Development of Predictive Growth Model of Imitation Crab Sticks Putrefactive Bacteria Using Mathematical Quantitative Assessment Model

수학적 정량평가모델을 이용한 게맛살 부패균의 성장 예측모델의 개발

  • Moon, Sung-Yang (Ourhome Co., Ltd. Food Research Institute Analysis and Inspection) ;
  • Paek, Jang-Mi (Faculty of Marine Bioscience & Technology, Kangnung National University) ;
  • Shin, Il-Shik (Faculty of Marine Bioscience & Technology, Kangnung National University)
  • 문성양 ((주)아워홈 식품연구원) ;
  • 백장미 (강릉대학교 해양생명공학부) ;
  • 신일식 (강릉대학교 해양생명공학부)
  • Published : 2005.12.31

Abstract

Predictive growth model of putrefactive bacteria of surimi-based imitation crab in the modified surimi-based imitation crab (MIC) broth was investigated. The growth curves of putrefactive bacteria were obtained by measuring cell number in MIC broth under different conditions (Initial cell number, $1.0{\times}10^2,\;1.0{\times}10^3$ and $1.0{\times}10^4$ colony forming unit (CFU)/mL; temperature, $15^{\circ}C,\;20^{\circ}C\;and\;25^{\circ}C$) and applied them to Gompertz model. The microbial growth indicators, maximum specific growth rate constant (k), lag time (LT) and generation time (GT), were calculated from Gompertz model. Maximum specific growth rate (k) of putrefactive bacteria was become fast with rising temperature and fastest at $25^{\circ}C$. LT and GT were become short with rising temperature and shortest at $25^{\circ}C$. There were not significant differences in k, LT and GT by initial cell number (p>0.05). Polynomial model, $k=-0.2160+0.0241T-0.0199A_0$, and square root model, $\sqrt{k}=0.02669$ (T-3.5689), were developed to express the combination effects of temperature and initial cell number, The relative coefficient of experimental k and predicted k of polynomial model was 0.87 from response surface model. The relative coefficient of experimental k and predicted k of square root model was 0.88. From above results, we found that the growth of putrefactive bacteria was mainly affected by temperature and the square root model was more credible than the polynomial model for the prediction of the growth of putrefactive bacteria.

게맛살로부터 분리한 주요 부패세균은 내열성 포자를 형성하는 Bacillus subtilis와 Bacillus licheniformis로 동정되었다. 게맛살의 제조 공정상 가열 처리 과정에서 B. subtilis와 B. Licheniformis 등 내열성 포자를 형성하는 균을 완전히 사멸시키기는 어려우며, 살아남은 포자는 유통과정 중, 적정 온도와 시간이 경과함에 따라, 영향 세포로 발아하여 게맛살의 부패에 영향을 미친다. 이러한 부패세균의 증식에 있어서 초기균수와 온도의 영향을 조사한 결과, 초기균수에 따른 최대증식속도상수(k)와 유도기(LT), 세대시간(GT)은 유의적인 차이가 없었으며, 온도의 영향이 지배적인 것으로 나타났다. 또한 본 실험에서 유도기(LT)와 온도의 관계는 $L(hr)=2.5219e^{-0.2467{\cdot}T}$의 관계가 성립하며, square root model과 polynomial model을 이용, 온도와 초기균수에 대한 최대증식속도상수(k)를 정량화한 정량평가모델을 개발하였으며, 그 식은 다음과 같다. $$Square\;root\;model:\;{\sqrt{k}}=0.0267\;(T-3.5089)$$ $$Polynomial model:\;k=-0.2160+0.0241T-0.01999A_0$$ 온도와 초기균수에 대한 최대증식속도상수(k)의 정량평가모델로부터 특정온도와 초기 균수에서 최대증식속도상수(k)를 계산할 수 있으며, 계산된 최대증식속도상수(k)를 균의 기본 증식 모델인 Gomperz model에 적용하여 균의 성장을 예측할 수 있었다.

