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Mathematical Problems in Engineering
Volume 2014, Article ID 392054, 7 pages
http://dx.doi.org/10.1155/2014/392054
Research Article

Comparison of Two Mechanistic Microbial Growth Models to Estimate Shelf Life of Perishable Food Package under Dynamic Temperature Conditions

Department of Food Science and Biotechnology, Kyungnam University, 7 Kyungnamdaehak-ro, Masanhappo-gu, Changwon 631-701, Republic of Korea

Received 14 July 2014; Accepted 17 September 2014; Published 1 October 2014

Academic Editor: Kit Keith L. Yam

Copyright © 2014 Dong Sun Lee. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Linked References

  1. T. A. McMeekin and T. Ross, “Shelf life prediction: status and future possibilities,” International Journal of Food Microbiology, vol. 33, no. 1, pp. 65–83, 1996. View at Publisher · View at Google Scholar · View at Scopus
  2. K. Koutsoumanis and G.-J. E. Nychas, “Application of a systematic experimental procedure to develop a microbial model for rapid fish shelf life predictions,” International Journal of Food Microbiology, vol. 60, no. 2-3, pp. 171–184, 2000. View at Publisher · View at Google Scholar · View at Scopus
  3. D. S. Lee, “Packaging and the microbial shelf life of food,” in Food Packaging and Shelf Life, G. L. Robertson, Ed., pp. 55–79, CRC Press, Boca Raton, Fla, USA, 2009. View at Google Scholar
  4. T. Tsironi, E. Gogou, E. Velliou, and P. S. Taoukis, “Application and validation of the TTI based chill chain management system SMAS (Safety Monitoring and Assurance System) on shelf life optimization of vacuum packed chilled tuna,” International Journal of Food Microbiology, vol. 128, no. 1, pp. 108–115, 2008. View at Publisher · View at Google Scholar · View at Scopus
  5. S. Bruckner, B. Petersen, and J. Kreyenschmidt, “Influence of cold chain interruptions on the shelf life of fresh pork and poultry,” International Journal of Food Science and Technology, vol. 47, no. 8, pp. 1639–1646, 2012. View at Publisher · View at Google Scholar · View at Scopus
  6. P. S. Taoukis, K. Koutsoumanis, and G. J. E. Nychas, “Use of time-temperature integrators and predictive modelling for shelf life control of chilled fish under dynamic storage conditions,” International Journal of Food Microbiology, vol. 53, no. 1, pp. 21–31, 1999. View at Publisher · View at Google Scholar · View at Scopus
  7. D. S. Lee, K.-J. Hwang, D. S. An, J. P. Park, and H. J. Lee, “Model on the microbial quality change of seasoned soybean sprouts for on-line shelf life prediction,” International Journal of Food Microbiology, vol. 118, no. 3, pp. 285–293, 2007. View at Publisher · View at Google Scholar · View at Scopus
  8. I. Seo, D. S. An, and D. S. Lee, “Development of convenient software for online shelf-life decisions for Korean prepared side dishes based on microbial spoilage,” Food Science and Biotechnology, vol. 18, no. 5, pp. 1243–1252, 2009. View at Google Scholar · View at Scopus
  9. J. K. Heising, M. Dekker, P. V. Bartels, and M. A. J. S. (Tiny) Van Boekel, “Monitoring the quality of perishable foods: opportunities for intelligent packaging,” Critical Reviews in Food Science and Nutrition, vol. 54, no. 5, pp. 645–654, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. L. Qi, M. Xu, Z. Fu, T. Mira, and X. Zhang, “C2SLDS: a WSN-based perishable food shelf-life prediction and LSFO strategy decision support system in cold chain logistics,” Food Control, vol. 38, no. 1, pp. 19–29, 2014. View at Publisher · View at Google Scholar · View at Scopus
  11. M. Kärkkäinen, “Increasing efficiency in the supply chain for short shelf life goods using RFID tagging,” International Journal of Retail & Distribution Management, vol. 31, no. 10, pp. 529–536, 2003. View at Google Scholar
