Abstract

Microbial death kinetics modelling is an integral stage of developing the food thermal sterilisation regimes. At present, a large number of models have been developed. Their properties are usually being accepted as adequate even beyond boundaries of experimental microbiological data zone. The wide range of primary models existence implies the lack of universality of each ones. This paper presents a comparative assessment of linear and nonlinear models of microbial death kinetics during the heat treatment of the Alicyclobacillus acidoterrestris spore form. The research allowed finding that single-phase primary models (as adjustable functions) are statistically acceptable for approximation of the experimental data: linear – the Bigelow’ the Bigelow as modified by Arrhenius and the Whiting-Buchanan models; and nonlinear – the Weibull, the Fermi, the Kamau, the Membre and the Augustin models. The analysis of them established a high degree of variability for extrapolative characteristics and, as a result, a marked empirical character of adjustable functions, i.e. unsatisfactory convergence of results for different models. This is presumably conditioned by the particularity and, in some cases, phenomenology of the functions themselves. Consequently, there is no reason to believe that the heat treatment regimes, developed on the basis of any of these empirical models, are the most effective. This analysis is the first link in arguing the necessity to initiate the research aimed at developing a new methodology for determining the regimes of food thermal sterilisation based on analysis of the fundamental factors such as ones defined spore germination activation and their resistance to external impact.

Keywords

Microorganisms, death kinetics, survival kinetics, sterilising effect, Alicyclobacillus acidoterrestris, model

