PREDICTIVE MICROBIOLOGY OF FOOD

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Postępy Mikrobiologii - Advancements of Microbiology

Polish Society of Microbiologists

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VOLUME 57 , ISSUE 3 (April 2018) > List of articles

PREDICTIVE MICROBIOLOGY OF FOOD

Elżbieta Rosiak * / Katarzyna Kajak-Siemaszko / Monika Trząskowska / Danuta Kołożyn-Krajewska

Keywords : risks analysis, food microbiology, predictive models, predictive software

Citation Information : Postępy Mikrobiologii - Advancements of Microbiology. Volume 57, Issue 3, Pages 229-243, DOI: https://doi.org/10.21307/PM-2018.57.3.229

License : (CC BY-NC-ND 4.0)

Published Online: 23-May-2019

ARTICLE

ABSTRACT

The beginnings of predictive microbiology date back to 1920 when Bigelow developed a logarithmic-linear dependence of kinetics on the death of microorganisms. Predictive microbiology is a sub-discipline of food microbiology, whose task is to predict the behavior of microorganisms in food using mathematical models. The predictive model for microbiology is usually a simplified description of the correlation between the observed reactions and the factors responsible for the occurrence of these reactions. There are several main conceptual models (empirical vs. mechanistic, stochastic vs. deterministic, dynamic vs. static), in which there are model divisions depending on the type of examined microorganism or the nature of the problems caused by microbes (kinetic vs. probabilistic), described variables (first, secondary and tertiary) or the influence of environmental factors on microbial populations (growth, survival, inactivation). The new generations of models include molecular and genomic models, transfer models, Artificial Neural Network, interactions between species, and single cell models.
The process of creating a mathematical model requires coordination of work and the knowledge of: microbiology, statistics, mathematics, chemistry, process engineering and computer and web science. It also requires appropriate hardware and software. There are four stages in the construction of a mathematical model: planning; data collection and analysis; mathematical description; validation and storage of data.
In recent years, numerous computer software programs have been developed: FISHMAP, FSSP, Dairy Product Safety Predictor, Symbiosis, GroPIN, Listeria Meat FDA-iRISK, TRiMiCri, Microbial Responses, GlnaFiT, FILTREX, PMM-Lab. ComBase database, on the other hand, is a pioneering achievement as an on-line tool. Some programs meet the requirements for creating Food Safety Model Repositories (FSMR).

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REFERENCES

1. Akmel D.C., Tapé T. i wsp.: Quantitative assessment of the microbiological risk associated with the consumption of attieke in Côte d’Ivoire. Food Control, 81, 65–73 (2017)

2. Alber S.A., Schaffner D.W.: Evaluation of data transformations used with squere root and Schoolfield models for predicting bacterial growth rate. App. Environ. Microbiol. 58, 3337–3342 (1992)

3. Alber S.A., Schaffner D.W.: New modified squere root and Schoolfield models for predicting bacterial growth rate as a function of temperature. J. Ind. Microbiol. 12, 206–210 (1993)

4. Amina M., Panagou E.Z., Kodogiannis V.S., Nychas G-J.E.; Wavelet neural networks for modeling high pressure inactivation kinetics of Listeria monocytogenes in UHT whole milk. Chemom. Intell. Lab. Syst. Discipline. 103, 170–183 (2010)

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

6. Atsamnia D., Hamadache M., Hanini S., Benkortbi O., Oukrif D.: Prediction of the antibacterial activity of garlic extract on
E. coli, S. aureus and B. subtilis by determining the diameter of the inhibition zones using artificial neural networks. LWT
– Food Science and Technology, 82, 287–295 (2017)

7. Augustin J-Ch., Carlier V.: Modelling the growth rate of Listeria monocytogenes with a multiplicative type model including interactions between environmental factors. Int. J. Food Microbiol. 56, 53–70 (2000)

8. Baranyi J., da Silva N.S.: The use of predictive models to optimize risk of decisions. Int. J. Food Microbiol. 240, 19–23 (2017)

