Prediktif modellemelerin gıdalarla ilgili mikrobiyal çalışmalarda kullanımı

Öz

Gıdaların mikrobiyolojik kalitesini ve güvenliğini sağlama ihtiyacı, mikrobiyal davranışı ölçme ve tahmin etme maksadıyla matematiksel modellerin kullanımına olan ilgiyi artırmıştır. Son zamanlarda, gıda kaynaklı patojen bakterilerin üremesini tahmin etmek için prediktif mikrobiyoloji geliştirilmiştir. Prediktif mikrobiyoloji modelleri mikrobiyal gıda güvenliğini ve kalitesini iyileştirmek için pratik uygulamaya sahiptir. Son yıllarda prediktif modelleme yaklaşımıyla ilgili yapılan çalışma sayısında artış mevcuttur. Bu çalışmada gıda mikrobiyolojisi alanında kullanılan bazı matematiksel modellerin (prediktif modeller) kullanımı derlenmeye çalışılmıştır.

Anahtar Kelimeler

• Matematiksel modelleme
• Prediktif model
• Gıda
• Bakteri
• Raf ömrü

The use of predictive models in food related microbial studies

Öz

The need to ensure the microbiological quality and safety of foods has increased interest in the use of mathematical models to measure and predict microbial behavior. Recently, predictive microbiology has been developed to predict the growth of foodborne pathogenic bacteria. Predictive microbiology models have practical application to improve microbial food safety and quality. In recent years, there has been an increase in the number of studies on the predictive modeling approach. In this study, the use of some mathematical models (predictive models) used in the field of food microbiology has been reviewed.

Anahtar Kelimeler

• Mathematical modeling
• Predictive model
• Food
• Bacteria
• Shelf life

Kaynakça

1. M. Mutlu, Matematik modelleme ve gıda mühendisliğinde kullanımı. 9. Gıda Kongresi, sayfa 31-32, Bolu, Türkiye, 24-26 Mayıs 2006.
2. Y. O. Devres ve M. Pala, Gıda sanayiinde matematiksel modellemenin önemi ve uygulama alanları. Gıda, 18 (3), 173-181, 1993.
3. L. Verschaffel, B. Greer and E. De Corte, Everyday knowledge and mathematical modeling of school word problems. In: Gravemeijer, K., Lehrer, R., Van Oers, B., Verschaffel, L. (eds) Symbolizing, Modeling and Tool Use in Mathematics Education. Mathematics Education Library, Springer, Dordrecht, vol 30, pp. 257-276, 2002. https://doi.org/10.1007/978-94-017-3194-2_16.
4. T. A. Roberts, Mathematical modeling of microbial growth. 3th Karlsruhe Nutrition Symposium Eurpean Research Towards Safer and Better Food, pp 33-42, Karlsruhe, Germany, October 18-20, 1998.
5. H. Bozkurt and O. Erkmen, Predictive modelling of Yersinia enterocolitica inactivation in Turkish Feta cheese during storage. Journal of Food Engineering, 47, 81-87, 2001. https://doi.org/10.1016/S0260-8774(00)00102-3.
6. T. K. Soboleva, A. B. Pleasants and G. le Roux, Predictive microbiology and food safety. International Journal of Food Microbiology, 57 (3), 183–192, 2000. https://doi.org/10.1016/S0168-1605(00)00265-8.
7. Y. Yoon, Principal theory and application of predictive microbiology. Food Science and Industry, 43 (1), 70-74, 2010. pISSN: 0257-2397.
8. M. H. Zwietering, I. Jongenburger, F. M. Rombouts and K. Vantriet, Modeling of the bacterial-growth curve. Applied and Environmental Microbiology, 56, 1875-1881, 1990. https://doi.org/10.1128/aem.56.6.1875-1881.1990.

