Predictive Microbiology Tools

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27 record(s) found




ID Author(s) Title / Description Publication Year
10000 Food Safety and Inspection Service, Agricultural Research Service, United States Department of Agriculture

Predictive Microbiology Information Portal
Predictive Microbiology Information Portal
URL: http://portal.arserrc.gov/

The USDA Food Safety Inspection Service (FSIS) and the USDA Agricultural Research Service (ARS) have joined together to produce the Predictive Microbiology Information Portal (PMIP). This portal is geared…

The USDA Food Safety & Inspection Service (FSIS) and the USDA Agricultural Research Service (ARS) have joined together to produce the Predictive Microbiology Information Portal (PMIP). This portal is geared to assist  food companies (large and small) in the use of predictive models, the appropriate application of models, and proper model interpretation. This vision is that the PMIP will be the highway to the most comprehensive websites that brings together large and small food companies in contact with the information needed to aid in the production of the safest foods. The PMIP links users to numerous and diverse resources associated with models (PMP), databases (ComBase) , regulatory requirements, and food safety principles.

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Source: Food Safety and Inspection Service, Agricultural Research Service, United States Department of Agriculture

Keywords:

Agricultural Research Service, databases, educational resources, food industry, Food Safety and Inspection Service, guidelines, laws and regulations, models, predictive microbiology

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00000000
n/a
9941 Singh, A., et al

Dynamic Predictive Model for the Growth of Salmonella spp. in Liquid Whole Egg
Dynamic Predictive Model for the Growth of Salmonella spp. in Liquid Whole Egg
URL: http://dx.doi.org/10.1111/j.1750-3841.2011.02074.x

A dynamic model for the growth ofSalmonellaspp. in liquid whole egg (LWE) (approximately pH 7.8) under continuously varying temperature was developed. The model was validated using 2 (5 to…

A dynamic model for the growth of Salmonella spp. in liquid whole egg (LWE) (approximately pH 7.8) under continuously varying temperature was developed. The model was validated using 2 (5 to 15 °C; 600 h and 10 to 40 °C; 52 h) sinusoidal, continuously varying temperature profiles. LWE adjusted to pH 7.8 was inoculated with approximately 2.5–3.0 log CFU/mL of Salmonella spp., and the growth data at several isothermal conditions (5, 7, 10, 15, 20, 25, 30, 35, 37, 39, 41, 43, 45, and 47 °C) was collected. A primary model (Baranyi model) was fitted for each temperature growth data and corresponding maximum growth rates were estimated. 

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Source: Journal of Food Science, Vol. 76, No. 3, Apr. 2011, p. M225–32.

Keywords:

egg products, eggs, growth models, models, Salmonella, temperature

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20110400
2011
9936 Izquier, A.; Gómez-López, V.M.

Modeling the pulsed light inactivation of microorganisms naturally occurring on vegetable substrates
Modeling the pulsed light inactivation of microorganisms naturally occurring on vegetable substrates
URL: http://dx.doi.org/10.1016/j.fm.2011.03.010

Pulsed light (PL) is a fast non-thermal method for microbial inactivation. This research studied the kinetics of PL inactivation of microorganisms naturally occurring in some vegetables. Iceberg lettuce, white…

Pulsed light (PL) is a fast non-thermal method for microbial inactivation. This research studied the kinetics of PL inactivation of microorganisms naturally occurring in some vegetables. Iceberg lettuce, white cabbage and Julienne-style cut carrots were subjected to increasing PL fluences up to 12 J/cm2 in order to study its effect on aerobic mesophilic bacteria determined by plate count. Also, sample temperature increase was determined by infrared thermometry. Survivors’ curves were adjusted to several models. 

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Source: Food Microbiology, [Epub ahead of print], Apr. 7, 2011.

Keywords:

cabbage, carrots, inactivation, lettuce, microbial activity, models, vegetables

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20110407
2011
9609 Broughall, J.M.; Brown, C.