  12. K. L. Yam, P. T. Takhistov, and J. Miltz, “Intelligent packaging: concepts and applications,” Journal of Food Science, vol. 70, no. 1, pp. R1–R10, 2005. View at Google Scholar · View at Scopus
  13. J. Baranyi, T. P. Robinson, A. Kaloti, and B. M. Mackey, “Predicting growth of Brochothrix thermosphacta at changing temperature,” International Journal of Food Microbiology, vol. 27, no. 1, pp. 61–75, 1995. View at Publisher · View at Google Scholar · View at Scopus
  14. L. Huang, “Estimation of growth of Clostridium perfringens in cooked beef under fluctuating temperature conditions,” Food Microbiology, vol. 20, no. 5, pp. 549–559, 2003. View at Publisher · View at Google Scholar · View at Scopus
  15. K. Koutsoumanis, P. S. Taoukis, and G. J. E. Nychas, “Development of a safety monitoring and assurance system for chilled food products,” International Journal of Food Microbiology, vol. 100, no. 1–3, pp. 253–260, 2005. View at Publisher · View at Google Scholar · View at Scopus
  16. M. Mataragas, E. H. Drosinos, A. Vaidanis, and I. Metaxopoulos, “Development of a predictive model for spoilage of cooked cured meat products and its validation under constant and dynamic temperature storage conditions,” Journal of Food Science, vol. 71, no. 6, pp. M157–M167, 2006. View at Publisher · View at Google Scholar · View at Scopus
  17. J. F. van Impe, F. Poschet, A. H. Geeraerd, and K. M. Vereecken, “Towards a novel class of predictive microbial growth models,” International Journal of Food Microbiology, vol. 100, no. 1–3, pp. 97–105, 2005. View at Publisher · View at Google Scholar · View at Scopus
  18. J. Baranyi, “Mathematics of predictive food microbiology,” International Journal of Food Microbiology, vol. 26, no. 2, pp. 199–218, 1995. View at Publisher · View at Google Scholar · View at Scopus
  19. L. Huang, “A new mechanistic growth model for simultaneous determination of lag phase duration and exponential growth rate and a new Bělehdrádek-type model for evaluating the effect of temperature on growth rate,” Food Microbiology, vol. 28, no. 4, pp. 770–776, 2011. View at Publisher · View at Google Scholar · View at Scopus
  20. K. Bernaerts, E. Dens, K. Vereecken et al., “Modeling microbial dynamics under time-varying conditions,” in Modeling Microbial Responses in Food, R. C. McKellar and X. Lu, Eds., pp. 243–261, CRC Press, Boca Raton, Fla, USA, 2004. View at Google Scholar
  21. K. Koutsoumanis, “Predictive modeling of the shelf life of fish under nonisothermal conditions,” Applied and Environmental Microbiology, vol. 67, no. 4, pp. 1821–1829, 2001. View at Publisher · View at Google Scholar · View at Scopus
  22. S. Koseki and S. Isobe, “Prediction of pathogen growth on iceberg lettuce under real temperature history during distribution from farm to table,” International Journal of Food Microbiology, vol. 104, no. 3, pp. 239–248, 2005. View at Publisher · View at Google Scholar · View at Scopus
  23. D. A. Longhi, F. Dalcanton, G. M. de Aragão, B. A. Carciofi, and J. B. Laurindo, “Assessing the prediction ability of different mathematical models for the growth of Lactobacillus plantarum under non-isothermal conditions,” Journal of Theoretical Biology, vol. 335, no. 1, pp. 88–96, 2013. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  24. I. A. M. Swinnen, K. Bernaerts, E. J. J. Dens, A. H. Geeraerd, and J. F. van Impe, “Predictive modelling of the microbial lag phase: a review,” International Journal of Food Microbiology, vol. 94, no. 2, pp. 137–159, 2004. View at Publisher · View at Google Scholar · View at Scopus