9. M. H. Zwietering, J. C. de Wit and S. Notermans, Application of predictive microbiology to estimate the number of Bacillus cereus in pasteurised milk at the point of consumption. International Journal of Food Microbiology, 30, 55-70, 1996. https://doi.org/10.1016/0168-1605(96)00991-9.
10. L. Huang, A comprehensive data analysis tool for predictive microbiology. International Journal of Food Microbiology, 171, 100-107, 2014. https://doi.org/10.1016/j.ijfoodmicro.2013.11.019.
11. F. Devlieghere, K. Francois, B. De Meulenaer and K. Baert, Modelling Food Safety. In: Safety in the Agri-Food Chain. P. A. Luning, F. Devlieghere and R. Verhe (eds). Wageningen: Wageningen Academic Publishers, pp. 397-439, 2006.
12. R. C. Whiting and R. L. Buchanan, A classification of models for predictive microbiology. Food Microbiology, 10, 175-177, 1993.
13. J. Ha, E. Gwak, M. H. Oh, B. Park, J. Lee, S. Kim, H. Lee, S. Lee, Y. Yoon and K. H. Choi, Kinetic behavior of Salmonella on low NaNO2 sausages during aerobic and vacuum storage. Korean Journal for Food Science of Animal Resources, 36 (2), 262-266, 2016. https://doi.org/10.5851/kosfa.2016.36.2.262.
14. O. Erkmen, Gıda Mikrobiyolojisi. Efil Yayınevi, 2.Baskı, Ankara, 2010.
15. A. M. Gibson, N. Bratchell and T. A. Roberts, The effect of sodium chloride and temperature on the rate and extent of growth of Clostridium botulinum type A in pasteurized pork slurry. Journal of Applied Bacteriology, 62, 479–490, 1987. https://doi.org/10.1111/j.1365-2672.1987.tb02680.x.
16. J. Baranyi, T. A. Roberts and P. McClure, A non-autonomous differential equation to model bacterial growth. Food Microbiology, 10, 43-59, 1993. https://doi.org/10.1006/fmic.1993.1005.
17. R. L. Buchanan, Predictive food microbiology. Trends in Food Science Technology, 4, 6-11, 1993. https://doi.org/10.1016/S0924-2244(05)80004-4.
18. R. C. Whiting, Microbial modelling in foods. Critical Reviews in Food Science and Nutrition, 35 (6), 467–494, 1995. https://doi.org/10.1080/10408399509527711.
19. D. A. Ratkowsky, R. K. Lowry, T. A. McMeekin, A. N. Stokes and R. E. Chandler, Model for bacterial culture growth rate throughout the entire biokinetic temperature range. Journal of Bacteriology, 154 (3), 1222-1226, 1983. https://doi.org/10.1128/jb.154.3.1222-1226.1983.
20. B. Fu, P. S. Taoukis and T. P. Labuza, Predictive microbiology for monitoring spoilage of dairy products with time-temperature integrators. Journal of Food Science, 56 (5), 1209-1215, 1991. https://doi.org/10.1111/j.1365-2621.1991.tb04736.x.
21. İ. Y. Genç ve A. Diler, Matematiksel modelleme ve su ürünlerinde kullanılan raf ömrü tahmin modelleri. Süleyman Demirel Üniversitesi, Yalvaç Akademi Dergisi, 2 (1), 13-18, 2017.
22. P. Dalgaard, O. Mejlholm and H. H. Huss, Application of an iterative approach for development of a microbial model predicting the shelf-life of packed fish. International Journal of Food Microbiology, 38, 169–179, 1997. https://doi.org/10.1016/S0168-1605(97)00101-3.
23. K. Koutsoumanis, Predictive modeling of the shelf life of fish under nonisothermal conditions. Applied And Enviromental Microbiology, 67 (4), 1821–1829, 2001. https://doi.org/10.1128/AEM.67.4.1821–1829.2001.
24. S. Lopez, M. Prieto, J. Dijkstra, M. S. Dhanoa and J. France, Statistical evaluation of mathematical models for microbial growth. International Journal of Food Microbiology, 96, 289– 300, 2004. https://doi.org/10.1016/j.ijfoodmicro.2004.03.026.
25. H. Ölmez ve N. Aran, Sodyum laktatın Bacillus cereus’un büyüme kinetiği üzerindeki etkisi. İtüdergisi/d mühendislik, Cilt:4, Sayı:3, 32-38, 2005.