9. Baranyi J., Pin C.: Estimating bacterial growth parameters by means of detection times. Appl. Environ. Microbiol. 65, 732–736 (1999)

10. Baranyi J., Roberts T.A., McClure P.: A non-autonomous differential equation to model bacterial growth. Food Microbiol. 10, 43–59 (1993)

11. Barker-Reid F., Harper G.A., Hamilton A.J.: Affluent effluent: growing vegetables with wastewater in Melbourne, Australia
– a wealthy but bone-dry city. Irrigat. Drain Syst. 24, 79–94 (2010)

12. Basheer I.A., Hajmeer M.: Artificial neural networks: fundamentals, computing, design, and application. J. Microbiological Methods, Neural Computting in Micrbiology, 43, 3–31 (2000)

13. Bowman J., McMeekin T., McQuestion O., Mellefont L., Ross T., Tamplin M.: The future of predictive microbiology: strategic research, innovative applications and great expectations. Int. J. Food Microbiol. 128, 2–9 (2008)

14. Brucker S., Albrecht A., Petersen B., Kreyenschmidt J.: A predictive shelf life model as a tool for the improvement of quality management in pork and poultry chains. Food Control, 29, 451–460 (2013)

15. Brul S., Mensonides F.I.C., Hellingwerf K.J., Teixeira de Mattos J.M.: Microbial systems biology: New frontiers open to predictive microbiology. Int. J. Food Microbiol. 5th International Conference on Predictive Modelling in Foods, 128, 16–21 (2008)

16. Buchanan R.L.: Predictive food microbiology. Trends Food Sci. Technol. 4, 6–11 (1993)

17. Buchanan R.L., Damert W.G., Whiting R.C., Van Schothorst M.: An approach for using epidemiologic and microbial food survey data to develop a ‘purposefully conservative’ estimate of the dose-response relationship between Listeria monocytogenes levels and the incidence of foodborne listeriosis. J. Food Prot. 60, 918–922 (1997)

18. Buchanan R.L., Havelaar A.H., Smith M.A., Whiting R.C., Julien E.: The key events dose-response framework: its potential for application to foodborne pathogenic microorganisms. Crit. Rev. Food Sci. Nutr. 49, 718–728 (2009)

19. Buchanan R.L., Smith J.L., Long W.: Microbial risk assessment: Dose-response relations and risk characterization. Int. J. Food Microbiol. 58, 159–172 (2000)

20. Butler F., Xu Y.: Prediction of Staphylococcus aureus growth in ham during chilling using Pathogen Modeling Program. Biosystem Engineering, 16, 20–23 (2011)

21. Carrasco E., Perez-Rodriguez F., Valero A., Garcia-Gimeno R.M., Zurera G.: Risk assessment and management of Listeria monocytogenes in ready-to-eat lettuce salads. Compr. Rev. Food Sci. F. 9, 498–512 (2010)

22. Codex Alimentarius Commission. Report of the Thirtieth Session of the Codex Committee on Food Hygiene (CCFH). Alinorm 99/13 Appendix IV: s. 58–64 1999. http://www.codexalimentarius.net/download/report/112/Al99_13e.pdf (18.09.2017)

23. Coleman M.E., Marks H.M.: Qualitative and quantitative risk assessment, Food Control, 10, 289–297(1999).

24. Couvert O., Augustin J-Ch. i wsp. Validation of a stochastic modelling approach for Listeria monocytogenes growth in refrigerated foods. Int. J. Food Microbiol. 144, 236–242 (2010)

25. Daughtry B.J., Davey K.R., King K.D.: Temperature dependence of growth kinetics of food bacteria. Food Microbiol. 14, 21–30 (1997)

26. Davey K.R.: Applicability of the Davey (linear Arrhenius) predictive model to the lag phase of microbial growth. J. App. Bacteriol., 70, 253–257 (1991)