Hazard analysis applied to microbial growth in foods: Development and application of three-dimensional models to predict bacterial growth
Hazard analysis applied to microbial growth in foods: Development and application of three-dimensional models to predict bacterial growth
URL: http://dx.doi.org/10.1016/0740-0020(84)90005-4

Mathematical-modelling techniques have been developed which are capable of describing the effect of two environmental variables, plus temperature, on the kinetics of bacterial growth. The models illustrated…

Mathematical-modelling techniques have been developed which are capable of describing the effect of two environmental variables, plus temperature, on the kinetics of bacterial growth. The models illustrated here demonstrate the effect of water activity and pH on the growth of Staphylococcus aureus and Salmonella typhimurium. Their application to predict the level of possible bacterial growth in hazard analysis techniques is illustrated.

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Source: Food Microbiology, Vol. 1, No. 1, Jan. 1984, p. 13-22.

Keywords:

bacteria, biological hazards, food microbiology, mathematical models, models, pH, Salmonella typhi, Staphylococcus aureus, temperature, water activity

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19840100
1984
9432 Delignette-Muller, M.L.; Cornu, M.; Pouillot, R.; Denis, J.B.

Use of Bayesian modelling in risk assessment: application to growth of Listeria monocytogenes and food flora in cold-smoked salmon
Use of Bayesian modelling in risk assessment: application to growth of Listeria monocytogenes and food flora in cold-smoked salmon
URL: http://dx.doi.org/10.1016/j.ijfoodmicro.2005.06.021

An attempt to use a Bayesian approach to model variability and uncertainty separately in microbial growth in a risk assessment is presented. It was conducted within the framework of…

An attempt to use a Bayesian approach to model variability and uncertainty separately in microbial growth in a risk assessment is presented. It was conducted within the framework of a French project aiming at assessing the exposure to Listeria monocytogenes in cold-smoked salmon. The chosen model describes the effect of time and temperature on bacterial growth. A Bayesian approach close to the one proposed by Pouillot et al. [Int. J. Food Microbiol. 81 (2003) 87] is used to estimate the variability and uncertainty of growth parameters from both literature data and data experimentally acquired during the project. Variability between strains and between products is taken into account. The growth of the food flora of cold-smoked salmon is also modelled by the same method. 

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Source: International Journal of Food Microbiology, Vol. 106, No. 2, Feb. 1, 2006, p. 195-208.

Keywords:

Bayesian theory, exposure assessment, fish, growth models, Listeria monocytogenes, methodology, microbiological risk assessment, models, quantitative risk assessment, risk assessment, uncertainty, variability

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20060201
2006
9353 van Gerwen, S.; Zwietering, M.

Growth and inactivation models to be used in quantitative risk assessments
Growth and inactivation models to be used in quantitative risk assessments
URL: http://www.ingentaconnect.com/content/iafp/jfp/1998/00000061/00000011/art00023

This article discusses the growth and inactivation models that can be used in a stepwise procedure for quantitative risk assessment. The proposed stepwise procedure provides a structured method of…

This article discusses the growth and inactivation models that can be used in a stepwise procedure for quantitative risk assessment. The proposed stepwise procedure provides a structured method of risk assessment and prevents the researcher from getting caught in too much complexity. This simplicity is necessary because of the complex nature of food safety. The principal aspects are highlighted during the procedure and many factors can be omitted since their quantitative effect is negligible.

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Source: Journal of Food Protection, Vol. 61, No. 11, Nov. 1998, p. 1541-9.

Keywords:

methodology, microbiological risk assessment, models, quantitative risk assessment, risk assessment

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19981100
1998
9159 Youart, A.M., et al

Modeling Time to Inactivation of Listeria monocytogenes in Response to High Pressure, Sodium Chloride, and Sodium Lactate
Modeling Time to Inactivation of Listeria monocytogenes in Response to High Pressure, Sodium Chloride, and Sodium Lactate
URL: http://www.ingentaconnect.com/content/iafp/jfp/2010/00000073/00000010/art00003

A mathematical model was developed to predict time to inactivation (TTI) by high pressure processing of Listeria monocytogenes in a broth system (pH 6.3) as a function of pressure…

A mathematical model was developed to predict time to inactivation (TTI) by high pressure processing of Listeria monocytogenes in a broth system (pH 6.3) as a function of pressure (450 to 700 MPa), inoculum level (2 to 6 log CFU/ml), sodium chloride (1 or 2%), and sodium lactate (0 or 2.5%) from a 4°C initial temperature. Ten L. monocytogenes isolates from various sources, including processed meats, were evaluated for pressure resistance. The five most resistant strains were used as a cocktail to determine TTI and for model validation. 

