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Development of Predictive Microbial Models for Food Safety and Their Associated Use in International Microbial Databases

  1. Develop predictive models that quantify growth kinetics and/or survival behavior of high priority pathogens (including but not limited to Shiga-toxin producing Escherichia coli, Listeria monocytogenes, Salmonella spp., Clostridium perfringens, and Staphylococcus aureus) in foods or food systems. This includes development of predictive models that quantify growth and/or inactivation kinetics of pathogens in food systems during heating and cooling. 1A: Pathogen Behavior in RTE Foods, Liquid Egg Products, and Produce - Measure and model pathogen growth in RTE foods, liquid egg products, and pre-packaged produce as a function of intrinsic and extrinsic factors, pathogen strain and physiological state, and natural background microflora. 1B: Thermal Inactivation Studies - Define combinations of intrinsic and extrinsic factors that delineate minimum heat treatments for pathogen lethality. 1C: Time-Temperature Conditions for Cooling Cooked Meat - Evaluate excessive time in cooling of heated meat and poultry products supplemented with additives to determine if the product remains safe.
  2. Develop methods for application in predictive microbiology that are allied to Objective 1. For example: computer simulation of bacterial growth and inactivation under dynamic conditions, and simulation of the growth, inactivation and survival of foodborne pathogens in the presence of competing background flora.
  3. Extend technology transfer through the expansion and continued maintenance of the Pathogen Modeling Program (PMP) and the Predictive Microbiology Information Portal (PMIP). Develop a computational framework to make the PMP compatible with Combase, and continue to support the development of ComBase with our associated partners (the Institute of Food Research [IFR] and the University of Tasmania [UTAS]) as an international data resource.
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Pathogen growth in RTE foods, liquid egg products, and pre-packaged produce as a function of intrinsic and extrinsic food factors, and pathogen strain and physiological state will be determined. Also, combinations of intrinsic and extrinsic factors that delineate minimum heat treatments for pathogen lethality as well as safe rate and extent of cooling of heated meat and poultry supplemented with additives will be determined. Both static and dynamic temperature models will be developed. Developed models will be validated against data sets not used in model development and data set obtained from ComBase and published literatures. The underlying mathematics of each predictive model will be implemented in the ARS Pathogen Modeling Program. Raw data will be added to ComBase. The project will also collaborate with the IFR and the UTAS to further develop the Combase on improving its interface, functionality, and compatibility with PMP.
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Huang, Lihan
Hwang, Cheng-An (Andy)
Juneja, Vijay
Project Reports:
Published Journal Articles USDA ARS (NP 108):
Comparative study of thermal inactivation kinetics of Salmonella spp. in peanut butter and peanut butter spread
Li C, Huang L, Chen J.
Food Control. 2014 Nov;45:143-9.
Predictive modeling for growth of non- and cold-adapted Listeria monocytogenes on fresh-cut cantaloupe at different storage temperatures
Hong YK, Yoon WB, Huang L, Yuk HG.
J Food Sci. 2014 Jun;79(6):M1168-72.
The influence of acid stress on the growth of Listeria monocytogenes and escherichia coli O157:H7 on cooked ham
Hwang C, Sheen S, Juneja VK, Hwang C, Yin T, Chang N.
Food Control. 2014 Mar;37:245-50.
IPMP 2013--a comprehensive data analysis tool for predictive microbiology
Huang L.
Int J Food Microbiol. 2014 Feb 3;171:100-7.
Growth and survival kinetics of Listeria monocytogenes in cooked egg whites
Fang T, Huang L .
Food Control. 2014 Feb;36(1):191-8.
Predictive thermal inactivation model for effects and interactions of temperature, NaCl, sodium pyrophosphate and sodium lactate on Listeria monocytogenes in ground beef
Juneja VK, Mukhopadhyay S, Marks HL, Mohr T, Warning A, Datta A .
Food Bioprocess Tech. 2014 Feb;7(2):437-46.
Antimicrobial effect of blueberry (Vaccinium corymbosum L.) extracts against the growth of Listeria monocytogenes and Salmonella Enteritidis
Shen X, Liu H, Zhao Y, Pan Y, Wu VC, Hwang C.
Food Control. 2014 Jan;35(1):159-65.
Thermal inactivation of Escherichia coli O157:H7 in strawberry puree and its effect on anthocyanins and color
Hus H, Huang L, Wu S.