27. Davey K.R.: Linear-Arrhenius models for bacterial growth and depth and vitamin denaturations. J. In. Microbiol. 12, 172–179 (1993)

28. Davey K.R.: Modeling the combined effect of temperature and pH on the rate coefficient for bacterial growth. Int. J. Food Microbiol. 23, 295–303 (1994)

29. De Keuckelaere A., Jacxsens L., Amoah P., Medema G., McClure P., Jaykus L.-A., Uyttendaele M.: Zero risk does not exist: lessons learned from microbial risk assessment related to use of water and safety of fresh produce. Compr. Rev. Food Sci. F. 14, 387–410 (2015)

30. Dermesonluoglu E., Fileri K., Orfanoudaki A., Tsevdou M., Modeling the microbial spoilage and quality decay of pre-packed dandelion leaves as function of temperature. J. Food Eng. 184, 21–30 (2016)

31. Devlieghere F., Francois K., Vermeulen A., Debevere J.: Predictive microbiology (w) Predictive modeling and risk assessment. Integrating safety and environmental knowledge into food studies towards European sustainable development, red. R. Costa, K. Kristbergsson, Springer, Boston, 2009, s. 29–53

32. Dominguez S., Schaffner D.W.: Microbiological quantitative risk assessment (w) Safety of meat and processed meat, red. F. Toldrá, Springer, New York, 2009, s. 591–614

33. FAO/WHO. Microbiological Risk Assessment Series 2: Risk Assessments of Salmonella in Eggs and Broiler Chickens. World Health Organization, Food and Agricultural Organization of the United Nations, Geneva, Rome, 2002, www.fao.org/3/a-y4392e.pdf (11.09.2017)

34. FAO/WHO. Microbiological risk assessment series 3: Hazard characterization for pathogens in food and water. World Health Organization, Food and Agricultural Organization of the United Nations, Geneva, Roma, 2003, whqlibdoc.who.int/publications/2003/9241562374.pdf (11.09.2017)

35. Fernández J.C., Hervás C., Martínez-Estudillo F.J., Gutiérrez P.A.: Memetic Pareto Evolutionary Artificial Neural Networks to determine growth/no-growth in predictive microbiology. Appl. Soft Comput. 11, 534–550 (2011)

36. Fernández-Navarro F., Hervás-Martínez C., Cruz-Ramírez M., Gutiérrez P.A., Valero A.: Evolutionary q-Gaussian Radial Basis Function Neural Network to determine the microbial growth/no growth interface of Staphylococcus aureus. Appl. Soft Comput. 11, 3012–3020 (2011)

37. Ferrari A., Lombardi S., Signoroni A.: Bacterial colony counting with convolutional Neural Networks in Digital Microbiology Imaging. Patter Recognit. 61, 629–640 (2017)

38. Giaccone V., Ferri, M..: Microbiological quantitative risk assessment and food safety: an update. Vet. Res. Commun. 29, 101–106 (2005)

39. Guiller L.: Predictive microbiology models and operational readiness. Procedia Food Sci. 7, 133–136 (2016)

40. Gunvig A., Hansen F., Borggaard C.: A mathematical modeling for predicting growth/no-growth of psychrotrophic C. botulinum in meat products with five variables. Food Control, 29, 309–317 (2013)

41. Hamilton A.J., Stagnitti F., Premier R., Boland A.M., Hale G.: Quantitative microbial risk assessment models for consumption of raw vegetables irrigated with reclaimed water. Appl Environ Mikrob. 72, 3284–3290 (2006)

42. Heinemann, M., Sauer U.: Systems biology of microbial metabolism. Curr. Opin. Microbiol. 13, 337–43 (2010)

43. Huang L.: IPMP 2013 – A comprehensive data analysis tool for predictive microbiology. Int. J. Food Microbiol. 171, 100–107 (2014).