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Source: Journal of Food Protection, Vol. 73, No. 10, Oct. 2010, p. 1793-1802.

Keywords:

Listeria monocytogenes, mathematical models, meat, meat products, model validation, models, validity

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20101000
2010
9093 Billoir, E., et al

Probabilistic Modeling of the Fate of Listeria Monocytogenes in Diced Bacon During the Manufacturing Process
Probabilistic Modeling of the Fate of Listeria Monocytogenes in Diced Bacon During the Manufacturing Process
URL: http://dx.doi.org/10.1111/j.1539-6924.2010.01494.x

To assess the impact of the manufacturing process on the fate of Listeria monocytogenes, the researchers in this article built a generic probabilistic model intended to simulate the successive…

To assess the impact of the manufacturing process on the fate of Listeria monocytogenes, the researchers in this article built a generic probabilistic model intended to simulate the successive steps in the process. Contamination evolution was modeled. By means of simulations, the model was explored using a baseline calibration and alternative scenarios, in order to assess the impact of changes in the process and of accidental events.

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Source: Risk Analysis, Vol. 31, No. 2, Feb. 2011, p. 237–54.

Keywords:

Bayesian theory, food processing, Listeria monocytogenes, methodology, microbiological risk assessment, models, pork, risk assessment, swine

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20110200
2011
9060 Min, K.J.; Yoon, K.S.

Development and Validation of a Predictive Model for Foodborne Pathogens in Ready-to-Eat Pork as a Function of Temperature and a Mixture of Potassium Lactate and Sodium Diacetate
Development and Validation of a Predictive Model for Foodborne Pathogens in Ready-to-Eat Pork as a Function of Temperature and a Mixture of Potassium Lactate and Sodium Diacetate
URL: http://www.ingentaconnect.com/content/iafp/jfp/2010/00000073/00000009/art00005

In this article, the researchers developed and validated secondary models that can predict growth parameters of Salmonella Typhimurium and Staphylococcus aureus in cooked-pressed ready-to-eat (RTE) pork as a function…

In this article, the researchers developed and validated secondary models that can predict growth parameters of Salmonella Typhimurium and Staphylococcus aureus in cooked-pressed ready-to-eat (RTE) pork as a function of concentrations (0 to 3%) of a commercial potassium lactate and sodium diacetate mixture (PL+SDA) and temperature (10 to 30°C). The primary growth data were fitted to a Gompertz equation to determine the lag time (LT) and growth rate (GR). Model performance was also evaluated by use of the prediction bias (Bf) and accuracy (Af) factors, median relative error, and mean absolute relative error, as well as the acceptable prediction zone method.

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Source: Journal of Food Protection, Vol. 73, No. 9, Sep. 2010, p. 1626-1632.

Keywords:

models, pork, ready-to-eat foods, Salmonella typhi, Staphylococcus aureus, swine, temperature

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20100900
2010
9007 Pan, W.; Schaffner, D.W.

Modeling the Growth of Salmonella in Cut Red Round Tomatoes as a Function of Temperature
Modeling the Growth of Salmonella in Cut Red Round Tomatoes as a Function of Temperature
URL: http://www.ingentaconnect.com/content/iafp/jfp/2010/00000073/00000008/art00013

The purpose of this study was to develop a mathematical model to describe the growth of Salmonella on cut tomatoes at various temperatures. Four Salmonella serotypes (Typhimurium, Newport, Javiana,…

The purpose of this study was to develop a mathematical model to describe the growth of Salmonella on cut tomatoes at various temperatures. Four Salmonella serotypes (Typhimurium, Newport, Javiana, and Braenderup) obtained from previous tomato-linked cases of salmonellosis were used in this study. 

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Source: Journal of Food Protection, Vol. 73, No. 8, Aug. 2010, p. 1502-1505

Keywords:

bacteria, biological hazards, food storage, mathematical models, models, produce, Salmonella, temperature, tomatoes

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20100800
2010
9003 Oyarzabal, O.A.; Oscar, T.P.; Speegle, L.; Nyati, H.

Survival of Campylobacter jejuni and Campylobacter coli on Retail Broiler Meat Stored at ?20, 4, or 12°C and Development of Weibull Models for Survival
Survival of Campylobacter jejuni and Campylobacter coli on Retail Broiler Meat Stored at ?20, 4, or 12°C and Development of Weibull Models for Survival
URL: http://www.ingentaconnect.com/content/iafp/jfp/2010/00000073/00000008/art00005

Survival of Campylobacter jejuni and Campylobacter coli isolated from broiler meat was investigated and modeled on retail breast meat.