J Food Sci. 2014 Jan;79(1):M74-80.
Phytochemicals in lowbush wild blueberry inactivate Escherichia coli O157:H7 by damaging its cell membrane
Lacombe A, Tadepalli S, Hwang C, Wu VC .
Foodborne Pathog Dis. 2013 Nov;10(11):944-50.
Predictive model for growth of Clostridium perfringens during cooling of cooked beef supplemented with NaCl, sodium nitrite and sodium pyrophosphate
Juneja VK, Marks HL, Mohr T, Thipareddi H .
J Food Process Techno. 2013 Oct 31;4(10):275.
Determination of thermal inactivation kinetics of Listeria monocytogenes in chicken meat by isothermal and dynamic methods
Huang L.
Food Control. 2013 Oct;33(2):484-8.
Effect of meat ingredients (sodium nitrite and erythorbate) and processing (vacuum storage and packaging atmosphere) on germination and outgrowth of Clostridium perfringens spores in ham during abusive cooling
Redondo-Solano M, Valenzuela-Martinez C, Cassada D, Snow DD, Burson DE, Juneja VK, Thippareddi H .
Food Microbiol. 2013 Sep;35(2):108–15.
Predictive thermal inactivation model for the combined effect of temperature, cinnamaldehyde and carvacrol on starvation-stressed multiple Salmonella serotypes in ground chicken
Juneja VK, Gonzales-Barron U, Butler F, Yadav AS, Friedman M.
Int J Food Microbiol. 2013 Jul 15;165(2):184-99.
Optimization of a new mathematical model for bacterial growth
Huang L.
J Appl Microbiol. 2013 Jul;32(1):283-8.
The effect of potassium sorbate and pH on the growth of Listeria monocytogenes in ham salad
Hwang C, Huang L .
J Food Process Preserv. 2013 Jun 25. [Epub ahead of print]
Predictive model for the reduction of heat resistance of Listeria monocytogenes in ground beef by the combined effect of sodium chloride and apple polyphenols
Juneja VK, Hwang C, Sheen S, Friedman M, Altuntas E, Ayhan K.
Int J Food Microbiol. 2013 Jun;164(1):54-9.
Growth kinetics of Listeria monocytogenes and spoilage microorganisms in fresh-cut cantaloupe
Fang T, Liu Y, Huang L.
Food Microbiol. 2013 May;34(1):174-81.
A probability model for enterotoxin production of Bacillus cereus as a function of pH and temperature
Ding T, Wang J, Park MS, Hwang CA, Oh DH.
J Food Prot. 2013 Feb;76(2):343-7.
Growth potential of Clostridium perfringens from spores in acidified beef, pork and poultry products during chilling
Juneja VK, Baker DA, Thippareddi H, Snyder OP Jr, Mohr TB.
J Food Prot. 2013 Jan;76(1):65-71.
A simplified method for numerical simulation of gas grilling of non-intact beef steaks to elimate Escherichia coli O157:H7
Huang L.
J Food Engineering. 2012 Dec;113(3):380-8.
Mathematical modeling and numerical analysis of the growth of non-O157 Shiga toxin-producing Escherichia coli in spinach leaves
Huang L.
Int J Food Microbiol. 2012 Nov 1;160(1):32-41.
Inhibition of Clostridium perfringens spore germination and outgrowth by lemon juice and vinegar product in reduced NaCl roast beef
Li L, Valenzuela-Martinez C, Redondo M, Juneja VK, Burson DE, Thippareddi H.
J Food Sci. 2012 Nov;77(11):M598-603.
Growth kinetics and model comparison of Cronobacter sakazakii in reconstituted powdered infant formula
Fang T, Gurtler JB, Huang L.
J Food Sci. 2012 Sep;77(9):E247-55.
Non-Journal Publications:
Chilled storage of foods - principles - (Book / Chapter)
Hwang, C., Huang, L. 2014. Chilled storage of foods - principles. In: Batt, C.A., Tortorello, M.L. (Eds), Encyclopedia of Food Microbiology, vol 1. Elsevier Ltd, Academic Press, pp. 427-431.
The effect of acid stress on the growth of listeria monocytogenes and escherichia coli O157:H7 in cooked ham - (Abstract Only)
Food Safety Categories:
Food and Feed Handling and Processing
Pathogen Biology
Farm-to-Table Categories:
Food processing