Journal of Food Hygiene and Safety (한국식품위생안전성학회지)

The Korean Society of Food Hygiene and Safety (한국식품위생안전성학회)
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Growth and Predictive Model of Wild-type Salmonella spp. on Temperature and Time during Cut and Package Processing in Cold Pork Meats

냉장돈육 가공공정 온도와 시간에서의 Wild-type Salmonella spp.의 성장특성 및 예측모델

  • Song, Ju Yeon (Department of Food and Nutrition, Kunsan National University) ;
  • Kim, Yong Soo (Quality Improvement Team, Korea Health Industry Development Institute) ;
  • Hong, Chong Hae (Department of Veterinary Medicine and Institute of Veterinary Science, School of Veterinary Medicine, Kangwon National University) ;
  • Bahk, Gyung Jin (Department of Food and Nutrition, Kunsan National University)
  • 송주연 (군산대학교 식품영양학과) ;
  • 김용수 (한국보건산업진흥원 품질향상팀) ;
  • 홍종해 (강원대학교 수의학과) ;
  • 박경진 (군산대학교 식품영양학과)
  • Received : 2012.08.23
  • Accepted : 2013.01.20
  • Published : 2013.03.31
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Abstract

This study presents the influence on growth properties determined using a novel predictive growth model of wild-type Salmonella spp. KSC 101 by variations in the temperature and time during cut packaging in cold, uncooked pork meat. The experiment performed for model development included an arrangement of different temperatures ($0^{\circ}C$, $5^{\circ}C$, $10^{\circ}C$, $15^{\circ}C$, and $20^{\circ}C$) and time durations (0, 1, 2, and 3 hours) that reflect actual pork-cut and packaging processes. No growth was observed at $0^{\circ}C$ and $5^{\circ}C$, whereas some growth was observed at $10^{\circ}C$, $15^{\circ}C$, and $20^{\circ}C$, with a mean increase of only 0.34 log CFU/g. The growth observed at $20^{\circ}C$ was more robust than that observed at $15^{\circ}C$, but the difference was not statistically significant (p > 0.05). However, compared with PMP (Pathogen Modeling Program), the wild-type Salmonella spp. KSC 101 showed a more rapid growth. We used the Gompertz 4 parameter equation as the primary model, and the exponential decay formula as the secondary model. The estimated $R^2$ values were 0.99 or higher. The developed model was evaluated by comparison of the experimental and predictive values, and the values were in agreement with the ${\pm}0.5$ log CFU/g, although the RMSE (Root mean square error) value was 0.103, which indicates a slight overestimation. Therefore, we suggest that the developed predictive growth model would be useful as a tool for evaluating sanitation criteria in pork cut-packaging processes.

본 연구에서는 멸균처리공정이 없는 돈육 포장공정을 대상으로 작업장에서 직접 분리한 야생균주인 Salmonella spp. KSC101를 작업장의 온도와 시간을 주요 변수로 하여, 이들 현장에서의 Salmonella spp. KSC101의 성장 특성을 파악하고, 이를 수학적으로 예측할 수 있는 모델을 개발하였다. 돈육포장공장 현장을 반영하여 온도는 0, 5, 10, 15, $20^{\circ}C$로, 시간은 0, 1, 2, 3시간으로 하였으며, $0^{\circ}C$와 $5^{\circ}C$에서는 성장이 발생하지 않았으며, $10^{\circ}C$, $15^{\circ}C$, $20^{\circ}C$에 는 약간의 성장이 있었으나 증가수준은 평균 0.34 log CFU/g정도였고, $20^{\circ}C$에서 성장율이 더 높았으나 $15^{\circ}C$와는 통계적으로는 유의하지 않았다(p < 0.05). 하지만 PMP와 비교시 야생균주인 Salmonella spp. KSC101의 성장이 더 빠른 것으로 나타났다. 이들 실험결과를 바탕으로 1차 모델은 Gompertz 4 parameter식을, 2차 모델은 Exponential decay식을 이용하여 성장예측모델을 개발하였으며, $R^2$값은 0.99이상으로 나타났다. 개발된 모델에 대한 검증으로 RMSE를 이용하였으며, 값이 0.103으로 양(+)의 방향으로 약간 초과 예측하는 것으로 나타났으나, 최종적으로 실험값과 예측값이 ${\pm}0.5$ log cfu/g 내에서 일치하고 있어, 본 연구에서 개발된 모델은 추후 냉장돈육 포장공정에서 위생관리기준 설정에 대한 과학적 근거자료로 활용할 수 있을 것이다.