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Source: Journal of Food Protection, Vol. 73, No. 8, Aug. 2010, p. 1438-144

Keywords:

bacteria, biological hazards, broilers, Campylobacter, Campylobacter coli, Campylobacter jejuni, freezing, models, poultry, poultry products, refrigeration, storage

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20100800
2010
9002 Gonzales-Barron, U.; Redmond, G.; Butler, F.

Modeling Prevalence and Counts from Most Probable Number in a Bayesian Framework: An Application to Salmonella Typhimurium in Fresh Pork Sausages
Modeling Prevalence and Counts from Most Probable Number in a Bayesian Framework: An Application to Salmonella Typhimurium in Fresh Pork Sausages
URL: http://www.ingentaconnect.com/content/iafp/jfp/2010/00000073/00000008/art00002

Prevalence and counts of Salmonella Typhimurium in fresh pork sausage packs at the point of retail were modeled by using Irish and United Kingdom retail surveys' data. A methodology…

Prevalence and counts of Salmonella Typhimurium in fresh pork sausage packs at the point of retail were modeled by using Irish and United Kingdom retail surveys' data. A methodology for modeling a second-order distribution for the initial Salmonella concentration (λ0) in pork sausage at retail was presented considering the uncertainty originated from the most probable-number (MPN) serial dilutions.

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Source: Journal of Food Protection, Vol. 73, No. 8, Aug. 2010, p. 1416-142

Keywords:

Bayesian theory, disease prevalence, models, pork, Salmonella typhi, swine

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20100800
2010
8860 Rodríguez, F.P., et al

mathematical risk model for Escherichia coli O157: H7 cross-contamination of lettuce during processing
A mathematical risk model for Escherichia coli O157: H7 cross-contamination of lettuce during processing
URL: http://dx.doi.org/10.1016/j.fm.2010.06.008

A stochastic simulation modelling approach was taken to determine the extent of Escherichia coli O157:H7 contamination in fresh-cut bagged lettuce leaving the processing plant. A probabilistic model was constructed…

A stochastic simulation modelling approach was taken to determine the extent of Escherichia coli O157:H7 contamination in fresh-cut bagged lettuce leaving the processing plant. A probabilistic model was constructed in Excel to account for E. coli O157:H7 cross contamination when contaminated lettuce enters the processing line.

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Source: Food Microbiology, [Epub ahead of print], Jun. 30, 2010

Keywords:

bacteria, biological hazards, cross contamination, Escherichia coli, Escherichia coli O157:H7, food pathogens, lettuce, mathematical models, methodology, models, risk assessment

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20100630
2010
8567 Duffy, S.; Schaffner, D.W.

Modeling the survival of Escherichia coli O157:H7 in apple cider using probability distribution functions for quantitative risk assessment
Modeling the survival of Escherichia coli O157:H7 in apple cider using probability distribution functions for quantitative risk assessment
URL: http://foodsci.rutgers.edu/schaffner/pdf%20files/Duffy%20JFP%202001.pdf

In this paper, the authors developed probability distribution functions for the change in concentration of E. coli O157:H7 in cider using data from scientific publications for use in a…

In this paper, the authors developed probability distribution functions for the change in concentration of E. coli O157:H7 in cider using data from scientific publications for use in a quantitative risk assessment.

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Source: Journal of Food Protection, Vol. 64, No. 5, May 2001, p. 599-605.

Keywords:

apples, bacteria, biological hazards, Escherichia coli, Escherichia coli O157:H7, meta-analysis, methodology, models, probability distribution, quantitative risk assessment, risk assessment

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20010000
2001
8541 Pouillot, R.; Lubran, M.B.; Cates, S.C.; Dennis, S.

Estimating Parametric Distributions of Storage Time and Temperature of Ready-to-Eat Foods for U.S. Households
Estimating Parametric Distributions of Storage Time and Temperature of Ready-to-Eat Foods for U.S. Households
URL: http://www.ingentaconnect.com/content/iafp/jfp/2010/00000073/00000002/art00013

This study used classical parametric survival modeling to derive parametric distributions from the RTI International storage practices data set, which was conducted as a national survey of U.S. adults…

This study used classical parametric survival modeling to derive parametric distributions from the RTI International storage practices data set, which was conducted as a national survey of U.S. adults to characterize consumers' home storage and refrigeration practices for 10 different categories of refrigerated ready-to-eat foods.

