Abstract

This study was sought to establish a predictive model for Listeria monocytogenes growth on the surface of fresh beef. Fresh beef were totally disinfected with ozone water prior to inoculate with L. monocytogenes and box-packaged to store at 5, 10, 15, 20, 25 and 30°C to calculate the CFU to predict the growth trend. The results showed that the growth trend of L. monocytogenes on fresh beef surface could be well fitted with modified Gompertz models (R2>0.98). Temperature effect on maximum specific growth rate and lag phase was demonstrated by the square-root model as a good linear relationship with R2 values of 0.93 and 0.87, respectively. Conclusively, a predictive growth model for L. monocytogenes on surface of fresh beef stored at various temperature from 5 to 30°C was established effectively as follows: LgN_t = lgN₀+lg(N_max/N₀)×Exp{-Exp[2.718×(-0.002137+0.002203t) 2/lg(N_max/N₀)×( (0.004544+0.015048t)^-2 -t)+1]}.

Keywords:

Fresh beef, Listeria monocytogenes, prediction model,

References

1. Armitage, N.H., 1997. Use of predictive microbiology in meat hygiene regulatory activity. Int. J. Food Microbiol., 36: 103-109.
   CrossRef    
2. Baranyi, J. and T.A. Roberts, 1994. A dynamic approach to predicting bacterial growth in food. Int. J. Food Microbiol., 23(3/4): 277-294.
   CrossRef    
3. Buchanan, R.L., R.C. Whiting and W.C. Damert, 1997. When is simple good enough: A comparison of the Gompertz, Baranyi and three-phase linear models for fitting bacterial growth curves. Food Microbiol., 14: 313-326.
   CrossRef    
4. Dalgaard, P., 1995. Modelling of microbial activity and prediction of shelf life for packed fresh fish. Int. J. Food Microbiol., 26(3): 305-317.
   CrossRef    
5. Ding, T., Q.L. Dong, L. Wang, M.H. Ma and Y.G. Jin, 2010. Establishment and validation of predictive model for Listeria monocytogenes in broth. J. Huazhong Agric. Univ., 29(4): 522-526.
   
6. GB 47892, 2010. Food microbiological examination: Aerobic plate count. National Food Safety Standard.
   
7. Li, B., L.T. Li and E. Tatsumi, 2001. Development of predictive microbiology. Food Fermentation Ind., 27(11): 54-57.
   
8. Liu, W., W.D. Liu and H.Q. Ying, 2007. Predictive microbiology and the application in the field of food safety. Chin. Preventive Med., 8(4): 511-512.
   
9. Mcmeekin, T.A., J. Bowman and O. Mcquestin, 2008. The future of predictive microbiology: Strategic research, innovative applications and great expectations. Int. J. Food Microbiol., 128(1): 2-9.
   CrossRef    PMid:18703250    
10. Qiu, J. and Y.M. Fang, 2007. Evaluation of germicidal efficacy of ozone water by two methods. Chin. J. Disinfect., 24(6): 515-517.
    
11. Ratkowsky, D.A., J. Olley, T.A. McKeekin and A. Ball, 1982. Relationship between temperature and growth rate of bacterial cultures. J. Bacteriol., 149: 1-5.
    PMid:7054139 PMCid:PMC216584    
12. Ross, T., 1996. Indices for performance evaluation of predictive models in food microbiology. J. Appl. Bacteriol., 81: 501-508.
    CrossRef    PMid:8939028    
13. Tang, Q.Y. and M.G. Feng, 2002. DPS Data Processing System for Practical Statistics. Science Press, Beijing.
    
14. Xu, T.Y., 1995. The forecast model of food microorganism growth. Food Sci., 16(1): 17-21.
    
15. Zhu, T.M., 2008. Dulplex PCR Identification and Molecular Typing of Listeria monocytogenes in Frozen Products. Huazhong Agricultural University Library, Wuhan.
    

Competing interests

The authors have no competing interests.

Open Access Policy

This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.

Copyright

The authors have no competing interests.