Development of Convenient Software for Online Shelf-life Decisions for Korean Prepared Side Dishes Based on Microbial Spoilage

  • Seo, Il (Department of Food Science and Biotechnology, Kyungnam University) ;
  • An, Duck-Soon (Department of Food Science and Biotechnology, Kyungnam University) ;
  • Lee, Dong-Sun (Department of Food Science and Biotechnology, Kyungnam University)
  • Published : 2009.10.31

Abstract

User-friendly software was developed to determine the shelf-life of perishable Korean seasoned side dishes in real time based on growth models of spoilage and pathogenic microorganisms. In the program algorithm, the primary spoilage and fastest-growing pathogenic organisms are selected according to the product characteristics, and their growth is simulated based on the previously monitored or recorded temperature history. To predict the growth of spoilage organisms with confidence limits, kinetic models for aerobic bacteria or molds/yeasts from published works are used. Growth models of pathogenic bacteria were obtained from the literature or derived with regression of their growth rate data estimated from established software packages. These models are also used to check whether the risk of pathogenic bacterial growth exceeds that of food spoilage organisms. Many example simulations showed that the shelf-lives of the examined foods are predominantly limited by the growth of spoilage organism rather than by pathogenic bacterial growth.

References

  1. Kim G-T, Ko Y-D, Lee DS. Shelf life determination of Korean seasoned side dishes. Food Sci. Technol. Int. 9: 257-263 (2003) https://doi.org/10.1177/108201303038059
  2. Seo I, Park JP, Lee DS. Correlation between microbiological and sensory quality indexes of Korean seasoned side dishes stored under chilled conditions. J. Food Sci. Nutr. 11: 257-260 (2006) https://doi.org/10.3746/jfn.2006.11.3.257
  3. Blackburn CDW. Modelling shelf-life. pp. 55-78. In: The Stability and Shelf-life of Food. Kilcast D, Subramaniam P (eds). Woodhead Publishing Limited, Cambridge, UK (2000)
  4. Singh RP. Scientific principles of shelf-life evaluation. pp. 3-22. In: Shelf-life Evaluation of Food. Man D, Jones A (eds). Aspen Publishers, Gaithersburg, MD, USA (2000)
  5. Wijtzes T, Van't Riet K, Huis in't Veld JHJ, Zwietering MH. A decision support system for the prediction of microbial food safety and food quality. Int. J. Food Microbiol. 42: 79-90 (1998) https://doi.org/10.1016/S0168-1605(98)00068-3
  6. Taoukis PS, Labuza TP. Time-temperature indicators (TTIs). pp. 103-126. In: Novel Food Packaging Techniques. Ahvenainen R (ed). Woodhead Publishing Limited, Cambridge, UK (2003)
  7. Anonymous. The RFID in Retail Manual 2005. Pira International, Leatherhead, UK. pp. 1-14 (2004)
  8. McMeekin TA, Olley J, Ratkowsky DA, Ross T. Predictive microbiology: Towards the interface and beyond. Int. J. Food Microbiol. 73: 395-407 (2002) https://doi.org/10.1016/S0168-1605(01)00663-8
  9. Dalgaard P, Buch P, Silberg S. Seafood spoilage predictordevelopment and distribution of a product specific application software. Int. J. Food Microbiol. 73: 343-349 (2002) https://doi.org/10.1016/S0168-1605(01)00670-5
  10. Koutsoumanis K, Taoukis PS, Nychas GJE. Development of a safety monitoring and assurance system for chilled food products. Int. J. Food Microbiol. 100: 253-260 (2005) https://doi.org/10.1016/j.ijfoodmicro.2004.10.024
  11. Alfaro B, Nuin M, Pin C, Marc YL. Development of a software to predict the shelf-life of fresh fish according to sensory and microbial parameters. pp. 68-71. In: Shelf Life International Meeting. University of Catania, Catania, Italy. The Italian Scientific Group of Food Packaging, Milano, Italy (2006)
  12. Kim SJ, An DS, Lee HJ, Lee DS. Microbial quality change model of Korean pan-fried meat patties exposed to fluctuating temperature conditions. J. Food Sci. Nutr. 13: 348-353 (2008) https://doi.org/10.3746/jfn.2008.13.4.348
  13. Lee DS, Hwang K-J, An DS, Park JP, Lee HJ. Model on the microbial quality change of seasoned soybean sprouts for on-line shelf life prediction. Int. J. Food Microbiol. 18: 285-293 (2007)
  14. Seo I, An DS, Park JP, Lee DS. Modeling of mould/yeast growth on Korean braised lotus root cuts as a primary quality index in storage. J. Foodservice 20: 143-151 (2009) https://doi.org/10.1111/j.1748-0159.2009.00136.x
  15. Bernaerts K, Dens E, Vereecken K, Geeraerd A, Devlieghere F, Debevere J, Van Impe JF. Modeling microbial dynamics under timevarying conditions. pp. 243-261. In: Modeling Microbial Responses in Food. McKellar RC, Lu X (eds). CRC Press, Boca Raton, FL, USA (2004)