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Source: Journal of Food Protection, Vol. 73, No. 2, Feb. 2010, p. 312-321.

Keywords:

bacteria, biological hazards, consumer attitudes, consumer behavior, Listeria, Listeria monocytogenes, models, ready-to-eat foods, refrigeration

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20100200
2010
8471 Bernaerts, K., et al

Concepts and tools for predictive modeling of microbial dynamics
Concepts and tools for predictive modeling of microbial dynamics
URL: http://www.ingentaconnect.com/content/iafp/jfp/2004/00000067/00000009/art00032

Based on two case studies, the fundamentals of both macroscopic (population) and microscopic (individual) modeling approaches are revisited. These illustrations deal with the modeling of (i) microbial lag under…

Based on two case studies, the fundamentals of both macroscopic (population) and microscopic (individual) modeling approaches are revisited. These illustrations deal with the modeling of (i) microbial lag under variable temperature conditions and (ii) interspecies microbial interactions mediated bylactic acid production (product inhibition).

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Source: Journal of Food Protection, Vol. 67, No. 9, Sep. 2004, p. 2041-2052.

Keywords:

biological hazards, Lactobacillus, Listeria, microbiology, models, predictive microbiology, reaction inhibition, temperature

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20040900
2004
8222 National Institute of Aquatic Resources (DTU Aqua), Technical University of Denmark

Seafood Spoilage and Safety Predictor
Seafood Spoilage and Safety Predictor
URL: http://sssp.dtuaqua.dk/

The SSSP software predicts shelf-life and growth of bacteria in different fresh and lightly preserved seafood, e.g. the effect of product temperature profiles recorded during storage and distribution by…

The SSSP software predicts shelf-life and growth of bacteria in different fresh and lightly preserved seafood, e.g. the effect of product temperature profiles recorded during storage and distribution by data loggers. Some of the predictive models in SSSP are equally useful for other types of food. The new version features a Listeria monocytogenes model.

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Source: National Institute of Aquatic Resources (DTU Aqua), Technical University of Denmark

Keywords:

bacteria, biological hazards, computer software, Listeria, Listeria monocytogenes, pathogens, seafoods

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00000000
n/a
7950 Sheen, S.; Hwang, C.A.

Modeling Transfer of Listeria monocytogenes from Slicer to Deli Meat During Mechanical Slicing
Modeling Transfer of Listeria monocytogenes from Slicer to Deli Meat During Mechanical Slicing
URL: http://www.liebertonline.com/doi/pdfplus/10.1089/fpd.2007.0049

In this study, transfer of L. monocytogenes from one contact surface to another for Ready-to-Eat deli meats with a delicatessen or restaurant type slicer was investigated. The objectives were…

In this study, transfer of L. monocytogenes from one contact surface to another for Ready-to-Eat deli meats with a delicatessen or restaurant type slicer was investigated. The objectives were to achieve the surface transfer model development and to predict the cross-contamination for the slicing operation. Two cross-contamination routes were studied for model development: (1) L. monocytogenes–contaminated blade to ham (Case I), and (2) L. monocytogenes–contaminated ham to blade and then to uncontaminated ham (Case II).

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Source: Foodborne Pathogens and Disease: Vol. 5 No. 2 pp. 135-146.

Keywords:

animal products, antibiotic resistance, bacteria, biological hazards, delicatessen foods, Enterobacteriaceae, Listeria, Listeria monocytogenes, meat, methodology, models, poultry, ready-to-eat foods, risk assessment

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20080400
2008
7937 Poschet, F.; Geeraerd, A.H.; Scheerlinck, N.; Nicolai, B.M.; Van Impe, J.F.