44. Huang L., Juneja V. K., Yan X.: Thermal inactivation of foodborne pathogens in the USA pathogen modeling program.
J. Therm. Anals. and Colorim. 106, 191–198 (2011)

45. Ilnicka-Olejniczak O., Hornecka D.: Prognozowanie w mikrobiologii żywności (w) Food Product Development. Opracowanie nowych produktów żywnościowych. Wydawnictwo AR, Poznań, 1995, s. 149–168

46. Julien E., Boobis A.R., Olin S.S.: The key events dose-response framework: a cross disciplinary mode-of-action based approach to examining dose-response and thresholds. Crit Rev. Food Sci. Nutr. 49, 682–689 (2009)

47. Keeratipibul S., Phewpan A., Lursinsap Ch.: Prediction of coliforms and Escherichia coli on tomato fruits and lettuce leaves after sanitizing by using Artificial Neural Networks. LWT-Food Sci. Technol. 44, 130–138 (2011)

48. Kilcast D., Subramanian P.: The stability and shelf-life of food. Woodhead Publishing, Cambridge, 2000

49. Kim H.W., Lee K., Kim S. H., Rhee M.S.: Predictive modeling of bacterial growth in ready-to-use salted napa cabbage (Brassica pekinensis) at different storage temperatures. Food Microbiol. 70, 129–136 (2018)

50. Klapwijk P.M., Jouve J.-L., Stringer M.F.: Microbiological risk assessment in Europe: the next decade. Int. J. Food Microbiol. 58, 223–230 (2000)

51. Koseki S.: Microbial responses viewer (MRV): A new ComBase-derived database of microbial responses to food environments. Int. J. Food Microbiol. 134, 75–82 (2009)

52. Koseki S.: Risk assessment of microbial and chemical contamination in fresh produce (w) Global Safety of Fresh Produce red. J. Hoorfan, Woodhead Publishing Limited, Cambridge, 2014, s. 153–171

53. Koutsoumanis, K.: A study on the variability in the growth limits of individual cells and its effect on the behavior of microbial populations. Int. J. Food Microbiol. 5th International Conference on Predictive Modelling in Foods, 128, 116–121 (2008)

54. Koutsoumanis K. P., Lianou A.: Stochasticity in Colonial Growth Dynamics of Individual Bacterial Cells. Appl. Environ. Microbiol. 79, 2294–2301 (2013)

55. Koutsoumanis K. P., Lianou A., Gougoul M.: Latest developments of foodborne pathogens modeling. Curr. Opin. Food Sci. 8, 89–98 (2016)

56. Krishnan K. R., Babuskin S., Babu P. A. S., Sivarajan M., Sukumar M.: Evaluation and predictive modeling the effect of spice extracts an raw chicken meat stored at different temperatures. J. Food Eng. 166, 29–37 (2015)

57. Lammerding, A.M., Fazil, A.: Hazard identification and exposure assessment for microbial food safety risk assessment. Int. J. Food Microbiol. 58, 147–157 (2000)

58. Larsen P., Yuki H., Gilbert J.: Modeling microbial communities: Current, developing, and future technologies for predicting microbial community interaction. J. Biotechnol. 160, 17–24 (2012)

59. Lim K.Y., Jiang S.C.: Reevaluation of health risk benchmark for sustainable water practice through risk analysis of rooftop-harvested rainwater. Water Res. 47, 7273–7286 (2013)

60. Mafart P.: Food engineering and predictive microbiology: on the necessity to combine biological and physical kinetics. Int. J. Food Microbiol. 100, 239–251 (2005)

61. Magnússon S.H., Verhagen H.: State of the art in benefit – risk analysis: Food microbiology. Food Chem. Toxicol. 50, 33–39 (2012)

62. McKellar C., Xuewen L.: Modeling microbial responses in food. CRC Press, USA, 2004

63. McKellar, R. C., Knight K.: A combined discrete-continuous model describing the lag phase of Listeria monocytogenes. Int. J. Food Microbiol. 54 (3), 171–80 (2000)

64. McMeekin T.A.: Predictive microbiology: quantitative science delivering quantifiable benefits to the meat industry and other food industries. Meat Science, 77, 17–27 (2007)