Keywords

References

  1. Whiting RC, Buchnan RL. Development of a quantitative model for Salmonella enteritidis in pasteurized liquid egg. Int. J. Food Microbiol. 36: 111-125 (1997) https://doi.org/10.1016/S0168-1605(97)01262-2
  2. Hathaway SC, Roger LC. A regulatory perspective on the potential use of microbial risk assessment in international trade Int. J. Food Microhiol. 36: 127-133 (1997) https://doi.org/10.1016/S0168-1605(97)01263-4
  3. Dalgaard P. Jorgensen LV. predicted and observed growth of Listeria monocytogenes in seafood challenge tests and in naturally contaminated cold-smoked salmon. Int. J. Food Microbiol. 40: 105-115 (1998) https://doi.org/10.1016/S0168-1605(98)00019-1
  4. CAC (Codex Alimentarius Commission). Draft principles and guidelines for conduct of microbiological risk assessment. CAC/GL-30, FAO Rome, Italy (1999)
  5. Faber JM. Predictive modelling of food deterioration and safety. pp 57-90. In: Food-borne microorganisms and their toxin. Pierson MD and Stem NJ (eds). Marcel Dekker, New York, NY. USA (1996)
  6. Miller AJ, Whiting RC, Smith JL. Use of risk assessment to reduce listeriosis incidence. Food Technol. 51: 100-103 (1997)
  7. Marks HM, Colemann ME, Lin CTJ, Roberts T. Topics microbial risk assessment: Dtnamic flow tree process. Risk Anal. 18: 309-328 (1998) https://doi.org/10.1111/j.1539-6924.1998.tb01298.x
  8. Brown MH, Davies KW, Billon CMP, Adair C, McClure PJ. Quantitative micobiological risk assessment: Principles applied to determining the comparative risk of Salmonellosis from chicken products. J. Food Protec. 61: 1446-1453 (1998) https://doi.org/10.4315/0362-028X-61.11.1446
  9. Park SY, Choi JW, Yeon JH, Lee MJ, Oh DH, Hong CH, Bahk GJ, Woo GJ, Park JS, Ha SD. Assessment of contamination level of foodborne pathogens in the main ingredients of Kimbab during the preparing process. Korean J. Food Sci. Technol. 37: 122-128 (2005)
  10. Bahk GJ, Oh DH, Ha SD, Par KH, Joung MS, Chun SJ, Park JS, Woo GJ, Hong CH. Quantitative microbial risk assessment model for Staphylococcus aureus in kimbab. Korean J. Food Sci. Technol. 37: 484-491 (2005)
  11. Moon SY, Chang TE, Woo GJ, Shin IS. Development of predictive growth model Vibrio parahaemolyticus using mathematical quantitative model. Korean J. Food Sci. Tech. 36: 349-354 (2004)
  12. Moon SY, Woo GJ, Shin IS. Development of predictive growth model Listeria monocytogenes using mathematical quantitative model. Korean J. Food Sci. Tech. 37: 194-198 (2005)
  13. AOAC. Official Method of Analysis of AOAC Intl. Method 940.36. Association of Official Analytical Chemists, Arlington, VA, USA (2000)
  14. Whiting RC. Microbial database building: What have we learned? Food Technol. 51: 82-84 (1997)
  15. Ratkowsky DA, Ross T. Modelling the bacterial growth/no growth interface. Lett. Appl. Microbiol. 20: 29-33 (1995) https://doi.org/10.1111/j.1472-765X.1995.tb00400.x
  16. Dunacn DB. Multiple-range and multiple F test. Biometrics, 11: 1-42 (1955) https://doi.org/10.2307/3001478
  17. Zwietering MH, Cuppers HGAH, de Wit, JC, Van 'T Riet, K. Evaluation of data transformations and validation of a model for the effect of temperature on bacterial growth. Appl. Environ. Microbiol. 60: 195-203 (1994)
  18. Buchanan R L, Phillips JG. Response surface model for predicting the effects of temperature, pH, sodium chloride content, sodium nitrite concentration and atmosphere on the growth of Listeria monocytogenes. J. Food Prot. 53: 370-376 (1990) https://doi.org/10.4315/0362-028X-53.5.370
  19. Baranyi J, Roberts TA. A dynamic approach to predicting bacterial growth in food. Int. J. Food Microbiol. 26: 199-218 (1995) https://doi.org/10.1016/0168-1605(94)00121-L
  20. Augustin JC, Vincent C. Modellling the growth rate of Listeria monocytogenes with a multiplicative type model including interactions between environmental factors. Int. J. Food Microbiol. 56: 53-70 (2000) https://doi.org/10.1016/S0168-1605(00)00224-5
  21. Ratkowsky DA, Lowry RK, Mcmeekin TA, Stokes AN, Chandler RE. Model for bacterial culture growth rate through the entire biokinetic temperature range. J. Bacteriol. 154: 1222-1226 (1983)
  22. Zwietering MH, de Koos JT, Hasenack BE, de Wit, JC, van 'T Riet K. Modeling of bacterial growth as a function of temperature. Appl. Environ. Microbiol. 57: 1094-1101 (1991)
  23. Zwietering MH, Cuppers HGAH, de Wit, JC, van 'T Riet, K. Evaluation of data transformations and validation of a model for the effect of temperature on bacterial growth. Appl. Environ. Microbiol. 60: 195-203 (1994)
  24. Giffel MC, Zwietering MH. Validation of predictive models describing the growth of Listeria monocytogenes. Int. J. Food Microbiol. 46: 135-149 (1999) https://doi.org/10.1016/S0168-1605(98)00189-5
  25. Larpin S, Sauvageot N, Pichereau V, Laplace JM, Auffray Y. Biosynthesis of exopolysaccharide by a Bacillus licheniformis strain isolated tram ropy cider, Int. J. Food Microbiol. 77: 1-9 (2002) https://doi.org/10.1016/S0168-1605(02)00058-2
  26. Carr JG, Norris JR, Pettipher GL, Wiley J. Essays in Agricultural and Food Microbiology. London, UK. pp. 291-307 (1897)
  27. Spilimbergo S, Bertucco A, Lauro FM, Bertoloni G. Inactivation of Bacillus subtilis spores by supercritical $CO_2$ treatment, Innovative Food Sci. Emerging Technol. 4: 161-165 (2003) https://doi.org/10.1016/S1466-8564(02)00089-9