Skip to main content
SpringerLink
Log in

Microbial Inactivation Models for Thermal Processes

  • Chapter
  • First Online:
Food Safety Engineering

Part of the book series: Food Engineering Series ((FSES))

Abstract

In many food products, the population of microorganisms present after initial manufacturing stages is too high. As such, an intervention treatment is required to reduce the microbial load of these products. Thermal treatments are still by far the most common methods for microbial inactivation. The appropriate use of these technologies is aided by using mathematical models that describe the effect of temperature and other conditions on microbial responses. These kinetic models are subcategorised as primary models that describe the evolution of the population with time and secondary models that describe the effect of the environmental conditions on the parameters of the primary models. This chapter offers a comprehensive discussion of primary and secondary models for thermal microbial inactivation.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 189.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 249.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD 279.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  • Abe H, Koyama K, Kawamura S et al (2018) Stochastic evaluation of Salmonella enterica lethality during thermal inactivation. Int J Food Microbiol 285:129–135

    CAS  PubMed  Google Scholar 

  • Albert I, Mafart P (2005) A modified Weibull model for bacterial inactivation. Int J Food Microbiol 100(1–3):197–211

    CAS  PubMed  Google Scholar 

  • Arrhenius S (1889) Über die Dissociationswärme und den Einfluss der Temperatur auf den Dissociationsgrad der Elektrolyt. Z Phys Chem 4(1):96–116

    Google Scholar 

  • Aspridou Z, Koutsoumanis KP (2015) Individual cell heterogeneity as variability source in population dynamics of microbial inactivation. Food Microbiol 45:216–221

    PubMed  Google Scholar 

  • Ball CO, Olson FCW (1957) Sterilization in food technology. McGraw-Hill, New York

    Google Scholar 

  • Baril E, Coroller L, Couvert O et al (2012) Food Microbiol 30(1):29–36

    PubMed  Google Scholar 

  • Bigelow W (1921) The logarithmic nature of thermal death curves. J Infect Des 29:528–536

    Google Scholar 

  • Cappuyns AM, Valdramidis VP, Van Impe JFM (2012) Modelling microbial inactivation kinetics: primary models. In: Valdramidis VP, Van Impe JF (eds) Progress on quantitative approaches for thermal food processing. Nova Scientific Publishers, Inc., New York, pp 99–119

    Google Scholar 

  • Cerf O (1977) Tailing of survival curves of bacterial spores. J Appl Bacteriol 42(1):1–19

    CAS  PubMed  Google Scholar 

  • Cerf O, Davey KR, Sadoudi AK (1996) Thermal inactivation of bacteria – a new predictive model for the combined effect of three environmental factors: temperature, pH and water activity. Food Res Int 29(3–4):219–226

    Google Scholar 

  • Chick H (1908) An investigation of the laws of disinfection. J Hyg 8(1):92–158

    CAS  PubMed  Google Scholar 

  • Davey KR, Lin SH, Wood DG (1978) The effect of pH on continuous high-temperature/short-time sterilization of liquid foods. AICHE J 24(3):537–540

    CAS  Google Scholar 

  • Dolan KD, Valdramidis VP, Mishra DK (2013) Parameter estimation for dynamic microbial inactivation: which model, which precision? Food Control 29(2):401–408

    Google Scholar 

  • Fernández PS, Ocio MJ, Rodrigo F et al (1996) Mathematical model for the combined effect of temperature and pH on the thermal resistance of Bacillus stearothermophilus and Clostridium sporogenes spores. Int J Food Microbiol 32(1–2):225–233

    PubMed  Google Scholar 

  • Gaillard S, Leguerinel I, Mafart P (1998a) Modelling combined effects of temperature and pH on the heat resistance of spores of Bacillus cereus. Food Microbiol 15(6):625–630

    Google Scholar 

  • Gaillard S, Leguerinel I, Mafart P (1998b) Model for combined effects of temperature, pH and water activity on thermal inactivation of Bacillus cereus spores. J Food Sci 63(5):887–889

    CAS  Google Scholar 

  • Geeraerd AH, Herremans CH, Van Impe JF (2000) Structural model requirements to describe microbial inactivation during a mild heat treatment. Int J Food Microbiol 59(3):185–209

    CAS  PubMed  Google Scholar 

  • Geeraerd AH, Valdramidis VP, Van Impe JF (2005) GInaFiT, a freeware tool to assess non-loglinear microbial survivor curves. Int J Food Microbiol 102(1):95–105

    CAS  PubMed  Google Scholar 

  • González I, López M, Martínez S et al (1999) Thermal inactivation of Bacillus cereus spores formed at different temperatures. Int J Food Microbiol 51(1):81–84

