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Stochastic inactivation evaluation of foodborne pathogens during ohmic heating of poultry meat

  • Sebahattin Serhat Turgut ORCID logo EMAIL logo and Aberham Hailu Feyissa ORCID logo
Published/Copyright: November 16, 2023
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International Journal of Food Engineering
From the journal International Journal of Food Engineering Volume 19 Issue 12

Abstract

The objective of this study is to stochastically assess the inactivation probabilities of four common foodborne pathogens (Listeria, Salmonella, Escherichia coli, and Campylobacter) in chicken meat during ohmic heating (OH) in a salt solution. A mechanistic model was used to accomplish this, coupling heat transfer, laminar fluid flow, and the electric field, and solved numerically using COMSOL Multiphysics® v5.6. The 3D model represented 1000 particles randomly placed on the meat’s surface to determine the 7-log reduction of bacterial load probability. These particles are virtual representatives of bacterial colonies in the model. The influence of uncertain input parameters (specific heat capacity and electrical conductivity) and OH conditions (salt concentration of the heating medium, applied voltage, and heating time) was explained using logistic regression. The same analysis was repeated for the slowest heating point of chicken meat, as well. According to the findings, cold spots are observed at the corners of the meat piece during OH, requiring additional attention to the meat surface temperature to prevent under-processing. Sensitivity analysis revealed that the applied voltage and brine concentration are the main factors affecting the inactivation probabilities of pathogenic bacterial cells on the chicken meat surface. Salmonella and Listeria may require higher electrical conductivity of chicken meat and longer processing times. The developed model enables predicting inactivation probabilities of microorganisms that can be found on the outer surface by measuring the core temperature of the meat. However, especially for bacteria with higher heat resistance, it is better to consider the cold spot temperature found in the corners of the food material during OH.

Keywords: Listeria ; Salmonella ; E. coli ; logistic regression; survival
Corresponding author: Sebahattin Serhat Turgut, Research Group for Food Production Engineering, National Food Institute, Technical University of Denmark (DTU), Lyngby, Denmark; and Department of Food Engineering, Faculty of Engineering, Suleyman Demirel University, Isparta, Türkiye, E-mail:

Acknowledgments

The main mechanistic model used in this study was previously presented at the 8th International Food Operations & Processing Simulation Workshop (19-21 September 2022, Rome, Italy), and its scope has been extended for the special issue of the conference.

  1. Research ethics: Not applicable.

  2. Author contributions: All authors have accepted responsibility for the entire content of this manuscript and approved its submission.

  3. Competing interests: Authors state no conflict of interest.

  4. Research funding: None declared.

  5. Data availability: Data available on reasonable request from the authors.

References

1. Bhat, ZF, Morton, JD, Bekhit, AEDA, Kumar, S, Bhat, HF. Emerging processing technologies for improved digestibility of muscle proteins. Trends Food Sci Technol 2021;110:226–39, https://doi.org/10.1016/j.tifs.2021.02.010.Search in Google Scholar

2. Yao, M, Ali Khan, I, Cheng, Y, Ang, Y, Zhou, X, Huang, M. Effects of cooking methods and tea marinades on the formation of heterocyclic amines and benzo[a]pyrene in grilled drumsticks. J Food Prot 2020;83:365–76, https://doi.org/10.4315/0362-028x.jfp-19-084.Search in Google Scholar PubMed

3. Song, JH, Kim, MJ, Kim, YJ, Lee, JH. Monitoring changes in acid value, total polar material, and antioxidant capacity of oils used for frying chicken. Food Chem 2017;220:306–12, https://doi.org/10.1016/j.foodchem.2016.09.174.Search in Google Scholar PubMed

4. Silva, FAP, Ferreira, VCS, Madruga, MS, Estévez, M. Effect of the cooking method (grilling, roasting, frying and sous-vide) on the oxidation of thiols, tryptophan, alkaline amino acids and protein cross-linking in jerky chicken. J Food Sci Technol 2016;53:3137–46, https://doi.org/10.1007/s13197-016-2287-8.Search in Google Scholar PubMed PubMed Central

