DOI QR코드

DOI QR Code

Model for Estimating CO2 Concentration in Package Headspace of Microbiologically Perishable Food

  • Lee, Dong-Sun (Department of Food Science & Biotechnology, Kyungnam University) ;
  • Kim, Hwan-Ki (Department of Food Science & Biotechnology, Kyungnam University) ;
  • An, Duck-Soon (Department of Food Science & Biotechnology, Kyungnam University) ;
  • Yam, Kit L. (Department of Food Science, Rutgers University)
  • Received : 2011.07.04
  • Accepted : 2011.11.21
  • Published : 2011.12.31

Abstract

Levels of carbon dioxide gas, a metabolite of microbial growth, have been reported to parallel the onset of microbial spoilage and may be used as a convenient index for a packaged food's shelf life. This study aimed to establish a kinetic model of $CO_2$ production from perishable food for the potential use for shelf life control in the food supply chain. Aerobic bacterial count and package $CO_2$ concentration were measured during the storage of seasoned pork meat at four temperatures (0, 5, 10 and $15^{\circ}C$), and their interrelationship was investigated to establish a mathematical model. The microbial growth at constant temperature was described by using model of Baranyi and Roberts. $CO_2$ production from the stored food could be explained by taking care of its yield and maintenance factors linked to the microbial growth. By establishing the temperature dependence of the microbial growth and $CO_2$ yield factor, $CO_2$ partial pressure or concentration in package headspace could be estimated to a limited extent, which is helpful for controlling the shelf life under constant and dynamic temperature conditions. Application and efficacy of the model needs to be improved with further refinement in the model.

Keywords

References

  1. Huss HH, Dalgaard P, Gram L. 1997. Microbiology of fish and fish products. In Seafood from Producer to Consumer, Integrated Approach to Quality. Luten JB, Borresen T, Oehlenschlager J, eds. Elsevier Science, Amsterdam, Netherlands. p 413-430.
  2. Sutherland J. 2003. Modelling food spoilage. In Food Preservation Techniques. Zeuthen P, Bogh-Sorensen L, eds. Woodhead Publishing, Cambridge, UK. p 451-474.
  3. Guerzoni ME, Gardini F, Duan J. 1990. Interactions between inhibition factors on microbial stability of fruitbased systems. Int J Food Microbiol 10: 1-18. https://doi.org/10.1016/0168-1605(90)90002-M
  4. Kim HK, An DS, Yam KL, Lee DS. 2011. Package headspace composition changes of chill-stored perishable foods in relation to microbial spoilage. Packag Technol Sci 24:343-352. https://doi.org/10.1002/pts.943
  5. Koutsoumanis K, Nychas GJE. 2000. Application of a systematic experimental procedure to develop a microbial model for rapid fish shelf life predictions. Int J Food Microbiol 60: 171-184. https://doi.org/10.1016/S0168-1605(00)00309-3
  6. Koutsoumanis KP, Stamatiou AP, Drosinos EH, Nychas GJE. 2008. Control of spoilage microorganisms in minced pork by a self-developed modified atmosphere induced by the respiratory activity of meat microflora. Food Microbiol 25: 915-921. https://doi.org/10.1016/j.fm.2008.05.006
  7. Vankerschaver K, Willocx F, Smout C, Hendricks M, Tobback P. 1996. The influence of temperature and gas mixtures on the growth of the intrinsic microorganisms on cut endive: predictive versus actual growth. Food Microbiol 13: 427-440. https://doi.org/10.1006/fmic.1996.0049
  8. Baranyi J, Roberts TA. 1994. A dynamic approach to predicting bacterial growth in food. Int J Food Microbiol 23: 277-294. https://doi.org/10.1016/0168-1605(94)90157-0
  9. Couriol C, Amrane A, Prigent Y. 2001. A new model for the reconstruction of biomass history from carbon dioxide emission during batch cultivation of Geotrichum candidum. J Biosci Bioeng 91: 570-575. https://doi.org/10.1016/S1389-1723(01)80175-4
  10. Jakobsen M, Jensen PN, Risbo J. 2009. Assessment of carbon dioxide solubility coefficients for semihard cheeses: the effect of temperature and fat content. Eur Food Res Technol 229: 287-294. https://doi.org/10.1007/s00217-009-1059-3
  11. Sivertsvik M, Rosnes JT, Jeksrud WK. 2004. Solubility and absorption rate of carbon dioxide into non-respiring foods. part 2: raw fish fillets. J Food Eng 63: 451-458. https://doi.org/10.1016/j.jfoodeng.2003.09.004
  12. Jakobsen M, Bertelsen G. 2006. Solubility of carbon dioxide in fat and muscle tissue. J Muscle Foods 17: 9-19. https://doi.org/10.1111/j.1745-4573.2006.00029.x
  13. Chun HK. 2006. Food Composition Table. Rural Resources Development Institute, Suwon, Korea. p 210-211.
  14. Rammert M, Paderson MHP. 1991. Die Loslichkeit von Kohlendioxid in Getraken. Brauwelt 131: 488-499.
  15. Lee DS, Hwang KJ, An DS, Park JP, Lee HJ. 2007. Model on the microbial quality change of seasoned soybean sprouts for on-line shelf life prediction. Int J Food Microbiol 18: 285-293.
  16. Koseki S, Isobe S. 2005. Prediction of pathogen growth on iceberg lettuce under real temperature history during distribution from farm to table. Int J Food Microbiol 104: 239-248. https://doi.org/10.1016/j.ijfoodmicro.2005.02.012
  17. Zwietering MH, de Koos JT, Hasenack BE, de Wit JC, Van't Riet K. 1991. Modeling bacterial growth as a function of temperature. Appl Environ Microbiol 57: 1094-1101.
  18. Lee DS. 2009. Packaging and the microbial shelf life of food. In Food Packaging and Shelf Life. Robertson G, ed. CRC Press, Boca Raton, FL, USA. p 55-79.
  19. McMeekin TA, Olley JN, Ross T, Ratkowsky DA. 1993. Predictive Microbiology. Research Studies Press, Somerset, UK. p 11-86.
  20. Huang L. 2003. Estimation of growth of Clostridium perfringens in cooked beef under fluctuating temperature conditions. Food Microbiol 20: 549-559. https://doi.org/10.1016/S0740-0020(02)00155-7
  21. Nauta MJ, Litman S, Barker GC, Carlin F. 2003. A retail and consumer phase model for exposure assessment of Bacillus cereus. Int J Food Microbiol 83: 205-218. https://doi.org/10.1016/S0168-1605(02)00374-4