  16. Chung SK, Lyu ES, Lee DS. Exploration of preservation hurdles in Korean traditional side dishes. Korean J. Food Preserv. 13: 259-268 (2006)
  17. Forsythe SJ. The Microbiological Risk Assessment of Food. Blackwell Science, Oxford, UK. pp. 34-112 (2002)
  18. Shapton DA, Shapton NF. Principles of Practices for the Safe Processing of Foods. Butterworth-Heinemann, Oxford, UK. pp. 388-441 (1994)
  19. Leistner L, Gould GW. Hurdle Technologies. Kluwer Academic/ Plenum Publishers, New York, USA. pp. 1-15 (2002)
  20. Joo IS, Lee JH, Kim SE, Sin DW, Jung LS, Sim Y, Kim HJ, Kim KH, Byen GJ, Heo OS, Bang OG. Detection of Listeria monocytogenes in the circulated frozen fishes and processed marine products. Vol. 6, pp. 155-161. In: The Annual Report of KFDA, Korea Food & Drug Administration, Seoul, Korea (2002)
  21. Kang CS, Han SB, Han JA, Kim JS, Kim JY, Paek OJ, Park EJ, Kim SH, Lee YJ. Studies on major foodborne pathogenic bacteria in ready-to eat foods. Vol. 6, pp. 95-104. In: The Annual Report of KFDA, Korea Food & Drug Administration, Seoul, USA (2005)
  22. Park SH, Lee DH, Kwak HS, Kang YS, Park YC, Cho YS, Kim CM. Studies on prevalence and detection of Clostridium perfringens in foods (I). Vol. 4, pp. 32-38. In: The Annual Report of KFDA, Korea Food & Drug Administration, Seoul, Korea (2000)
  23. Woo GJ, Hwang IG, Kwak HS, Park JS, Kim MG, Lee GY, Koh YH, Moon SY, Byun JS, Ahn SK, Her MS. Monitoring on foodborne pathogenic microorganism in foods. Vol. 8-1, pp. 560- 568. In: The Annual Report of KFDA, Korea Food & Drug Administration, Seoul, Korea (2004)
  24. Woo GJ, Lee DH, Park SH, Kwak HS, Kang YS, Park JS, Park YC, Cho YS, Kim CM. Studies on prevalence and detection of Clostridium perfringens in foods (II). Vol. 5, pp. 44-52. In: The Annual Report of KFDA, Korea Food & Drug Administration, Seoul, Korea (2001)
  25. Rho M-J, Chung MS, Park J. Predicting the contamination of Listeria monocytogenes and Yersinia enterocolitica in pork production using Monte Carlo simulation. Korean J. Food Sci. Technol. 35: 928-936 (2003)
  26. Hass CN, Rose JB, Gerba CP. Quantitative Microbial Risk Assessment. John Wiley & Sons, New York, USA. pp. 162-187 (1999)
  27. Olmez HK, Aran N. Modeling the growth kinetics of Bacillus cereus as a function of temperature, pH, sodium lactate, and sodium chloride concentrations. Int. J. Food Microbiol. 98: 135-143 (2005) https://doi.org/10.1016/j.ijfoodmicro.2004.05.018
  28. Buchanan RL, Bagi LK, Goins RV, Phillips JG. Response surface models for the growth kinetics of Escherichia coli O157:H7. Food Microbiol. 10: 303-315 (1993) https://doi.org/10.1006/fmic.1993.1035
  29. Smith-Simpson S, Schaffner DW. Development of a model to predict growth of Clostridium perfringens in cooked beef during cooling. J. Food Protect. 68: 336-341 (2005)
  30. Zurera-Cosano G, Castillejo-Rodriguez AM, Garcia-Gimeno RM, Rincon-Leon F. Performance of response surface and Davey model for prediction of Staphylococcus aureus growth parameters under different experimental conditions. J. Food Protect. 67: 1138-1145 (2004)
  31. Augustin J-C, Carlier V. Mathematical modelling of the growth rate and lag time for Listeria monocytogenes. Int. J. Food Microbiol. 56: 29-51 (2000) https://doi.org/10.1016/S0168-1605(00)00223-3
  32. IFR. Combase Predictor. Available from: Institute of Food Research (http://www.ifr.ac.uk/Safety/GrowthPredictor/). Accessed Jan 28, 2008
  33. Huang L. Estimation of growth of Clostridium perfringens in cooked beef under fluctuating temperature conditions. Food Microbiol. 20: 549-559 (2003) https://doi.org/10.1016/S0740-0020(02)00155-7
  34. Nauta MJ, Litman S, Barker GC, Carlin F. A retail and consumer phase model for exposure assessment of Bacillus cereus. Int. J. Food Microbiol. 83: 205-218 (2003) https://doi.org/10.1016/S0168-1605(02)00374-4
  35. Li KY, Torres JA. Microbial growth estimation in liquid media exposed to temperature fluctuations. J. Food Sci. 58: 644-648 (1993) https://doi.org/10.1111/j.1365-2621.1993.tb04347.x
  36. Baranyi J, Roberts TA. A dynamic approach to predicting bacterial growth in food. Int. J. Food Microbiol. 23: 277-294 (1994) https://doi.org/10.1016/0168-1605(94)90157-0
  37. Park JP, Lee DS. Analysis of temperature effect on microbial growth parameters and estimation of food shelf life with confidence band. J. Food Sci. Nutr. 13: 104-111 (2008) https://doi.org/10.3746/jfn.2008.13.2.104
  38. Lee K-E, An DS, Lyu ES, Chung SK, Lee DS. Identification of hurdles for improving storage stability of braised kidney beans, a Korean seasoned side dish. J. Food Process. Preserv. 33: 33-46 (2009) https://doi.org/10.1111/j.1745-4549.2008.00235.x