5. Naveena, BM, Muthukumar, M, Muthulakshmi, L, Anjaneyulu, ASR, Kondaiah, N. Effect of different cooking methods on lipid oxidation and microbial quality of vacuum-packaged emulsion products from chicken. J Food Process Preserv 2014;38:39–47, https://doi.org/10.1111/j.1745-4549.2012.00740.x.Search in Google Scholar

6. Mottram, DS. Flavour formation in meat and meat products: a review. Food Chem 1998;62:415–24, https://doi.org/10.1016/s0308-8146(98)00076-4.Search in Google Scholar

7. de Verdier, MG, Hagman, U, Peters, RK, Steineck, G, Övervik, E. Meat, cooking methods and colorectal cancer: a case-referent study in Stockholm. Int J Cancer 1991;49:520–5, https://doi.org/10.1002/ijc.2910490408.Search in Google Scholar PubMed

8. Kanjanapongkul, K. Rice cooking using ohmic heating: determination of electrical conductivity, water diffusion and cooking energy. J Food Eng 2017;192:1–10, https://doi.org/10.1016/j.jfoodeng.2016.07.014.Search in Google Scholar

9. Varghese, KS, Pandey, MC, Radhakrishna, K, Bawa, AS. Technology, applications and modelling of ohmic heating: a review. J Food Sci Technol 2014;51:2304–17, https://doi.org/10.1007/s13197-012-0710-3.Search in Google Scholar PubMed PubMed Central

10. Goullieux, A, Pain, JP. Ohmic heating. In: Emerging Technologies for Food Processing. Amsterdam: Academic Press; 2014:399–426 pp.10.1016/B978-0-12-411479-1.00022-XSearch in Google Scholar

11. Turgut, SS, Küçüköner, E, Feyissa, AH, Karacabey, E. A novel drying system – simultaneous use of ohmic heating with convectional air drying: system design and detailed examination using CFD. Innov Food Sci Emerg Technol 2021;72:102727, https://doi.org/10.1016/j.ifset.2021.102727.Search in Google Scholar

12. Jun, S, Sastry, S. Modeling and optimization of ohmic heating of foods inside a flexible package. J Food Process Eng 2005;28:417–36, https://doi.org/10.1111/j.1745-4530.2005.00032.x.Search in Google Scholar

13. Akkara, M, Kayaardı, S. Effect of advanced preservation techniques on meat quality. Akademik Gıda 2014;12:79–85.Search in Google Scholar

14. Shim, JY, Lee, SH, Jun, S. Modeling of ohmic heating patterns of multiphase food products using computational fluid dynamics codes. J Food Eng 2010;99:136–41, https://doi.org/10.1016/j.jfoodeng.2010.02.009.Search in Google Scholar

15. Turgut, SS, Siamos, E, Feyissa, AH. Simulation and parametric sensitivity evaluation for ohmic heating of chicken breast. In: Proceedings of the 8th International Food Operations and Processing Simulation Workshop (FoodOPS 2022) 2022. https://doi.org/10.46354/i3m.2022.foodops.009.Search in Google Scholar

16. Rouger, A, Tresse, O, Zagorec, M. Bacterial contaminants of poultry meat: sources, species, and dynamics. Microorganisms 2017;5:50, https://doi.org/10.3390/microorganisms5030050.Search in Google Scholar PubMed PubMed Central

17. Murphy, RY, Marks, BP, Johnson, ER, Johnson, MG. Thermal inactivation kinetics of Salmonella and Listeria in ground chicken breast meat and liquid medium. J Food Sci 2000;65:706–10, https://doi.org/10.1111/j.1365-2621.2000.tb16076.x.Search in Google Scholar

