Journal of Food Hygiene and Safety (한국식품위생안전성학회지)

The Korean Society of Food Hygiene and Safety (한국식품위생안전성학회)
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Development of a Predictive Model Describing the Growth of Listeria Monocytogenes in Fresh Cut Vegetable

샐러드용 신선 채소에서의 Listerio monocytogenes 성장예측모델 개발

  • Cho, Joon-Il (Food Microbiology Division, Food Safety Evaluation Department, National Institute of Food and Drug Safety Evaluation, Korea Food and Drug Administration) ;
  • Lee, Soon-Ho (Food Microbiology Division, Food Safety Evaluation Department, National Institute of Food and Drug Safety Evaluation, Korea Food and Drug Administration) ;
  • Lim, Ji-Su (Food Microbiology Division, Food Safety Evaluation Department, National Institute of Food and Drug Safety Evaluation, Korea Food and Drug Administration) ;
  • Kwak, Hyo-Sun (Food Microbiology Division, Food Safety Evaluation Department, National Institute of Food and Drug Safety Evaluation, Korea Food and Drug Administration) ;
  • Hwang, In-Gyun (Food Microbiology Division, Food Safety Evaluation Department, National Institute of Food and Drug Safety Evaluation, Korea Food and Drug Administration)
  • 조준일 (식품의약품안전청 식품의약품안전평가원 식품위해평가부 미생물과) ;
  • 이순호 (식품의약품안전청 식품의약품안전평가원 식품위해평가부 미생물과) ;
  • 임지수 (식품의약품안전청 식품의약품안전평가원 식품위해평가부 미생물과) ;
  • 곽효선 (식품의약품안전청 식품의약품안전평가원 식품위해평가부 미생물과) ;
  • 황인균 (식품의약품안전청 식품의약품안전평가원 식품위해평가부 미생물과)
  • Received : 2010.09.07
  • Accepted : 2011.01.10
  • Published : 2011.03.31
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Abstract

In this study, predictive mathematical models were developed to predict the kinetics of Listeria monocytogenes growth in the mixed fresh-cut vegetables, which is the most popular ready-to-eat food in the world, as a function of temperature (4, 10, 20 and $30^{\circ}C$). At the specified storage temperatures, the primary growth curve fit well ($r^2$=0.916~0.981) with a Gompertz and Baranyi equation to determine the specific growth rate (SGR). The Polynomial model for natural logarithm transformation of the SGR as a function of temperature was obtained by nonlinear regression (Prism, version 4.0, GraphPad Software). As the storage temperature decreased from $30^{\circ}C$ to $4^{\circ}C$, the SGR decreased, respectively. Polynomial model was identified as appropriate secondary model for SGR on the basis of most statistical indices such as mean square error (MSE=0.002718 by Gompertz, 0.055186 by Baranyi), bias factor (Bf=1.050084 by Gompertz, 1.931472 by Baranyi) and accuracy factor (Af=1.160767 by Gompertz, 2.137181 by Baranyi). Results indicate L. monocytogenes growth was affected by temperature mainly, and equation was developed by Gompertz model (-0.1606+$0.0574^*Temp$+$0.0009^*Temp^*Temp$) was more effective than equation was developed by Baranyi model (0.3502-$0.0496^*Temp$+$0.0022^*Temp^*Temp$) for specific growth rate prediction of L.monocytogenes in the mixed fresh-cut vegetables.

본 연구에서는 식중독 예방과 식품의 안전성 확보 및 정량적 미생물 위해평가에 활용하기위하여, Gompertz model과 Baranyi model을 이용하여 샐러드용 신선채소에서 L. monocytogenes의 SGR에 관한 성징예측모델(SGR by Gompertz equation=-0.1606+$0.0574^*Temp$+$0.0009^*Temp^*Temp$, SGR by Baranyi equation=0.3502-$0.0496^*Temp$+$0.0022^*Temp^*Temp$)을 개발하였다. 개발된 모델의 적합성 평가를 위해 MSE, Bf, 및 Af factor를 산출하였다. 샐러드용 신선 채소의 MSE, Bf, Af는 Gompertz model식을 적용한 경우 0.002718, 1.050084, 1.160767, Baranyi model 식을 적용한 경우 0.055186, 1.931472, 2.137181으로 나타나 Gompertz model식을 적용하여 개발한 예측모델이 Baranyi model 식을 이용하여 개발한 예측모델에 비해 적합성이 높은 것으로 나타났다. Gompertz model식을 활용하여 본 연구에서 개발된 샐러드용 신선 채소에서의 L. monocytogenes 성장 예측모델은 신선 채소류를 생산, 가공, 보관 및 판매하는 산업체에서 널리 활용 가능할 것으로 판단되며, 더욱 정확한 예측모델 개발을 위해서는 pH 및 수분활성도 등 다양한 변수에 따른 미생물의 성장패턴 변화 등에 관한 연구가 추가적으로 시행되어야 할 것으로 생각되어 진다.