65. McMeekin T. A., Bowman J., McQuestin O., Mellefont L., Ross T., Tamplin M.: The future of predictive microbiology: Strategic research, innovative applications and great expectations. Int. J. Food Microbiol. 128, 2–9 (2008)

66. McMeekin T.A., Olley J., Ratkowsky D.A., Ross T.: Predictive microbiology: towards the interface and beyond. Int. J. Food Mocrobiol. 73, 395–407 (2002)

67. McMeekin T.A., Olley J.N., Ross T., Ratkowsky D.A.: Predictive microbiology, theory and application, RST LTD, England, 1993

68. McMeekin T. A., Ross T.: Predictive microbiology: providing a knowledge-based framework for change management. J. Food Microbiol. 1, 133–153 (2002)

69. Moon H., Kim S., Chen J., George N., Kodell R..: Model uncertainty and model averaging in the estimation of infectious dose for microbial pathogens. Risk Anal. 33, 220–231 (2013).

70. Mota A., Mena K.D., Soto-Beltran M., Tarwater P.M., Chaidez C.: Risk assessment of Cryptosporidium and Giardia in water irrigating fresh produce in Mexico. J. Food Protect. 72, 2184–2188 (2009)

71. Najjar Y.M., Basheer I.A., Hajmeer H.N.: Computional neural networks for predictive microbiology: I. Methodology. Int.
J. Food Mocrobiol. 34, 27–49 (1997)

72. Nauta M.J..: Modelling bacterial growth in quantitative microbiological risk assessment: is it possible? Int. J. Food Microbiol. 73, 297–304 (2002)

73. Nauta, M. van der Fels-Klerx I., Havelaar A.: A poultry-processing model for quantitative microbiological risk assessment. Risk Anal. 25, 85–98 (2005)

74. O’Mahony C., Seman D. L.: Modeling the microbial shelf life of Foods and Beverages (w) The Stability and Shelf Life of Food, red. Persis Subramaniam, Woodhead Publishing Series in Food Science, Cambridge, 2016, s. 253–289

75. Pérez-Rodríguez F., Campos D., Ryser E.T., Buchholz A.L,. Posada-Izquierdo G.D., Marks B.P., Zurera G., Todd E.C.D.: A mathematical risk model for Escherichia coli O157:H7 cross contamination of lettuce during processing. Food Microbiol. 28, 694–701 (2011)

76. Pérez-Rodríguez F., Valero A.: Application of Predictive Models in Quantitative Risk Assessment and Risk Management (w) Predictive Microbiology in Foods (red.) F. Pérez-Rodríguez, A. Valero, Springer, Berlin, 2013, s. 87–97

77. Pérez-Rodríguez F., Valero A., Carrasco E., García R.M., Zurera G.: Understanding and modeling bacterial transfer to foods: a review. Trends Food Sci. Technol. 19, 131–44 (2008)

78. Pérez-Rodríguez F., van Asselt E.D., Garcia-Gimeno R.M., Zurera G., Zwietering M.H.: Extracting additional risk managers information from a risk assessment of Listeria monocytogenes in deli meats. J. Food Prot. 70, 1137–1152 (2007)

79. Petterson S.R, Ashbolt N.J., Sharma A.: Microbial risks from wastewater irrigation of salad crops: a screening-level risk assessment. Water Environ. Res. 73, 667–672 (2001)

80. Plaza-Rodríguez C., Thoens C., Falenski A., Weiser A.A., Appel B., Kaesbohrer A., Filter M.: A strategy to establish Food safety Model Repositories. Int. J. Food Microbiol. 204, 81–90 (2015)

81. Pruitt K.M., Kamau D.N.: Mathematical model of bacterial growth, inhibition and depth under combined stress conditions. J. Ind. Microbiol. 12, 221–231 (1993)

82. Pujol L., Albert I., Magras C., Johnson N., B., Membre J.-M., Probabilistic exposure assessment model to estimate aseptic-UHT product failure rate. Int. J. Food Microbiol. 192, 124–141 (2015)