26. R. Gospavic, J. Kreyenschmidt, S. Bruckner, V. Popov and N. Haque, Mathematical modelling for growth of Pseudomonas spp. in poultry under variable temperature conditions. International Journal of Food Microbiology, 127, 290-297, 2008. https://doi.org/10.1016/j.ijfoodmicro.2008.07.022.
27. Z. Yang, X. Jiao, P. Li, Z. Pan, J. Huang, R. Gu, W. Fang and G. Chao, Predictive model of Vibrio parahaemolyticus growth and survival on salmon meat as a function of temperature. Food Microbiology, 26, 606–614, 2009. https://doi.org/10.1016/j.fm.2009.04.004.
28. A. Singh, N. R. Korasapati, V. K. Juneja, J. Subbiah, G. Froning and H. Thippareddi, Dynamic predictive model for the growth of Salmonella spp. in liquid whole egg. Journal of Food Science, 76 (3), 225-232, 2011. https://doi.org/10.1111/j.1750-3841.2011.02074.x.
29. A. Lobacz, J. Kowalik and A. Tarczynska, Modeling the growth of Listeria monocytogenes in mold-ripened cheeses. Journal of Dairy Science, 96, 3449–3460, 2013. https://doi.org/10.3168/jds.2012-5964.
30. O. Ağyar ve F. Üçkardeş, Probiyotik özellikte üç farklı Laktik Asit Bakterileri grubu suşunun koloni büyüme eğrilerinin modifiye edilmiş Gompertz modeli ile modellenmesi. Türk Tarım ve Doğa Bilimleri Dergisi, 1 (3), 430-434, 2014.
31. M. Kološta, A. Slottová, M. Drončovský, L. Klapáčová, V. Kmeť, D. Bujňáková, A. Lauková, G. Greif, M. Greifová and M. Tomáška, Characterisation of lactobacilli from ewe’s and goat’s a milk for their further processing re-utilisation. Potravinarstvo Scientific Journal for Food Industry, 8 (1), 130-134, 2014. https://doi.org/10.5219/354.
32. E. Bednarko-Młynarczyk, J. Szteyn, I. Białobrzewski, A. Wiszniewska-Łaszczych and K. Liedtke, Modeling the kinetics of survival of Staphylococcus aureus in regional yogurt from goat’s milk. Polish Journal of Veterinary Sciences, 18 (1), 39-45, 2015. https://doi.org/10.1515/pjvs-2015-0005.
33. J. Kowalik and A. Lobacz, Development of a predictive model describing the growth of Yersinia enterocolitica in Camembert-type cheese. International Journal of Food Science and Technology, 50, 811–818, 2015. https://doi.org/10.1111/ijfs.12715.
34. K. S. Özdemir, Gıda ve biyoaktif gıda bileşenlerinin kaplanması: Proses ve depolama stabilitesi üzerine etkileri. Doktora Tezi, Hacettepe Üniversitesi, Fen Bilimleri Enstitüsü, Gıda Mühendisliği Anabilim Dalı, Türkiye, 2015.
35. S. Kalkan, Probiyotik laktik asit bakterilerinin Staphylococcus aureus'a karşı antimikrobiyel etkilerinin farklı matematiksel modeller ile analizi. Sinop Üniversitesi Fen Bilimleri Dergisi, 1 (2), 150 – 159, 2016. ISSN: 2536-4383.
36. A. Lytou, E. Z. Panagou and G.-J. E. Nychas, Development of a predictive model for the growth kinetics of aerobic microbial population on pomegranate marinated chicken breast fillets under isothermal and dynamic temperature conditions. Food Microbiology, 55, 25–31, 2016. https://doi.org/10.1016/j.fm.2015.11.009.
37. E. J. Quinto, J. M. Marín and D. W. Schaffner, Effect of the competitive growth of Lactobacillus sakei MN on the growth kinetics of Listeria monocytogenes Scott A in model meat gravy. Food Control, 63, 34-45, 2016. http://doi.org/10.1016/j.foodcont.2015.11.025.
38. J. Szczawiński, M. E. Szczawińska, A. Łobacz and A. Jackowska-Tracz, Modeling the effect of temperature on survival rate of Listeria monocytogenes in yogurt. Polish Journal of Veterinary Sciences, 19 (2), 317-324, 2016. https://doi.org/10.1515/pjvs-2016-0039.
