at the transcriptional level were mainly involved in energy metabolism, fatty acid and phospholipid metabolism, biosynthesis of proteins, transport and binding proteins, DNA metabolism, cellular processes, and regulatory functions. No significant change was noted for the expression of genes encoding known virulence factors such as sigB, prfA, inlA, inlB, plcA, plcB, and hly. These results suggest that L. monocytogenes grown on RTE deli meat changes its transcription of proteins involved in its metabolic pathways to obtain an energy source or to adapt to environmental change without increasing the expression of virulence factors. The global transcriptome profiles provide a better understanding of the growth or adaptation of L. monocytogenes in RTE meat products.

Quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella enterica, and Listeria monocytogenes in leafy green vegetables consumed at salad bars, based on modeling supply chain logistics

Quantitative microbial risk assessments do not usually account for the planning and ordering mechanisms (logistics) of a food supply chain. These mechanisms and consumer demand determine the storage and delay times of products. The aim of this study was to quantitatively assess the difference between simulating supply chain logistics (MOD) and assuming fixed storage times (FIX) in microbial risk estimation for the supply chain of fresh-cut leafy green vegetables destined for working-canteen salad bars. The results of the FIX model were previously published (E. Franz, S. O. Tromp, H. Rijgersberg, and H. J. van der Fels-Klerx, J. Food Prot. 73:274-285, 2010). Pathogen growth was modeled using stochastic discrete-event simulation of the applied logistics concept. The public health effects were assessed by conducting an exposure assessment and risk characterization. The relative growths of Escherichia coli O157 (17%) and Salmonella enterica (15%) were identical in the MOD and FIX models. In contrast, the relative growth of Listeria monocytogenes was considerably higher in the MOD model (1,156%) than in the FIX model (194%). The probability of L. monocytogenes infection in The Netherlands was higher in the MOD model (5.18×10(-8)) than in the FIX model (1.23×10(-8)). The risk of listeriosis-induced fetal mortality in the perinatal population increased from 1.24×10(-4) (FIX) to 1.66×10(-4) (MOD). Modeling the probabilistic nature of supply chain logistics is of additional value for microbial risk assessments regarding psychrotrophic pathogens in food products for which time and temperature are the postharvest preventive measures in guaranteeing food safety.

Quantitative microbial risk assessment for Escherichia coli O157:H7, salmonella, and Listeria monocytogenes in leafy green vegetables consumed at salad bars

Fresh vegetables are increasingly recognized as a source of foodborne outbreaks in many parts of the world. The purpose of this study was to conduct a quantitative microbial risk assessment for Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes infection from consumption of leafy green vegetables in salad from salad bars in The Netherlands. Pathogen growth was modeled in Aladin (Agro Logistics Analysis and Design Instrument) using time-temperature profiles in the chilled supply chain and one particular restaurant with a salad bar. A second-order Monte Carlo risk assessment model was constructed (using @Risk) to estimate the public health effects. The temperature in the studied cold chain was well controlled below 5 degrees C. Growth of E. coli O157:H7 and Salmonella was minimal (17 and 15%, respectively). Growth of L. monocytogenes was considerably greater (194%). Based on first-order Monte Carlo simulations, the average number of cases per year in The Netherlands associated the consumption leafy greens in salads from salad bars was 166, 187, and 0.3 for E. coli O157:H7, Salmonella, and L. monocytogenes, respectively. The ranges of the average number of annual cases as estimated by second-order Monte Carlo simulation (with prevalence and number of visitors as uncertain variables) were 42 to 551 for E. coli O157:H7, 81 to 281 for Salmonella, and 0.1 to 0.9 for L. monocytogenes. This study included an integration of modeling pathogen growth in the supply chain of fresh leafy vegetables destined for restaurant salad bars using software designed to model and design logistics and modeling the public health effects using probabilistic risk assessment software.

Modeling logistic performance in quantitative microbial risk assessment

In quantitative microbial risk assessment (QMRA), food safety in the food chain is modeled and simulated. In general, prevalences, concentrations, and numbers of microorganisms in media are investigated in the different steps from farm to fork. The underlying rates and conditions (such as storage times, temperatures, gas conditions, and their distributions) are determined. However, the logistic chain with its queues (storages, shelves) and mechanisms for ordering products is usually not taken into account. As a consequence, storage times-mutually dependent in successive steps in the chain-cannot be described adequately. This may have a great impact on the tails of risk distributions. Because food safety risks are generally very small, it is crucial to model the tails of (underlying) distributions as accurately as possible. Logistic performance can be modeled by describing the underlying planning and scheduling mechanisms in discrete-event modeling. This is common practice in operations research, specifically in supply chain management. In this article, we present the application of discrete-event modeling in the context of a QMRA for Listeria monocytogenes in fresh-cut iceberg lettuce. We show the potential value of discrete-event modeling in QMRA by calculating logistic interventions (modifications in the logistic chain) and determining their significance with respect to food safety.

Use of stochastic models to assess the effect of environmental factors on microbial growth

We present a novel application of a stochastic ecological model to the study and analysis of microbial growth dynamics as influenced by environmental conditions in an extensive experimental data set. The model proved to be useful in bridging the gap between theoretical ideas in ecology and an applied problem in microbiology. The data consisted of recorded growth curves of Escherichia coli grown in triplicate in a base medium with all 32 possible combinations of five supplements: glucose, NH(4)Cl, HCl, EDTA, and NaCl. The potential complexity of 2(5) experimental treatments and their effects was reduced to 2(2) as just the metal chelator EDTA, the presumed osmotic pressure imposed by NaCl, and the interaction between these two factors were enough to explain the variability seen in the data. The statistical analysis showed that the positive and negative effects of the five chemical supplements and their combinations were directly translated into an increase or decrease in time required to attain stationary phase and the population size at which the stationary phase started. The stochastic ecological model proved to be useful, as it effectively explained and summarized the uncertainty seen in the recorded growth curves. Our findings have broad implications for both basic and applied research and illustrate how stochastic mathematical modeling coupled with rigorous statistical methods can be of great assistance in understanding basic processes in microbial ecology.