REFERENCES

1. Galstyan AG, Radaeva IA, Chervetsov VV, Turovskaya SN, Illarionova EE, Petrov AN. Obnovlennye mezhgosudarstvennye standarty na konservy [Updated interstate standards for canned food]. Milk Processing. 2016;197(3):28–33. (In Russ.).
2. Prosekov AY, Ivanova SA. Food security: The challenge of the present. Geoforum. 2018;91:73–77. DOI: https://doi.org/10.1016/j.geoforum.2018.02.030.
3. Galstyan AG, Aksyonova LM, Lisitsyn AB, Oganesyants LA, Petrov AN. Modern approaches to storage and effective processing of agricultural products for obtaining high-quality food products. Vestnik Rossijskoj akademii nauk. 2019;89(5):539–542. (In Russ.). DOI: https://doi.org/10.31857/S0869-5873895539-542.
4. Turovskaya SN, Galstyan AG, Petrov AN, Radaeva IA, Illarionova EE, Semipyatniy VK, et al. Safety of canned milk as an integrated criterion of their technology effectiveness. Russian experience. Food Systems. 2018;1(2):29–54. (In Russ.). DOI: https://doi.org/10.21323/2618-9771-2018-1-2-29-54.
5. Barron UAG. Modeling Thermal Microbial Inactivation Kinetics. In: Sun D-W, editor. Thermal Food Processing: New Technologies and Quality Issues, Second Edition. CRC Press; 2012. pp. 151–191. DOI: https://doi.org/10.1201/b12112.
6. Bevilacqua A, Speranza B, Sinigaglia M, Corbo MR. A Focus on the Death Kinetics in Predictive Microbiology: Benefits and Limits of the Most Important Models and Some Tools Dealing with Their Application in Foods. Foods. 2015;4(4):565–580.
7. Forghani F, den Bakker M, Futral AN, Diez-Gonzalez F. Long-Term Survival and Thermal Death Kinetics of Enterohemorrhagic Escherichia coli Serogroups O26, O103, O111, and O157 in Wheat Flour. Applied and Environmental Microbiology. 2018;84(13). DOI: https://doi.org/10.1128/aem.00283-18.
8. Li R, Kou XX, Zhang LH, Wang SJ. Inactivation kinetics of food-borne pathogens subjected to thermal treatments: a review. International Journal of Hyperthermia. 2018;34(2):177–188. DOI: https://doi.org/10.1080/02656736.2017. 1372643.
9. Whiting RC, Buchanan RL. Predictive microbiology In: Doyle MP, Beuchat LR, Montville TJ, editors. Food Microbiology: Fundamentals and Frontier. Washington, DC: ASM Press; 1997. pp. 728–739.
10. Chick H. An Investigation of the Laws of Disinfection. Journal of Hygiene. 1908;8(1):92–158. DOI: https://doi.org/10.1017/S0022172400006987.
11. Bigelow WD, Bohart GS, Richardson AC, Ball CO. Heat Penetration in Processing Canned Foods. Washington, DC: Research Laboratory, National Canners Association; 1920.
12. Bigelow WD. The logarithmic nature of thermal death time curves. Journal of Infectious Diseases. 1921;29(5):528–536. DOI: https://doi.org/10.1093/infdis/29.5.528.
13. Schaffner DW, Labuza TP. Predictive microbiology: Where are we, and where are we going? Food Technology. 1997;51(4):95–99.
14. Peleg M, Engel R, Gonzalez-Martinez C, Corradini MG. Non-Arrhenius and non-WLF kinetics in food systems. Journal of the Science of Food and Agriculture. 2002;82(12):1346–1355. DOI: https://doi.org/10.1002/jsfa.1175.
15. Xiong R, Xie G, Edmondson AE, Sheard MA. A mathematical model for bacterial inactivation. International Journal of Food Microbiology. 1999;46(1):45–55. DOI: https://doi.org/10.1016/s0168-1605(98)00172-x.
16. McKellar RC, Lu X. Modelling microbial responses in foods. CRC Press, 2003. 360 p.
17. Buchanan RL, Golden MH, Whiting RC. Differentiation of the effects of pH and lactic or acetic-acid concentration on the kinetics of listeria-monocytogenes inactivation. Journal of Food Protection. 1993;56(6):474–478.
18. Whiting RC. Modeling bacterial survival in unfavorable environments. Journal of Industrial Microbiology. 1993;12 (3–5):240–246. DOI: https://doi.org/10.1007/bf01584196.
19. van Boekel M. On the use of the Weibull model to describe thermal inactivation of microbial vegetative cells. International Journal of Food Microbiology. 2002;74(1–2):139–159. DOI: https://doi.org/10.1016/s0168-1605(01)00742-5.
20. Mafart P, Couvert O, Gaillard S, Leguerinel I. On calculating sterility in thermal preservation methods: application of the Weibull frequency distribution model. International Journal of Food Microbiology. 2002;72(1–2):107–113. DOI: https://doi.org/10.1016/s0168-1605(01)00624-9.
21. Bhaduri S, Smith PW, Palumbo SA, Turner-Jones CO, Smith JL, Marmer BS, et al. Thermal destruction of Listeria monocytogenes in liver sausage slurry. Food Microbiology. 1991;8:75–78.
22. Casolari A. Microbial death. In: Bazin MJ, Prosser JL, editors. Physiological Models in Microbiology. Volume 2. Boca Raton, FL: CRC Press; 1988. pp. 1–44.
23. Geeraerd AH, Herremans CH, Van Impe JF. Structural model requirements to describe microbial inactivation during a mild heat treatment. International Journal of Food Microbiology. 2000;59(3):185–209. DOI: https://doi.org/10.1016/s0168-1605(00)00362-7.
24. Daugthry BJ, Davey KR, Thomas CJ, Verbyla AP. Food processing – a new model for the thermal destruction of contaminating bacteria. In: Jowitt R, editor. Engineering and Food at ICEF7. Sheffield: Sheffield Academic Press; 1997. pp. A113–A116.
25. McKellar RC, Lu X. Primary Models. In: McKellar RC, Lu X, editors. Modelling microbial responses in foods. CRC Press, 2003. pp. 21–27.
26. Bermudez-Aguirre D, Corradini MG. Inactivation kinetics of Salmonella spp. under thermal and emerging treatments: A review. Food Research International. 2012;45(2):700–712. DOI: https://doi.org/10.1016/j.foodres.2011.05.040.
27. Cole MB, Davies KW, Munro G, Holyoak CD, Kilsby DC. A vitalistic model to describe the thermal inactivation of listeria-monocytogenes. Journal of Industrial Microbiology. 1993;12(3–5):232–239. DOI: https://doi.org/10.1007/bf01584195.
28. Membre JM, Majchrzak V, Jolly I. Effects of temperature, pH, glucose, and citric acid on the inactivation of Salmonella typhimurium in reduced calorie mayonnaise. Journal of Food Protection. 1997;60(12):1497–1501.
29. Kamau DN, Doores S, Pruitt KM. Enhanced thermal-destruction of listeria-monocytogenes and Staphylococcusaureus by the lactoperoxidase system. Applied and Environmental Microbiology. 1990;56(9):2711–2716.
30. Xiong R, Xie G, Edmondson AS, Linton RH, Sheard MA. Comparison of the Baranyi model with the modified Gompertz equation for modelling thermal inactivation of Listeria monocytogenes Scott A. Food Microbiology. 1999;16(3):269–279. DOI: https://doi.org/10.1006/fmic.1998.0243.
31. Augustin JC, Carlier V, Rozier J. Mathematical modelling of the heat resistance of Listeria monocytogenes. Journal of Applied Microbiology. 1998;84(2):185–191.
32. Fernandez A, Salmeron C, Fernandez PS, Martinez A. Application of a frequency distribution model to describe the thermal inactivation of two strains of Bacillus cereus. Trends in Food Science & Technology. 1999;10(4–5):158–162. DOI: https://doi.org/10.1016/s0924-2244(99)00037-0.
33. Chiruta J, Davey KR, Thomas CJ. Combined effect of temperature and pH on microbial death in continuous pasteurization of liquids. In: Jowitt R, editor. Engineering and Food at ICEF7. Sheffield: Sheffield Academic Press; 1997. pp. A109–A112.
34. Cerf O. Tailing of survival curves of bacterial-spores. Journal of Applied Bacteriology. 1977;42(1):1–19. DOI: https://doi.org/10.1111/j.1365-2672.1977.tb00665.x.
35. Whiting RC, Buchanan RL. Use of predictive microbial modeling in a HACCP program. Proceedings of the Second ASEPT International Conference: Predictive Microbiology and HACCP; 1992; Laval. Laval: ASEPT; 1992. pp. 125–141.
36. Coroller L, Leguerinel I, Mettler E, Savy N, Mafart P. General Model, Based on Two Mixed Weibull Distributions of Bacterial Resistance, for Describing Various Shapes of Inactivation Curves. Applied and Environmental Microbiology. 2006;72(10):6493–6502. DOI: https://doi.org/10.1128/aem.00876-06.
37. Method on the Detection of Taint Producing Alicyclobacillus in Fruit Juices. 2007.
38. Aneja KR, Dhiman R, Kumar NA, Aneja A. Review Article. Emerging Preservation Techniques for Controlling Spoilage and Pathogenic Microorganisms in Fruit Juices. International Journal of Microbiology. 2014;2014. DOI: https://doi.org/10.1155/2014/758942.
39. ISO 7218:2007. Microbiology of Food and Animal Feeding Stuffs – General Requirements and Guidance for Microbiological Examinations. 2013.