    PubMed  Google Scholar 

  • Grant IR, Hitchings EI, McCartney A et al (2002) Effect of commercial-scale high-temperature, short-time pasteurization on the viability of Mycobacterium paratuberculosis in naturally infected cows’ milk. Appl Env Microbiol 68(2):602–603

    CAS  Google Scholar 

  • Humpheson L, Adams MR, Anderson WA et al (1998) Biphasic thermal inactivation kinetics in Salmonella enteridis PT4. Appl Environ Microbiol 64(2):459–464

    CAS  PubMed  PubMed Central  Google Scholar 

  • Jarvis NA, O’Bryan CA, Dawoud M et al (2016) An overview of Salmonella thermal destruction during food processing and preparation. Food Control 68:280–290

    Google Scholar 

  • Juneja VK, Marmer BS, Phillips JG et al (1995) Influence of the intrinsic properties of food on thermal inactivation of spores of nonproteolytic Clostridium botulinum: development of a predictive model. J Food Saf 15(4):349–363

    Google Scholar 

  • Juneja VK, Eblen BS, Marks HM (2001) Modeling non-linear survival curves to calculate thermal inactivation of Salmonella in poultry of different fat levels. Int J Food Microbiol 70(1–2):37–51

    CAS  PubMed  Google Scholar 

  • Juneja VK, Gonzales-Barron U, Butler F et al (2013) Predictive thermal inactivation model for the combined effect of temperature, cinnamaldehyde and carvacrol on starvation-stressed multiple Salmonella sterotypes in ground chicken. Int J Food Microbiol 165(2):184–199

    CAS  PubMed  Google Scholar 

  • Juneja V, Mukhopadhyay S, Marks H (2014) Predictive thermal inactivation model for effects and interactions of temperature, NaCl, sodium pyrophosphate, and sodium lactate on Listeria monocytogenes in ground beef. Food Bioprocess Technol 7(2):437–446

    CAS  Google Scholar 

  • Knirsch MC, dos Santos CA, de Oliveira Soares Vincente AAM et al (2010) Ohmic heating – a review. Trends Food Sci Technol 21(9):436–441

    CAS  Google Scholar 

  • Knoerzer K, Juliano P, Gladman S et al (2007) A computational model for temperature and sterility distributions in a pilot-scale high pressure high-temperature process. AICHE J 53(11):2996–3010

    CAS  Google Scholar 

  • Krishnamurthy K, Khurana HK, Jun S et al (2008) Infrared heating in food processes: an overview. Compr Rev Food Sci Food Saf 7(1):2–13

    Google Scholar 

  • Lemaire V, Cerf O, Audurier A (1989) Thermal resistance of Listeria monocytogens. Annales de Recherches Vétérinaires, INRA Editions 20(4):493–500

    Google Scholar 

  • Luechapattanaporn K, Wang Y, Wang J et al (2004) Microbial safety in radio-frequency processing of packaged foods. J Food Sci 69(7):201–206

    Google Scholar 

  • Mafart P, Leguerinel I (1998) Modeling combined effects of temperature and pH on heat resistance of spores by a linear-Bigelow equation. J Food Sci 63(1):6–8

    CAS  Google Scholar 

  • Mafart P, Couvert O, Gaillard S et al (2002) On calculating sterility in thermal preservation methods: application of the Weibull frequency distribution model. Int J Food Microbiol 72(1–2):107–113

    CAS  PubMed  Google Scholar 

  • Mafart P, Leguerinel I, Coroller L et al (2012) Modelling microbial resistance during thermal treatment: secondary models. In: Valdramidis VP, Van Impe JF (eds) Progress on quantitative approaches for thermal food processing. Nova Scientific Publishers, Inc., New York, pp 99–119

    Google Scholar 

  • McCann MS, Sheridan JJ, McDowell DA et al (2006) Effects of steam pasteurisation on Salmonella Typhimurium DT104 and Escherichia coli O157:H7 surface inoculated onto beef, pork and chicken. J Food Eng 76(1):32–40

    Google Scholar 

  • Peleg M, Cole MB (1998) Reinterpretation of microbial survival curves. Crit Rev Food Sci Nutr 38(5):353–380

    CAS  PubMed  Google Scholar 

  • Reichart O (1994) Modelling the destruction of Escherichia coli on the base of reaction kinetics. Int J Food Microbiol 23(3–4):449–465

    CAS  PubMed  Google Scholar 

  • Ross T, McMeekin TA (1994) Predictive microbiology. Int J Food Microbiol 23(3–4):241–264