18. Murphy, RY, Duncan, LK, Berrang, ME, Marcy, JA, Wolfe, RE. Thermal inactivation D- and Z-values of Salmonella and Listeria innocua in fully cooked and vacuum packaged chicken breast meat during postcook heat treatment. Poult. Sci. 2002;81:1578–83, https://doi.org/10.1093/ps/81.10.1578.Search in Google Scholar PubMed

19. de Jong, AEI, van Asselt, ED, Zwietering, MH, Nauta, MJ, de Jonge, R. Extreme heat resistance of food borne pathogens Campylobacter jejuni, Escherichia coli, and Salmonella typhimurium on chicken breast fillet during cooking. Int. J. Microbiol. 2012;2012:e196841, https://doi.org/10.1155/2012/196841.Search in Google Scholar PubMed PubMed Central

20. Osaili, TM, Al-Nabulsi, AA, Shaker, RR, Olaimat, AN, Jaradat, ZW, Holley, RA. Thermal inactivation of Salmonella Typhimurium in chicken shawirma (gyro). Int J Food Microbiol 2013;166:15–20, https://doi.org/10.1016/j.ijfoodmicro.2013.06.009.Search in Google Scholar PubMed

21. Osaili, T, Griffis, CL, Martin, EM, Beard, BL, Keener, A, Marcy, JA. Thermal inactivation studies of Escherichia coli O157:H7, Salmonella, and Listeria monocytogenes in ready-to-eat chicken-fried beef patties. J. Food Prot. 2006;69:1080–6, https://doi.org/10.4315/0362-028x-69.5.1080.Search in Google Scholar PubMed

22. Al-Sakkaf, A. Thermal inactivation and kinetic parameters for Campylobacter jejuni on chicken skin. Can J Microbiol 2021;67:623–38, https://doi.org/10.1139/cjm-2020-0543.Search in Google Scholar PubMed

23. Stringer, SC, George, SM, Peck, MW. Thermal inactivation of Escherichia coli O157:H7. J Appl Microbiol 2000;88:79S–89S, https://doi.org/10.1111/j.1365-2672.2000.tb05335.x.Search in Google Scholar PubMed

24. Dominguez-Hernandez, E, Salaseviciene, A, Ertbjerg, P. Low-temperature long-time cooking of meat: eating quality and underlying mechanisms. Meat Sci 2018;143:104–13, https://doi.org/10.1016/j.meatsci.2018.04.032.Search in Google Scholar PubMed

25. Silva, FVM, Gibbs, PA. Thermal pasteurization requirements for the inactivation of Salmonella in foods. Food Res Int 2012;45:695–9, https://doi.org/10.1016/j.foodres.2011.06.018.Search in Google Scholar

26. U.S. Food SafetyInspection Service. Risk assessment of the impact of lethality standards on salmonellosis from ready-to-eat meat and poultry products; 2005.Search in Google Scholar

27. Khodeir, M, Rouaud, O, Ogé, A, Jury, V, Le-Bail, P, Le-Bail, A. Study of continuous cake pre-baking in a rectangular channel using ohmic heating. Innov. Food Sci. Emerg. Technol. 2021;67:102580, https://doi.org/10.1016/j.ifset.2020.102580.Search in Google Scholar

28. Kim, SS, Choi, W, Park, SH, Kang, DH. Mathematical modeling of ohmic heating for inactivation of acid-adapted foodborne pathogens in tomato juice. Int J Food Eng 2020;16:20190388. https://doi.org/10.1515/ijfe-2019-0388.Search in Google Scholar

29. Patel, A, Singh, M, De Pilli, T. Ohmic heating for food products – a review. Curr. Appl. Sci. Technol. 2018;27:1–7, https://doi.org/10.9734/cjast/2018/40664.Search in Google Scholar

30. Sarang, S, Sastry, SK, Gaines, J, Yang, TCS, Dunne, P. Product formulation for ohmic heating: blanching as a pretreatment method to improve uniformity in heating of solid–liquid food mixtures. J Food Sci 2007;72:E227–34, https://doi.org/10.1111/j.1750-3841.2007.00380.x.Search in Google Scholar PubMed