Keywords

Acknowledgement

Supported by : 농림수산식품부

References

  1. Benschop, J., Stevenson, M. A., and Dahl, J.: Towards incorporating spatial risk analysis for Salmonella sero-positivity into the Danish swine surveillance programme. Prev. Vet. Med., 83, 347-359 (2008). https://doi.org/10.1016/j.prevetmed.2007.09.005
  2. Little, C.L., Richardson, J.F., and Owen, R.J.: Campylobacter and Salmonella in raw red meats in the United Kingdom: prevalence, characterization and antimicrobial resistance pattern, 2003-2005. Food Microbiol., 25, 538-543 (2008). https://doi.org/10.1016/j.fm.2008.01.001
  3. Korea Food & Drug Administration (KFDA): Preventive foodborne disease in Korea. Available from: http://fm.kfda.go.kr. (2012).
  4. Animal, Plant and Fisheries Quarantine and Inspection Agency (QIA): Sanitary control for livestock products - HACCP in Korea. Available from: http://www.qia.go.kr/livestock/clean/livestock_ idea_haccp.jsp. (2012).
  5. Van Impe, J. F., Poschet, F. Geeraerd, A. H., Vereecken, K. M.: Towards a novel class of predictive microbial growth models. Int. J. Food Microbiol., 100, 97-105 (2005). https://doi.org/10.1016/j.ijfoodmicro.2004.10.007
  6. McMeekin, T., Oiley, J., Ratkowsky, D., Corkrey, R., Ross, T.: Predictive microbiology theory and application: is it all about rates?. Food Control., (In press) (2012).
  7. Bahk, G.J.: The possibility of predictive modeling for using microbial risk assessment from the literature experimental data. Food Sci. Biotechnol., 18, 137-142 (2009).
  8. USDA Food Safety & Inspection Service (FSIS): Predictive Microbiology Information Portal. Available from: http://portal. arserrc.gov/PMIPHome.aspx. (2012).
  9. Baranyi, J. and Tamplin, M. L.: ComBase: a common database on microbial responses to food environments. J. Food Prot. 67, 1967-1971 (2004). https://doi.org/10.4315/0362-028X-67.9.1967
  10. ComBase. Combined database for predictive microbiology. Available from: http://www.combase.cc/. (2012).
  11. McMeekin, T. A, Olley, J., Ratkowsky, D. A., Ross, T.: Predictive microbiology: towards the interface and beyond. Int. J. Food Microbiol., 73, 395-407 (2002). https://doi.org/10.1016/S0168-1605(01)00663-8
  12. Nauta, M. J.: Modelling bacterial growth in quantitative microbiological risk assessment is it possible? Int. J. Food Microbiol., 73, 297-301 (2002). https://doi.org/10.1016/S0168-1605(01)00664-X
  13. Ding, T., Wang, J., Forghani, F., Ha, S.D., Chung, M.S., Bahk, G.J., Hwang, I.G., Abdallah, E., Oh, D.H.: Development of predictive models for the growth of Escherichia coli O157:H7 on cabbage in Korea. J. Food Sci., 77, 257-263 (2012).
  14. Park, S.Y., Choi, J.W., Chung, D.H., Kim, M.G, Lee, K.H., Kim, K.S., Bahk, G.J., Bae, D.H., Park, S.K., Kim, K.Y., Kim, C.H., Ha, S.D.: Predictive modeling for the growth of Listeria monocytogenes in Sesame leaf. Food Sci. Biotechnol., 16, 238-242 (2007).
  15. Jin, S.S., Jin, Y.G., Yoon, K.S., Woo, G.J., Hwang, I.G., Bahk, G.J., Oh, D.H.: Predictive modeling of the growth and survival of Listeria monocytogenes using a response surface model. Food Sci. Biotechnol., 15, 715-720 (2006).
  16. Hong, C.H., Sim, W.S., Chun, S.J., Kim, Y.S., Oh, D.H., Ha, S.D., Choi, W.S., Bahk, G.J.: Predictive growth model of native isolated Listeria monocytogens on raw pork meats as a function of temperature and time. Kor. J. Food Sci. Technol., 37, 850-855 (2005).
  17. Jimenez, S.M., Caliusco, M.F., Tiburzi, M. C.: Predictive models for reduction of Salmonella Hadar on chicken skin during single and double sequential spraying treatments with acetic acid. J. Appl. Microbial., 103, 528-535 (2007). https://doi.org/10.1111/j.1365-2672.2006.03272.x
  18. Oh, D.H., Rahman, S.M.E., Kim J.M., Bahk, G.J.: The statistics probability analysis of pork-cutting processing conditions for microbial risk assessment. J. Fd Hyg. Safety., 24, 63-68 (2009).
  19. McKellar, R.C., Lu X.: Modeling microbial responses in food. CRC press, FL, USA. pp. 23-24 (2004).
  20. Sarah, S.M., Schaffner, D.W.: Development of model to predictive growth of Clostridium perfringens in cooked in cooked beef during cooling. J. Food Prot., 68, 336-341 (2005). https://doi.org/10.4315/0362-028X-68.2.336
  21. Puerta-Gomez, A.F., Moreira, R.G., Kim, J., Castell-Perez, E.: Modeling the growth rates of Escherichia coli and Salmonella Typhimurium LT2 in baby spinach leaves under slow cooling. Food Control., 29, 11-17 (2013). https://doi.org/10.1016/j.foodcont.2012.05.070
  22. Mann, J.E., Smith, L., Brashears, M.M.: Validation of time and temperature values as critical limits for Salmonella and background flora growth during the production of fresh ground and boneless pork products. J. Food Prot., 67, 1389-1393 (2004). https://doi.org/10.4315/0362-028X-67.7.1389