    CAS  PubMed  Google Scholar 

  • Rosso L, Lobry JR, Bajar S et al (1995) Convinient model to describe the combined effect of temperature and pH on microbial growth. Appl Env Microbiol 61(2):610–616

    CAS  Google Scholar 

  • Santillana Farakos SM, Frank JF, Schaffner DW (2013) Modeling the influence of temperature, water activity and water mobility on the persistence of Salmonella in low-moisture foods. Int J Food Microbiol 166(2):280–293

    CAS  Google Scholar 

  • Schoolfield RM, Sharpe PJH, Magnuon CE (1981) Non-linear regression of biological temperature-dependent rate models based on absolute reaction-rate theory. J Theor Biol 88(4):719–731

    CAS  PubMed  Google Scholar 

  • Simpson SG, Williams MC (1974) An analysis of high temperature/short time sterilization during laminar flow. J Food Sci 39(5):1047–1054

    Google Scholar 

  • Sörqvist S (2003) Heat resistance in liquids of Enterococcus spp., Listeria spp., Escherichia coli, Yersinia enterocolitica, Salmonella spp. and Campylobacter spp. Acta Vet Scand 44(1–2):1–19

    PubMed  PubMed Central  Google Scholar 

  • Splittstoesser, DF, McLellan MR, Churey JJ (1995) Heat resistance of Escherichia coli O157:H7 in Apple Juice. J Food Prot 59(3):226–229

    Google Scholar 

  • Tang J (2015) Unlocking potentials of microwaves for food safety and quality. J Food Sci 80(8):E1776–E1793

    CAS  PubMed  PubMed Central  Google Scholar 

  • Van Impe JF, Vercammen D, Van Derlinden E (2013) Towards a next generation of predictive models: a systems biology primer. Food Control 29(2):336–342

    Google Scholar 

  • Villa-Rojas R, Tang J, Wang S et al (2013) Thermal inactivation of Salmonella Enteritidis PT 30 in almond kernels as influenced by water activity. J Food Prot 76(1):26–32

    PubMed  Google Scholar 

  • Xezones H, Hutchings IJ (1965) Thermal resistance of Cl. Botulinum spores as affected by fundamental food constituents. Food Technol 19:113–115

    Google Scholar 

  • Xiong R, Xie G, Edmondson AE, Sheard MA (1999) A mathematical model for bacterial inactivation. Int J Food Microbiol 46(1):45–55

    CAS  PubMed  Google Scholar 

Download references

Acknowledgements

This work was supported by projects C24/18/046 and PFV/10/002 (Center of Excellence OPTEC-Optimization in Engineering) and grant PDM/18/136 of the KU Leuven Research Fund and by the Fund for Scientific Research-Flanders, project G.0863.18. This work was also partly supported by the CA15118 Mathematical and Computer Science Methods for Food Science and Industry (FoodMC).

Author information

Authors and Affiliations

  1. BioTeC, Chemical and Biochemical Process Technology and Control, Department of Chemical Engineering, KU Leuven, Ghent, Belgium

    Simen Akkermans, Cindy Smet & Jan Van Impe

  2. OPTEC, Optimization in Engineering Center-of-Excellence, KU Leuven, Leuven, Belgium

    Simen Akkermans, Cindy Smet & Jan Van Impe

  3. CPMF2, Flemish Cluster Predictive Microbiology in Foods – www.cpmf2.be, Ghent, Belgium

    Simen Akkermans, Cindy Smet & Jan Van Impe

  4. Department of Food Sciences and Nutrition, Faculty of Health Sciences, University of Malta, Msida, Malta

    Vasilis Valdramidis

  5. Department of Molecular Medicine and Biobanking, University of Malta, Msida, Malta

    Vasilis Valdramidis

Authors
  1. Simen Akkermans
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Cindy Smet
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vasilis Valdramidis
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Jan Van Impe
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Jan Van Impe .

Editor information

Editors and Affiliations

  1. Department of Agricultural and Biological Engineering, Pennsylvania State University, University Park, PA, USA

    Ali Demirci

  2. Department of Food Science and Human Nutrition, University of Illinois Urbana-Champaign, Urbana, IL, USA

    Hao Feng

  3. Department of Food Science and Nutrition, Institute for Food Safety and Health, Illinois Institute of Technology, Chicago, IL, USA

    Kathiravan Krishnamurthy

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Akkermans, S., Smet, C., Valdramidis, V., Van Impe, J. (2020). Microbial Inactivation Models for Thermal Processes. In: Demirci, A., Feng, H., Krishnamurthy, K. (eds) Food Safety Engineering. Food Engineering Series. Springer, Cham. https://doi.org/10.1007/978-3-030-42660-6_15

Download citation

Publish with us

Policies and ethics

Search

Navigation