83. Ratkowsky D.A.: Principles of nonlinear regression modeling, J. Ind. Microbiol., 12, 195–199 (1993)

84. Roberts T. A.: Microbial Growth and Survival: Developments in Predictive Modelling. Int. Biodeterior. Biodegr. 36, 297–309 (1995)

85. Ross T., McMeekin T.A.: Predictive microbiology. Int. J. Food Mocrobiol. 23, 241–264 (1994)

86. Rozporządzenie Komisji (WE) nr 2073/2005 z dnia 15 listopada 2005 r. w sprawie kryteriów mikrobiologicznych dotyczących środków spożywczych. Dz.U. L 338 z 22.12.2005

87. Seliwiorstow T., Uyttendaele M., De Zutter L., Nauta M.: Application of TRiMiCri for the evaluation of risk based microbiological criteria for Campylobacter on broiler meat. Microb. Risk Anal. 2–3, 78–82 (2016)

88. Sheen S., Hwang Ch.-A.: Mathematical modeling the cross-contamination of Escherichia coli O157:H7 on the surface of ready-to-eat meat product while slicing. Food Microbiol. 27, 37–43 (2010)

89. Smith M.A., Takeuchi K., Anderson G., Ware G.O., McClure H.M., Raybourne R.B., Mytle N., Doyle M. P.: Dose response for Listeria monocytogenes induced stillbirths in nonhuman primates. Infect. Immun. 76, 726–731 (2008)

90. Strachan N.J.C., Doyle M.P., Kasuga F., Rotariu O., Ogden I.D.: Dose response modelling of Escherichia coli O157 incorporating data from foodborne and environmental outbreaks. Int. J. Food Microbiol. 10, 35–47 (2005)

91. Tadeusiewicz R.: Elementarne wprowadzenie do techniki sieci neuronowych z przykładowymi programami, Akademicka Oficyna Wydawnicza PLJ, Warszawa, 1998

92. Tadeusiewicz R.: Sieci Neuronowe, Akademicka Oficyna Wydawnicza RM, Warszawa, 1993

93. Tenenhaus-Aziza F., Ellouze M.: Software for predictive microbiology and risk assessment: A description and comparison of
tools presented at the ICPMF8 Software Fair, Food Microbiol. 45, 290–299 (2015)

94. Teunis, P.F.M., Van der Heijden, O.G., Van der Giessen, J.W.B., Havelaar, A.H.: The dose-response relation in human volunteers for gastro-intestinal pathogens, Report number 284550002. National Institute of Public Health and the Environment (RIVM), Bilthoven, The Netherlands 1996, http://www.rivm.nl/en/Documents_and_publications/Scientific/Reports/1996/april/The_dose_response_relation_in_human_volunteers_for_gastro_intestinal_pathogens (4.09.2017)

95. van Gerwen S., Zwietering M.H.: Growth and inactivation models to be used in quantitative risk assessments. J. Food Prot. 61, 1541–1549 (1998)

96. van Ginneken M., Oron G.: Risk assessment of consuming agricultural products irrigated with reclaimed wastewater: an exposure model. Water Resour. Res. 36, 2691–2699 (2000)

97. Whiting R.C.: Microbial modeling in food. Critical Reviews in Food Scie. and Nutrit. 35, 467–494 (1995)

98. Whiting, R. C., Buchanan R. L.: A classification of models for predictive microbiology. Food Microbiology, 10, 175–177 (1993)

99. Williams D., Irvin E.A., Chmielewski R.A., Frank J.F., Smith M.A.: Dose response of Listeria monocytogenes after oral exposure in pregnant guinea pigs. J. Food Prot. 70, 1122–1128 (2007)

100. Zwietring M.H., Jongenburger I., Rombouts F.M., Van’t Rient K.: Modeling of the bacterial growth curve. Appl. Environ. Microbiol. 56, 1875–1881 (1990)

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