39. S. Vega, D. Saucedo, D. Rodrigo, C. Pina, C. Armero and A. Martĺnez, Modeling the isothermal inactivation curves of Listeria innocua CECT 910 in a vegetable beverage under low-temperature treatments and different pH levels. Food Science and Technology International, 22 (6), 525–535, 2016. https://doi.org/10.1177/1082013215624807.
40. S. Bursova, L. Necidova, D. Harustiakova and B. Janstova, Growth potential of Yersinia enterocolitica in pasteurised cow's and goat's milk stored at 8 °C and 24 °C. Food Control, 73, 1415-1419, 2017. http://doi.org/10.1016/j.foodcont.2016.11.006.
41. L. D. A. Gonçalves, R. H. Piccoli, A. P. Peres, A. V. Saúde, Predictive modeling of Pseudomonas fluorescens growth under different temperature and pH values. Brazilian Journal of Microbiology, 48, 352–358, 2017. https://doi.org/10.1016/j.bjm.2016.12.006.
42. A. Mishra, M. Guo, R. L. Buchanan, D. W. Schaffner and A. K. Pradhan, Development of growth and survival models for Salmonella and Listeria monocytogenes during non-isothermal time-temperature profiles in leafy greens. Food Control, 71, 32-41, 2017. http://doi.org/10.1016/j.foodcont.2016.06.009.
43. D. Savran, Yoğurt üretimi ve depolaması sırasında Salmonella Enteritidis’in canlı kalma durumunun modellenmesi. Doktora Tezi, Ankara Üniversitesi, Fen Bilimleri Enstitüsü, Gıda Mühendisliği Anabilim Dalı, Türkiye, 2017.
44. N. B. Silva, D. A. Longhi, W. F. Martins, J. B. Laurindo, G. M. F. de Aragao and B. A. M. Carciofi, Modeling the growth of Lactobacillus viridescens under non-isothermal conditions in vacuum-packed sliced ham. International Journal of Food Microbiology, 240, 97–101, 2017. http://doi.org/10.1016/j.ijfoodmicro.2016.05.014.
45. A. Bolívar, J. C. C. P. Costa, G. D. Posada-Izquierdo, A. Valero, G. Zurera and F. Pérez-Rodríguez, Modelling the growth of Listeria monocytogenes in Mediterranean fish species from aquaculture production. International Journal of Food Microbiology, 270, 14–21, 2018. https://doi.org/10.1016/j.ijfoodmicro.2018.02.005.
46. H. W. Kim, K. Lee, S. H. Kim and M. S. Rhee, Predictive modeling of bacterial growth in ready-to-use salted napa cabbage (Brassica pekinensis) at different storage temperatures. Food Microbiology, 70, 129-136, 2018. https://doi.org/10.1016/j.fm.2017.09.017.
47. A. Silvestri, E. Ferrari, S. Gozzi, F. Marchi and R. Foschino, Determination of temperature dependent growth parameters in psychrotrophic pathogen bacteria and tentative use of mean kinetic temperature for the microbiological control of food. Frontiers in Microbiology, 9, 3023, 2018. https://doi.org/10.3389/fmicb.2018.03023.
48. J. Ha, J. Lee, S. Lee, S. Kim, Y. Choi, H. Oh, Y. Kim, Y. Lee, Y. Seo and Y. Yoon, Mathematical models to describe the kinetic behavior of Staphylococcus aureus in jerky. Food Science of Animal Resources, 39 (3), 371-378, 2019. https://doi.org/10.5851/kosfa.2019.e28.
49. J. C. C. P. Costa, A. Bolívar, A. Valero, E. Carrasco, G. Zurera and F. Perez-Rodriguez, Evaluation of the effect of Lactobacillus sakei strain L115 on Listeria monocytogenes at different conditions of temperature by using predictive interaction models. Food Research International, 131, 108928, 2020. https://doi.org/10.1016/j.foodres.2019.108928.
50. F. Tarlak, M. Ozdemir and M. Melikoglu, Predictive modelling for the growth kinetics of Pseudomonas spp. on button mushroom (Agaricus bisporus) under isothermal and non-isothermal conditions. Food Research International, 130, 108912, 2020. https://doi.org/10.1016/j.foodres.2019.108912.