한국식품위생안전성학회지 (Journal of Food Hygiene and Safety)

  • 제34권4호
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  • Pages.374-379
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  • 2019
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  • 1229-1153(pISSN)
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  • 2465-9223(eISSN)
한국식품위생안전성학회 (The Korean Society of Food Hygiene and Safety)
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Predictive Modeling for the Growth of Salmonella Enterica Serovar Typhimurium on Lettuce Washed with Combined Chlorine and Ultrasound During Storage

  • Park, Shin Young (Department of Seafood and Aquaculture Science, Institute of Marine Industry, Gyeongsang National University) ;
  • Zhang, Cheng Yi (Advanced Food Safety Research Group, BrainKorea21 Plus, Department of Food Science and Technology, Chung-Ang University) ;
  • Ha, Sang-Do (Advanced Food Safety Research Group, BrainKorea21 Plus, Department of Food Science and Technology, Chung-Ang University)
  • 투고 : 2019.05.23
  • 심사 : 2019.07.26
  • 발행 : 2019.08.30
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초록

본 연구에서는 대표적인 신선 잎채소류인 상추의 세척 단계에서 초음파 (37 kHz) 와 염소 (100~300 ppm) 의 병용처리 후 냉장 ~ 실온저장 ($10{\sim}25^{\circ}C$)에 따른 이 식품 중의 Salmonella Typhimurium의 성장예측모델을 개발하였다. 1 차 모델 개발을 위해 Gompertz 방정식을 활용하여 각기 다른 실험 조건에서의 S. Typhimurium의 생육도 (SGR 과 LT)를 조사했다. 본 방정식에 의한 1 차 모델 개발시 $R^2$가 0.92 이상으로 우수하게 나타났으며 저장온도가 낮을수록 초음파에 사용된 염소의 농도가 높을수록 SGR 값은 감소하였고 LT 값은 증가하였다. 이를 바탕으로 2 차 polynomial 모델을 개발하여 다양한 통계적 지표 ($R^2$, MSE, $A_f$ 및 $B_f$)를 통해 분석한 결과 개발된 모델의 적합성을 확인할 수 있었다. 따라서 개발된 모델이 초음파와 염소의 병용 세척에 따른 저장 중 상추에 대한 S. Typhimurium의 성장예측모델로 사용 가능하다고 판단되어지며, 신선 잎채소류에서의 식중독을 예방하고 미생물학적 위생관리기준을 설정하는데 기초자료로 활용될 수 있을 것으로 사료된다.

This study developed predictive growth models of Salmonella enterica Serovar Typhimurium on lettuce washed with chlorine (100~300 ppm) and ultrasound (US, 37 kHz, 380 W) treatment and stored at different temperatures ($10{\sim}25^{\circ}C$) using a polynomial equation. The primary model of specific growth rate (SGR) and lag time (LT) showed a good fit ($R^2{\geq}0.92$) with a Gompertz equation. A secondary model was obtained using a quadratic polynomial equation. The appropriateness of the secondary SGR and LT model was verified by coefficient of determination ($R^2=0.98{\sim}0.99$ for internal validation, 0.97~0.98 for external validation), mean square error (MSE=-0.0071~0.0057 for internal validation, -0.0118~0.0176 for external validation), bias factor ($B_f=0.9918{\sim}1.0066$ for internal validation, 0.9865~1.0205 for external validation), and accuracy factor ($A_f=0.9935{\sim}1.0082$ for internal validation, 0.9799~1.0137 for external validation). The newly developed models for S. Typhimurium could be incorporated into a tertiary modeling program to predict the growth of S. Typhimurium as a function of combined chlorine and US during the storage. These new models may also be useful to predict potential S. Typhimurium growth on lettuce, which is important for food safety purposes during the overall supply chain of lettuce from farm to table. Finally, the models may offer reliable and useful information of growth kinetics for the quantification microbial risk assessment of S. Typhimurium on washed lettuce.

