Browse > Article

Development of Predictive Growth Model of Vibrio parahaemolyticus Using Mathematical Quantitative Model  

Moon, Sung-Yang (Faculty of Marine Bioscience and Technology, Kangnung National University)
Chang, Tae-Eun (Faculty of Marine Bioscience and Technology, Kangnung National University)
Woo, Gun-Jo (Korea Food and Drug Administration)
Shin, Il-Shik (Faculty of Marine Bioscience and Technology, Kangnung National University)
Publication Information
Korean Journal of Food Science and Technology / v.36, no.2, 2004 , pp. 349-354 More about this Journal
Abstract
Predictive growth model of Vibrio parahaemolyticus in modified surimi-based imitation crab broth was investigated. Growth curves of V. parahaemolyticus were obtained by measuring cell concentration in culture broth under different conditions ($Initial\;cell\;level,\;1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}\;colony\;forming\;unit\;(CFU)/mL$; temperature, 15, 25 37, and $40^{\circ}C$; pH 6, 7, and 8) and applying them to Gompertz model. Microbial growth indicators, maximum specific growth rate (k), lag time (LT), and generation time (GT), were calculated from Gompertz model. Maximum specific growth rate (k) of V. parahaemolyticus increased with increasing temperature, reaching maximum rate at $37^{\circ}C$. LT and GT were also the shortest at $37^{\circ}C$. pH and initial cell number did not influence k, LT, and GT values significantly (p>0.05). Polynomial model, $k=a{\cdot}\exp(-0.5{\cdot}((T-T_{max}/b)^{2}+((pH-pH_{max)/c^{2}))$, and square root model, ${\sqrt{k}\;0.06(T-9.55)[1-\exp(0.07(T-49.98))]$, were developed to express combination effects of temperature and pH under each initial cell number using Gauss-Newton Algorism of Sigma plot 7.0 (SPSS Inc.). Relative coefficients between experimental k and k Predicted by polynomial model were 0.966, 0.979, and 0.965, respectively, at initial cell numbers of $1{\times}10^{2},\;1{\times}10^{3},\;and\;1{\times}10^{4}CFU/mL$, while that between experimental k and k Predicted by square root model was 0.977. Results revealed growth of V. parahaemolyticus was mainly affected by temperature, and square root model showing effect of temperature was more credible than polynomial model for prediction of V. parahaemolyticus growth.
Keywords
predictive growth model; Vibrio parahaemolyticus; Gompertz model; polynomial model; square root model; maximum specific growth rate (k);
Citations & Related Records
연도 인용수 순위
  • Reference
1 Ross T, McMeekin TA. Predictive microbiology. Int. J. Food Microbiol. 23: 241-264 (1994)   DOI   ScienceOn
2 Notermans S, Gallhoff G, Zwitering MH, Mead GC. The HACCP concept: specification of criteria using quantitative risk assesment. Food Microbiol. 12: 81-90 (1995)   DOI   ScienceOn
3 AOAC. Official Methods of Analysis. Method 940.36. Association of Official Analytical Chemists, Arlington, VA, USA (2000)
4 Ratkowsky DA, Ross T. Modelling the bacterial growth/no growth interface. Lett. Appl. Microbiol. 20: 29-33 (1995)   DOI   ScienceOn
5 Coleman ME, Marks HM. Qualitative and quantitative risk assessment. Food Control 10: 289-297 (1999)   DOI   ScienceOn
6 Miles DW. Predicting the growth of Vibrio parahaemolyticus. BS thesis, University of Tasmania, Tasmania, AU (1994)
7 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)
8 Yano N. Predictive Microbiology and its application in food industry. Jpn. J. Food Microbiol. 15: 81-87 (1998)   DOI
9 Miles DW, Thomas R, Olley J, Thomas A, McMeekin TA. Development and evaluation of a predictive model for the effect of temperature and water activity on the growth rate of Vibrio parahaemolyticus, Int. J. Food Microbiol. 38: 133-142 (1997)   DOI   ScienceOn
10 Ross T, McMeekin TA. Predictive microbiology. Int. J. Food Microbiol. 23: 241-264 (1994)   DOI   ScienceOn
11 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. Milcobiol. 57: 1094-1101 (1991)
12 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)
13 Baker DA. Application of modelling in HACCP plan development. Int. J. Food Microbiol. 25: 251-261 (1995)   DOI   PUBMED   ScienceOn
14 Duncan DB. Multiple-range and multiple F test. Biometrics 11:1-42(1955)   DOI   ScienceOn