Keywords

References

  1. 식품의약품안전청(KFDA): 식중독예방 대국민 홍보사이트 Available from: http://fm.kfda.go.kr/. (2009).
  2. Choi, J.W., Park, S.Y., Yeon, J.H., Lee, M.J., Chung, D.H., Lee, K.H., Kim, M.G., Lee, D.H., Kim, K.S. and Ha, S.D.: Microbial contamination levels of fresh vegetables distributed in markets. Korean J. Food hygiene and Safety, 20, 43-47 (2005).
  3. Itoh, Y., Sugita-Konishi, Y., Kasuage, F., Iwaki, M., Hara-kudo, Y., Saito, N., Noguchi, Y., Konuma, H. and Kumagai, S.: Enterohemorrhagic Escherichia coli O157:H7 Present in Radish Sprouts. Appl. Environ. Microbial., 64, 1352-1535 (1998).
  4. Beuchat, L.R., Harris, L.R., Linda, J., Ward, T.E. and K 먼, T.M.: Development of a propose standard method for assessing the efficacy of fresh produce sanitizers. J. Food prot, 64, 1103-1109 (2001). https://doi.org/10.4315/0362-028X-64.8.1103
  5. Bahk, G.J., Kim, S.J., Sim, W.C., Chun, S.J., Choi, E.Y., Choi, W.S. and Hong CH: Estimation of contamination level of Listeria monocytogenes in meat and meat products using probability approaches. Korean J. Food hygiene and Safety, 18, 107-112 (2003).
  6. Lake, R., Hudson, A., Cressey, P. and Nortje, G.: Risk profile: Listeria monocytogenes in processed ready-to-eat meats. Institute of Environmental Science & Research. http://www.nzfsa. govt.nz/.
  7. Afshin, A.B., Misaghi, A. and Khaschabi, D.: Growth response and modelling of the effects of Zataria multiflora Boiss. essential oil, pH and temperature on Salmonella Typhimurium and Staphylococcus aureus. Lebensmittel Wissenschaft Technologie, 40, 973-981 (2006).
  8. Amit, P., Theodore, P.L. and Francisco, D.: Comparison of primary predictive models to study the growth of Listeria monocytogenes at low temperatures in liquid cultures and selection of fastest growing ribotypes in meat and turkey product slurries. J. Food Microbiology., 25, 460-470 (2008). https://doi.org/10.1016/j.fm.2008.01.009
  9. Baranyi, J. and Roverts, T.A.: A dynamic approach to predicting bacterial growth in food. J. Food Microbiology., 23, 277-294 (1994). https://doi.org/10.1016/0168-1605(94)90157-0
  10. Chen H: Use of linear, Weibull,and log-logistic functions to model pressure inactivation of seven foodborne pathogens in milk. J. Food Microbiology., 24, 197-204 (2007). https://doi.org/10.1016/j.fm.2006.06.004
  11. Delignette-Muller, M.L., Cornu, M., Pouillot, R. and Denis, J.B.: Use of Bayesian modelling in risk assessment: Application to growth of Listeria monocytogenes and food flora in coldsmoked salmon. J. Food Microbiology., 106, 195-208 (2006). https://doi.org/10.1016/j.ijfoodmicro.2005.06.021
  12. Grijspeerdt, K., Kreft, J.U. and Messens, W.: Individual-based modelling of growth and migration of Salmonella enteritidis in hens' eggs. J. Food Microbiology., 100, 323-333 (2005). https://doi.org/10.1016/j.ijfoodmicro.2004.10.028
  13. Hajmeer, M., Basheer, I., Hew, C. and Clive DO: Modeling the survival of Salmonella spp. in chorizos. J. Food Microbiology., 107, 59-67 (2006). https://doi.org/10.1016/j.ijfoodmicro.2005.08.012
  14. Heo, C., Choi, Y.S., Kim, C.J. and Paik HD: Estimation of shelflife of frankfurter using predictive models of spoilage bacterial growth. Korean J. food sci. technol., 28, 289-295 (2009).
  15. Heo, C., Kim, H.W., Choi, Y.S., Kim, C.J. and Paik, H.D.: Application of predictive microbiology for shelf-life estimation of Tteokgalbi containing dietary fiber from rice bran. Korean J. food sci. technol., 28, 232-239 (2008). https://doi.org/10.5851/kosfa.2008.28.2.232
  16. Oh, D.H., Ha, S.D., Park, K.H., Joung, M.S., Chun, S.J., Park, J.S., Woo, G.J. and Hong CH: Quantitative MicrobialRisk Assessment Model for Staphylococcus aureus in Kimbab. Korean J. food sci. technol., 37, 484-491 (2005).