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참고문헌

  1. Slavin, J.L., Lloyd, B.: Health Benefits of fruits and vegetables. Adv. Nutr., 3, 506-516 (2012). https://doi.org/10.3945/an.112.002154
  2. Sivapalasingam, S., Friedman, C.R., Cohen, L., Tauxe, R.V.: Fresh produce: a growing cause of outbreaks of foodborne illness in the United States, 1973 through 1997. J. Food Prot., 67, 2342-2353 (2004). https://doi.org/10.4315/0362-028X-67.10.2342
  3. Golberg, D., Kroupitski, Y., Belausov, E., Pinto, R., Sela, S.: Salmonella Typhimurium internalization is variable in leafy vegetables and fresh herbs. Int. J. Food Microbiol.145, 250-257 (2011). https://doi.org/10.1016/j.ijfoodmicro.2010.12.031
  4. Kroupitski, Y., Pinto, R., Brandl, M.T., Belausov, E., Sela, S.: Interactions of Salmonella enterica with lettuce leaves. J. Appl. Microbiol. 106, 1876-1885 (2009). https://doi.org/10.1111/j.1365-2672.2009.04152.x
  5. Tsen, H.Y. (2002). Molecular typing of Salmonella enterica serovars Typhimurium, Typhi, and Enteritidis isolated in Taiwan. J. Drug Anal., 10, 242-251 (2002).
  6. Lim, Y.H., Hirose, K., Izumiya, H., Arakawa, E., Takahashi, H., Terajima, J., Itoh, K, Tamura, K., Kim, S., Watanabe, H.: Multiplex polymerase chain reaction assay for selective detection of Salmonella enterica Serovar Typhimurium. Japanes J. Infectious Dis., 56, 151-155 (2003).
  7. Anonymous: National Salmonella Typhimurium Outbreak linked to lettuce retrieved from http://www.foodpoisonjournal.com/foodborne-illnessoutbreaks/national-salmonellatyphimurium-outbreak-linked-to-lettuce/ (2009).
  8. Chang, J.M., Fang, T.J.: Survival of Escherichia coli O157:H7 and Salmonella enteric serovars Typhimurium in iceberg lettuce and the antimicrobial effect of rice vinegar against E. coli O157:H7. Food Microbiol. 24, 745-751 (2007). https://doi.org/10.1016/j.fm.2007.03.005
  9. Piyasena, P., Mohareb, E., McKellar, R.C.: Inactivation of microbes using ultrasound: A review. Int. J. Food Microbiol. 87, 207-216 (2003). https://doi.org/10.1016/S0168-1605(03)00075-8
  10. Ross, T., Dalgaard, P., Tienungoon, S.: Predictive modeling of the growth and survival of Listeria in fishery products. Int. J. Food Microbiol. 62, 231-245 (2000). https://doi.org/10.1016/S0168-1605(00)00340-8
  11. Tomac, A., Mascheroni, R.H., Yeannes, M.I.: Modelling the effect of gamma irradiation on the inactivation and growth kinetics of psychrotrophic bacteria in squid rings during refrigerated storage. J. Food Eng., 117, 211-216 (2013). https://doi.org/10.1016/j.jfoodeng.2013.02.021
  12. Gibson, A.M., Bratchell, N., Roberts, T.A.: Predicting microbial growth: Growth response of Salmonella in a laboratory medium as affected by pH, sodium chloride and storage temperature. Int. J. Food Microbiol. 6, 155-178 (1988). https://doi.org/10.1016/0168-1605(88)90051-7
  13. Huang, L.: Thermal inactivation of Listeria monocytogenes in ground beef under isothermal and dynamic temperature conditions. J. Food Eng., 90, 380-387 (2009). https://doi.org/10.1016/j.jfoodeng.2008.07.011
  14. Huang, L.: A comprehensive data analysis tool for predictive microbiology. Int. J. Food Microbiol., 171, 100-107 (2013). https://doi.org/10.1016/j.ijfoodmicro.2013.11.019
  15. Park, S.Y., Seo, K.Y., Ha, S.D.: A response surface model based on absorbance data for the growth rates of Salmonella enterica Serovar Typhimurium as a function of temperature, NaCl, and pH. J. Microbiol. Biotechnol. 17, 644-649 (2007).
  16. Kim, B.Y., Choi, S.Y., Seo, K.Y., Ha, S.D.: Temperature dependent growth characteristics and a predictive mathematical model of Salmonella enterica serovar Typhimurium in kimbab. J. Korean Sco. Appl. Biol. Chem., 54, 454-459 (2011). https://doi.org/10.3839/jksabc.2011.070
  17. Oh, S.R., Kang, I., Oh, M.H., Ha, S.D.: Inhibitory effect of chlorine and ultraviolet radiation on growth of Listeria monocytogenes in chicken breast and development of predictive models. Poultry Sci., 93, 200-207 (2014). https://doi.org/10.3382/ps.2013-03394
  18. Yoon, K.S., Min, K.J., Jung, Y.J., Kwon, K.Y., Lee, J.K., Oh, S.W.: A model of the effect of temperature on the growth of pathogenic and nonpathogenic Vibrio parahaemolyticus isolated from oysters in Korea. Food Microbiol., 25, 635-641 (2008). https://doi.org/10.1016/j.fm.2008.04.007
  19. Jung, Y.J., Min, K.J., Yoon, K.S.: Responses of acid-stressed Salmonella Typhimurium in broth and chicken patties to subsequent antimicrobial stress with $\varepsilon$-polylysine and combined potassium lactate and sodium diacetate. Food Microbiol., 26, 467-474 (2009). https://doi.org/10.1016/j.fm.2009.02.007
  20. Sutherland, J.P., Bay, A.J., Roberts, T.A.: Predictive modeling of growth of Staphylococcus aureus: The effects of temperature, pH, and sodium chloride. Int. J. Food Microbiol., 21, 217-236 (1994). https://doi.org/10.1016/0168-1605(94)90029-9
  21. Adair, C., Kilsby, D.C., Whittal, P.T.: Comparison of the school field (non-linear Arrhenius) model and the square root model for predicting bacterial growth in foods. Food Microbiol., 6, 7-18 (1989). https://doi.org/10.1016/S0740-0020(89)80033-4
  22. Lebert, I., Robeles-Olvera, V., Lebert, A.: Application of polynomial models to predict growth of mixed cultures of Pseudomonas spp. and Listeria in meat. Int. J. Food Microbiol. 61, 27-39 (2000). https://doi.org/10.1016/S0168-1605(00)00359-7
  23. Ross, T., Dalgarrd, P., Tienungoon, S.: Predictive modeling of the growth and survival of Listera in fishery products. Int. J. Food Microbiol., 62, 231-245 (2000). https://doi.org/10.1016/S0168-1605(00)00340-8
  24. Carrasco, E., Garcia-Gimeno, R., Seselovsky, R., Valero, A., Perez, F., Zurera, G.: Predictive model of Listeria monocytogenes growth rate under different temperatures and acids. Food Sci. Technol. Int., 12, 47-56 (2006). https://doi.org/10.1177/1082013206062234