31. Sarang, S, Sastry, SK, Knipe, L. Electrical conductivity of fruits and meats during ohmic heating. J Food Eng 2008;87:351–6, https://doi.org/10.1016/j.jfoodeng.2007.12.012.Search in Google Scholar

32. Tulsiyan, P, Sarang, S, Sastry, SK. Electrical conductivity of multicomponent systems during ohmic heating. Int J Food Prop 2008;11:233–41, https://doi.org/10.1080/10942910701302580.Search in Google Scholar

33. de Albuquerque, CD, Curet, S, Boillereaux, L. A 3D-CFD-heat-transfer-based model for the microbial inactivation of pasteurized food products. Innov. Food Sci. Emerg. Technol. 2019;54:172–81, https://doi.org/10.1016/j.ifset.2019.04.007.Search in Google Scholar

34. Choi, W, Kim, SS, Park, SH, Ahn, JB, Kang, DH. Numerical analysis of rectangular type batch ohmic heater to identify the cold point. Food Sci Nutr 2020;8:648–58, https://doi.org/10.1002/fsn3.1353.Search in Google Scholar PubMed PubMed Central

35. Engchuan, W, Jittanit, W, Garnjanagoonchorn, W. The ohmic heating of meat ball: modeling and quality determination. Innov. Food Sci. Emerg. Technol. 2014;23:121–30, https://doi.org/10.1016/j.ifset.2014.02.014.Search in Google Scholar

36. Guo, W, Llave, Y, Jin, Y, Fukuoka, M, Sakai, N. Mathematical modeling of ohmic heating of two-component foods with non-uniform electric properties at high frequencies. Innov. Food Sci. Emerg. Technol. 2017;39:63–78, https://doi.org/10.1016/j.ifset.2016.11.005.Search in Google Scholar

37. Jiang, X, Li, L, Shen, W, Zhou, J. Numerical simulation of inhomogeneous food with ohmic heating. Int J Food Eng 2010;6, https://doi.org/10.2202/1556-3758.1775.Search in Google Scholar

38. Marra, F, Zell, M, Lyng, JG, Morgan, DJ, Cronin, DA. Analysis of heat transfer during ohmic processing of a solid food. J Food Eng 2009;91:56–63, https://doi.org/10.1016/j.jfoodeng.2008.08.015.Search in Google Scholar

39. Salengke, S, Sastry, SK. Models for ohmic heating of solid–liquid mixtures under worst-case heating scenarios. J Food Eng 2007;83:337–55, https://doi.org/10.1016/j.jfoodeng.2007.03.026.Search in Google Scholar

40. Zell, M, Cronin, DA, Morgan, DJ, Marra, F, Lyng, JG. Solid food pasteurization by ohmic heating: influence of process parameters. In: COMSOL Conference; 2008.Search in Google Scholar

41. Bird, RB, Stewart, WE, Lightfoot, EN. Transport phenomena. USA: John Wiley & Sons, Inc.; 2000.Search in Google Scholar

42. De Alwis, AAP, Fryer, PJ. A finite-element analysis of heat generation and transfer during ohmic heating of food. Chem Eng Sci 1990;45:1547–59, https://doi.org/10.1016/0009-2509(90)80006-z.Search in Google Scholar

43. Smelt, JPPM, Brul, S. Thermal inactivation of microorganisms. Crit Rev Food Sci Nutr 2014;54:1371–85, https://doi.org/10.1080/10408398.2011.637645.Search in Google Scholar PubMed

44. Espinosa, MF, Sancho, AN, Mendoza, LM, Mota, CR, Verbyla, ME. Systematic review and meta-analysis of time-temperature pathogen inactivation. Int J Hyg Environ Health 2020;230:113595, https://doi.org/10.1016/j.ijheh.2020.113595.Search in Google Scholar PubMed

45. Stavropoulou, E, Bezirtzoglou, E. Predictive modeling of microbial behavior in food. Foods 2019;8:654. https://doi.org/10.3390/foods8120654.Search in Google Scholar PubMed PubMed Central