Monte Carlo analysis as a tool to incorporate variation on experimental data in predictive microbiology
Monte Carlo analysis as a tool to incorporate variation on experimental data in predictive microbiology
URL: http://dx.doi.org/10.1016/S0740-0020(02)00156-9

The objective of this paper is to illustrate methodologically how to generate, starting from the experimental observations and a deterministic growth model, probability density functions for (i) the model…

The objective of this paper is to illustrate methodologically how to generate, starting from the experimental observations and a deterministic growth model, probability density functions for (i) the model parameters and (ii) the predictions as a function of time, by using Monte Carlo analysis. A normal distribution over the experimental data was considered. This probabilistic approach, incorporating experimental variation, is applied to experimental growth data of Escherichia coli K12 and Listeria innocua ATCC 33090.

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Source: Food Microbiology: Vol. 20 No. 3 pp. 285-295.

Keywords:

Escherichia coli, Listeria, models, Monte Carlo method, predictive microbiology, probability distribution, simulation models

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20030600
2003
7927 Oscar, T.P.

Predictive Model for Survival and Growth of Salmonella Typhimurium DT104 on Chicken Skin during Temperature Abuse
Predictive Model for Survival and Growth of Salmonella Typhimurium DT104 on Chicken Skin during Temperature Abuse
URL: http://www.ingentaconnect.com/content/iafp/jfp/2009/00000072/00000002/art00010

To better predict risk of Salmonella infection from chicken subjected to temperature abuse, a study was undertaken to develop a predictive model for survival and growth of Salmonella Typhimurium…

To better predict risk of Salmonella infection from chicken subjected to temperature abuse, a study was undertaken to develop a predictive model for survival and growth of Salmonella Typhimurium DT104 on chicken skin with native flora. For model development, chicken skin portions (2.14 cm2) were inoculated with 0.85 log of Salmonella Typhimurium DT104 (ATCC 700408) and then stored at 5 to 50°C for 8 h. Kinetic data from the storage trials were fit to a primary model to determine lag time (?), specific growth rate (?), and the 95% prediction interval (PI). Secondary models for ?, ?, and PI as a function of storage temperature were developed and then combined with the primary model to create a tertiary model. Performance of the tertiary model was evaluated against dependent data, independent data for interpolation, and independent data for extrapolation to kosher chicken skin by using an acceptable prediction zone from ?1 (fail-safe) to 0.5 (fail-dangerous) log per skin portion.

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Source: Journal of Food Protection, Vol. 72 No. 2 pp. 304--314.

Keywords:

hazard characterization, methodology, models, poultry, poultry products, risk assessment, Salmonella, Salmonella typhi

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20090200
2009
7922 Hwang, C.A.; Porto-Fett, A.C.S.; Juneja, V.K.; Ingham, S.C.; Ingham, B.H.; Luchansky, J.B.

Modeling the survival of Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella Typhimurium during fermentation, drying, and storage of soudjouk-style fermented sausage
Modeling the survival of Escherichia coli O157:H7, Listeria monocytogenes, and Salmonella Typhimurium during fermentation, drying, and storage of soudjouk-style fermented sausage
URL: http://dx.doi.org/10.1016/j.ijfoodmicro.2008.12.003

This study quantified and modeled the survival of Escherichia coli O157:H7, Listeria monocytogenes and Salmonella Typhimurium in soudjouk-style fermented sausage during fermentation, drying, and storage. The objectives of this…

This study quantified and modeled the survival of Escherichia coli O157:H7, Listeria monocytogenes and Salmonella Typhimurium in soudjouk-style fermented sausage during fermentation, drying, and storage. The objectives of this study were to quantify the survival of E. coli O157:H7, L. monocytogenes, and Salmonella Typhimurium in a soudjouk-style sausage during fermentation and drying to various pH and aw values and at various storage temperatures, and to describe the survival using mathematical equations to estimate the survivability of these three pathogens in other FDSS products.

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Source: International Journal of Food Microbiology, Vol. 129 No. 3 pp. 244-252.

Keywords:

Escherichia coli, Escherichia coli O157:H7, food processing, hazard characterization, Listeria, Listeria monocytogenes, meat, meat products, models, poultry, Salmonella, Salmonella typhi

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20090228
2009
7836 Ivanek, R.; Grohn, Y.T. Wiedmann, M.; Wells, M.T.