  17. FAO/WHO: Principles and guidelines for incorporating microbiological risk assessment in the development of food safety standards, guidelines and related textes. 2002.
  18. Gimenez, B. and Dalgaard, P.: Modelling and predicting the simultaneous growth of Listeria monocytogenes and spoilage micro-organisms in cold-smoked salmon. J. applied microbiology 96, 96-109 (2004). https://doi.org/10.1046/j.1365-2672.2003.02137.x
  19. Isabel, W. and Virginia, N.S.: Use of predictive microbiology in microbial food safety risk assessment. Int. J. Food Microbiol., 36, 97-102 (1997). https://doi.org/10.1016/S0168-1605(97)01260-9
  20. Jagannath, A., Nakamura, I. and Tsuchido, T.: Modelling the combined effects of pH, temperature and sodium chloride stresses on the thermal inactivation of Bacillus subtilis spores in a buffer systems. J. applied microbiology., 95, 135-141.
  21. Juneja, V.K., Eblen, B.S. and Marks, H.M.: Modeling non-linear survival curves to calculate thermal inactivation of Salmonella in poultry of different fat levels. J. Food Microbiology., 70, 37-51 (2001). https://doi.org/10.1016/S0168-1605(01)00518-9
  22. Kim, S.R., Rhee, M.S., Kim, B.C., Lee, H. and Kim, K.H.: Modeling of the inactivation of Salmonella typhimuriumby supercritical carbon dioxide in physiological saline and phosphatebuffered saline. J Microbiol Methods., 70, 132-141 (2007). https://doi.org/10.1016/j.mimet.2007.04.003
  23. Koseki, S. and Isobe, S.: Prediction of pathogen growth on iceberg lettuce under real temperature history during distribution from farm to table. J. Food Microbiology., 104, 239-248 (2005). https://doi.org/10.1016/j.ijfoodmicro.2005.02.012
  24. Koseki, S., Mizuno, Y. and Yamamoto, K.: Predictive modelling of the recovery of Listeria monocytogenes on sliced cooked ham after high pressure processing. J. Food Microbiology., 119, 300- 307 (2007). https://doi.org/10.1016/j.ijfoodmicro.2007.08.025
  25. Lopez, S., Prieto, M., Dijkstra, J., Dhanoa, M.S. and France, J.: Statistical evaluation of mathematical models for microbial growth. J. Food Microbiology., 96, 289-300 (2004). https://doi.org/10.1016/j.ijfoodmicro.2004.03.026
  26. Miles, D.W., Ross, T., Olley, J. and Mcmeekin, T.A.: Development and evaluation of a predictive model for the effect of temperature and water activity on the growth rate of Vibrio parahaemolyticus. J. Food Microbiology., 38, 133-142 (1997). https://doi.org/10.1016/S0168-1605(97)00100-1
  27. Moon, S.Y., Woo, G.J. and Shin, I.S.: Development of predictive growth model of Listeria monoctogenes using mathematical quantitiative asseessment model. Korean J. food sci. technol., 37, 194-198 (2005).
  28. Pal, A., Labuza, T.P. and Diez-Gonzalez, F.: Comparison of primary predictive models to study the growth of Listeria monocytogenes at low temperatures in liquid cultures and selection of fastest growing ribotypes in meat and turkey product slurries. J. Food Microbiology., 25, 460-470 (2008). https://doi.org/10.1016/j.fm.2008.01.009
  29. Zuliani, V., Lebert, I., Augustin, J.C., Garry, P. and Lebert, A.: Modelling the behaviour of Listeria monocytogenes in ground pork as a function of pH, water activity, nature and concentration of organic acid salts. J. Food Microbiology., 103, 536-550 (2006).
  30. Park, S.Y., Choi, J.W., Yeon, H.J., Lee, M.J., Chung, D.W., Kim, M.G., Lee, K.H., Kim,K.S., Lee, D.H. and Bahk, G.J.: Predictive Modeling for the Growth of Listeria monocytogenes as a Function of Temperature, NaCl, and pH. J. Microbiology and Biotechnology., 15, 1323-1330 (2005).
  31. HWANG, C.A. and MARMER, B.S.: Growth of Listeria monocytogenes in egg salad and pasta salad formulated with mayonnaise of various pH and stored at refrigerated and abuse temperatures. J. Food Microbiology., 24, 211-218 (2007). https://doi.org/10.1016/j.fm.2006.06.002