References
- Kang HJ, Kim YH, Son GW. Contamination level of retail meat and chickens by quantitative test of food poisoning bacteria. J. Food Hyg. Safety 15: 204-208 (2000)
- Hong CH, An SC. Isolation and stereotyping of Listeria monocytogenes in pork fabrication processing environment. J. Food Hyg. Safety 13: 425-429 (1998)
- Rho MJ, Chung MS, Lee JH, Park J. Monitoring of microbial hazards at farms, slaughterhouses. and processing lines of swine in Korea. J. Food Protect. 64: 1388-1391 (2001) https://doi.org/10.4315/0362-028X-64.9.1388
- Gill CO, Greer GG, Dilts BD. The aerobic growth of Aeromonas hydrophila and Listeria monocytogenes in broths and on pork. Int. J. Food Microbiol. 35: 67-74 (1997) https://doi.org/10.1016/S0168-1605(96)01224-X
- Chung MS, Lee SW, Park GY, Lee HH, Lee CS, Lee JH. Analysis of microbiological hazards at pork processing plants in Korea. Korean J. Food Sci. Anim. Resour. 19: 36-40 (1999)
- Rho MJ, Chung MS. Park JY. Predicting the contamination of Listeria monocytogenes and Yersinia enterocolotica in pork production using Monte Carlo simulation. Korean J. Food Sci. Technol. 35: 928-936 (2003)
- Hoornstra E. Notermars S. Quantitative microbiological risk assessment. Int. J. Food Microbiol. 66: 21 -29 (2001) https://doi.org/10.1016/S0168-1605(00)00529-8
- Davidson VJ, Ryks J. Comparison of Monte Carlo and Fuzzy math simulation methods for quantitative microbial risk assessment. J. Food Protect. 10: 1900-1910 (2003)
- George SM, Richardson LCC, Peck MW. Predictive models of the effect of temperature, pH and acetic and lactic acids on the growth of Listeria monocvtogenes. Int. J. Food Microbiol. 32: 73-90(1996) https://doi.org/10.1016/0168-1605(96)01108-7
- Nauta MJ. Separation of uncertainty and variability in quantitative microbial risk assessment. Int. J. Food Microbiol. 57: 9-18 (2000) https://doi.org/10.1016/S0168-1605(00)00225-7
- Poschet F, Geeraerd AH, Scheerlinck N, Nicolai BM, Van Impe JF. Monte Carlo analysis as a tool to incorporate variation on experimental data in predictive microbiology. Int. J. Food Microbiol. 20: 285-295 (2003) https://doi.org/10.1016/S0740-0020(02)00156-9
- Quelch J, Cameron IT. Uncertainty representation and propagation in quantified risk assessment using fuzzy sets. J. Loss Prev. Process Ind. 7: 463-473 (1994) https://doi.org/10.1016/0950-4230(94)80004-9
- Zhang Q, Lichfield JB. Applying fuzzy mathematics to product development and comparison. Food Technol. 45: 108-112 (1991)
- Lee SJ. Introduction about fuzzy theory. Food Sci. Ind. 33: 20-26 (2000)
- Zimmermann HJ. Fuzzy Set Theory and its Applications. Kluwer Academic Publishers, MA, USA (1991)
- Chen SJ, Hwang CL. Fuzzy Multiple Attribute Decision Making-Methods and Application. Springer, Berlin, Germany (1992)
- Giachetti RE, Young RE. Analysis of the error in the standard approximation of triangular and trapezoidal fuzzy numbers and the development of a new algorithm. Fuzzy Sets Sys. 91: 1-13 (1997) https://doi.org/10.1016/S0165-0114(96)00118-2
- Palisade. Guide to using @RISK: risk analysis and simulation add-in for Microsoft Excel, Vers. 4. Palisade Corp., Newfield, NY, USA (2000)
- Klir GJ, Folger TA. Fuzzy Set, Uncertainty and Information. Prentice-Hall International, Inc., London, UK (1988)
- Zadeh LA. Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans. SMC. 3: 28-44 (1973)