46. Soni, A, Bremer, P, Brightwell, G. A comprehensive review of variability in the thermal resistance (D-values) of food-borne pathogens—a challenge for thermal validation trials. Foods 2022;11:4117. https://doi.org/10.3390/foods11244117.Search in Google Scholar PubMed PubMed Central

47. Saltelli, A. Sensitivity analysis for importance assessment. Risk Anal 2002;22:579–90. https://doi.org/10.1111/0272-4332.00040.Search in Google Scholar PubMed

48. Baratloo, A, Hosseini, M, Negida, A, El Ashal, G. Part 1: simple definition and calculation of accuracy, sensitivity and specificity. Emergency 2015;3:48–9.Search in Google Scholar

49. Royston, P, Altman, DG. Visualizing and assessing discrimination in the logistic regression model. Stat Med 2010;29:2508–20, https://doi.org/10.1002/sim.3994.Search in Google Scholar PubMed

50. Allison, PD. Logistic regression using SAS: theroy and application. NC, USA: SAS Institute; 2012.Search in Google Scholar

51. Buzrul, S. Modeling and simulating of the high hydrostatic pressure inactivation of microorganisms in foods. In: Improving Food Quality with Novel Food Processing Technologies. Boca Raton: CRC Press; 2014:195–213 pp.Search in Google Scholar

52. Cariboni, J, Gatelli, D, Liska, R, Saltelli, A. The role of sensitivity analysis in ecological modelling. Ecol Model 2007;203:167–82. https://doi.org/10.1016/j.ecolmodel.2005.10.045.Search in Google Scholar

53. Cebrián, G, Mañas, P, Condón, S. Comparative resistance of bacterial foodborne pathogens to non-thermal technologies for food preservation. Front Microbiol 2016;7:734, https://doi.org/10.3389/fmicb.2016.00734.Search in Google Scholar PubMed PubMed Central

54. Buzrul, S. High hydrostatic pressure inactivation of microorganisms: a probabilistic model for target log-reductions. Int J Food Microbiol 2019;309:108330, https://doi.org/10.1016/j.ijfoodmicro.2019.108330.Search in Google Scholar PubMed

55. Alters, RE, May, KN. Thermal conductivity and density of chicken breast muscle and skin. Food Technol 1963;17:808–11.Search in Google Scholar

56. McKetta, JJ. Encyclopedia of chemical processing and design. CRC Press; 1995, vol 52.Search in Google Scholar

57. Geankoplis, CJ. Transport processes and separation process principles. Prentice Hall Professional Technical Reference; 2003.Search in Google Scholar
Received: 2023-01-22
Accepted: 2023-10-16
Published Online: 2023-11-16

© 2023 Walter de Gruyter GmbH, Berlin/Boston

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  2. Critical Review
  3. Value addition of mushrooms by incorporation in the food products: an overview
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  5. Stochastic inactivation evaluation of foodborne pathogens during ohmic heating of poultry meat
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https://doi.org/10.1515/ijfe-2023-0022
Keywords for this article
Listeria; Salmonella; E. coli; logistic regression; survival

Articles in the same Issue

  1. Frontmatter
  2. Critical Review
  3. Value addition of mushrooms by incorporation in the food products: an overview
  4. Articles
  5. Stochastic inactivation evaluation of foodborne pathogens during ohmic heating of poultry meat
  6. Modelling of convective drying of potatoes polyhedrons
  7. Effect of medicine food homology Penthorum chinense Pursh on physicochemical property and volatile flavor substances analysis of Chinese sausage
  8. Effect of maceration, ultrasound, and microwave-assisted method of extraction on antioxidant activity and phenolic profile of free, esterified, and bound phenolics of Tulaipanji rice
  9. Ultrafiltration associated with microporous resin decolorizing the Bacillus subtilis fermentation broth for the production of γ-polyglutamic acid
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