Mathematical Model of Listeria monocytogenes Cross-Contamination in a Fish Processing Plant
Mathematical Model of Listeria monocytogenes Cross-Contamination in a Fish Processing Plant
URL: http://www.ingentaconnect.com/content/iafp/jfp/2004/00000067/00000012/art00009

This report details a mathematical simulation model describing cross contamination pathways for L. monocytogenes. A smoked fish processing plant was used for the model. The report concludes that 10.7%…

This report details a mathematical simulation model describing cross contamination pathways for L. monocytogenes. A smoked fish processing plant was used for the model. The report concludes that 10.7% of foods in a lot are contaminated, and that the most significant contributors to contamination were the frequency in which employees' gloves touched food and food surfaces, and the frequency of changing gloves. Access to the full report requires purchase.

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Source: Journal of Food Protection: Vol. 67, No. 12 pp. 2688–2697.

Keywords:

bacteria, biological hazards, fish, fish products, Listeria, Listeria monocytogenes, methodology, models, risk assessment, seafoods, shellfish, statistical models

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20041200
2004
7835 Schaffner, D.W.

Mathematical Frameworks for Modeling Listeria Cross-contamination in Food-processing Plants
Mathematical Frameworks for Modeling Listeria Cross-contamination in Food-processing Plants
URL: http://foodsci.rutgers.edu/schaffner/pdf%20files/Schaffner%20JFS%202004.pdf

This study uses Monte Carlo simulation techniques to quantitatively describe the cross-contamination of Listeria monocytogenes. Two models are presented. The first model describes the number and prevalence for four…

This study uses Monte Carlo simulation techniques to quantitatively describe the cross-contamination of Listeria monocytogenes. Two models are presented. The first model describes the number and prevalence for four different strains while the second model describes the number and prevalence for only one strain. The purpose of these models is to provide a mathematical framework for future predictive models with the ultimate goal of understanding and controlling L. monocytogenes in food-processing plants.

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Source: Journal of Food Science

Keywords:

bacteria, biological hazards, cross contamination, Listeria, Listeria monocytogenes, methodology, models, risk assessment, statistical models

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20040709
2004
2862 Institute of Food Research

MicroFit v1.0
MicroFit v1.0
URL: http://www.ifr.ac.uk/MicroFit/

Tool for obtaining microbiological growth parameters from challenge test data. You must register to download the software, download is free

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Source: Institute of Food Research

Keywords:

biological hazards, hazard characterization, mathematical models, predictive microbiology

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n/a
2860 Eastern Regional Research Center, Agricultural Research Service, U.S. Department of Agriculture (USDA)/United Kingdom Food Standards Agency/Institute of Food Research

ComBase
ComBase
URL: http://www.combase.cc/

Database of predictive microbiology information collected from researchers, institutions, and published literature. The database may be searched based on temperature, pH, water activity, condition, source (publication), organism, and environment.…

Database of predictive microbiology information collected from researchers, institutions, and published literature. The database may be searched based on temperature, pH, water activity, condition, source (publication), organism, and environment. Files are returned giving organism, maximum rate, doubling time or D-value, source, conditions, environment, temperature, pH, water activity, a table and chart for log concentration vs. time, and any further details. A basic registration process also allows a user to download the data

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Source: Eastern Regional Research Center, Agricultural Research Service, U.S. Department of Agriculture

Keywords:

biological hazards, databases, hazard characterization, predictive microbiology, temperature

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00000000
n/a
259 Snyder, Jr., O.

Calculating the Total Growth of Bacteria in Cooked Food Using the FDA Code Controls
Calculating the Total Growth of Bacteria in Cooked Food Using the FDA Code Controls
URL: http://www.hi-tm.com/Documents2001/time-temp-calculations.html

Tables for calculating length of time cooked foods may be safely kept at various temperatures

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Source: Hospitality Institute of Technology and Management

Keywords:

bacteria, Food and Drug Administration, food contamination, microbial growth, predictive microbiology, temperature

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20010107
2001
13 Microbial Food Safety Research Unit, Eastern Regional Research Center, Agricultural Research Service, U.S. Department of Agriculture (USDA)

Pathogen Modeling Program
Pathogen Modeling Program
URL: http://ars.usda.gov/Services/docs.htm?docid=6786

Tool for estimating the effects of multiple variables on the growth or survival of foodborne pathogens

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Source: Microbial Food Safety Research Unit, Eastern Regional Research Center, Agricultural Research Service, U.S. Department of Agriculture

Keywords:

computer software, food microbiology, food pathogens, microbial growth, models, pathogen survival, predictive microbiology, simulation models

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