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http://dx.doi.org/10.5713/ajas.18.0322

Quantitative microbial risk assessment of Campylobacter jejuni in jerky in Korea  

Ha, Jimyeong (Department of Food and Nutrition, Sookmyung Women's University)
Lee, Heeyoung (Department of Food and Nutrition, Sookmyung Women's University)
Kim, Sejeong (Department of Food and Nutrition, Sookmyung Women's University)
Lee, Jeeyeon (Department of Food and Nutrition, Sookmyung Women's University)
Lee, Soomin (Department of Food and Nutrition, Sookmyung Women's University)
Choi, Yukyung (Department of Food and Nutrition, Sookmyung Women's University)
Oh, Hyemin (Department of Food and Nutrition, Sookmyung Women's University)
Yoon, Yohan (Department of Food and Nutrition, Sookmyung Women's University)
Publication Information
Asian-Australasian Journal of Animal Sciences / v.32, no.2, 2019 , pp. 274-281 More about this Journal
Abstract
Objective: The objective of this study was to estimate the risk of Campylobacter jejuni (C. jejuni) infection from various jerky products in Korea. Methods: For the exposure assessment, the prevalence and predictive models of C. jejuni in the jerky and the temperature and time of the distribution and storage were investigated. In addition, the consumption amounts and frequencies of the products were also investigated. The data for C. jejuni for the prevalence, distribution temperature, distribution time, consumption amount, and consumption frequency were fitted with the @RISK fitting program to obtain appropriate probabilistic distributions. Subsequently, the dose-response models for Campylobacter were researched in the literature. Eventually, the distributions, predictive model, and dose-response model were used to make a simulation model with @RISK to estimate the risk of C. jejuni foodborne illness from the intake of jerky. Results: Among 275 jerky samples, there were no C. jejuni positive samples, and thus, the initial contamination level was statistically predicted with the RiskUniform distribution [RiskUniform (-2, 0.48)]. To describe the changes in the C. jejuni cell counts during distribution and storage, the developed predictive models with the Weibull model (primary model) and polynomial model (secondary model) were utilized. The appropriate probabilistic distribution was the BetaGeneral distribution, and it showed that the average jerky consumption was 51.83 g/d with a frequency of 0.61%. The developed simulation model from this data series and the dose-response model (Beta Poisson model) showed that the risk of C. jejuni foodborne illness per day per person from jerky consumption was $1.56{\times}10^{-12}$. Conclusion: This result suggests that the risk of C. jejuni in jerky could be considered low in Korea.
Keywords
Campylobacter jejuni; Jerky; Quantitative Microbial Risk Assessment;
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1 Eidson M, Sewell CM, Graves G, Olson R. Beef jerky gastroenteritis outbreaks. J Environ Health 2000;62:9-13.
2 Tauxe RV. Emerging foodborne diseases: an evolving public health challenge. Emerg Infect Dis 1997;3:425-34.   DOI
3 Acheson D, Allos BM. Campylobacter jejuni infections: update on emerging issues and trends. Clin Infect Dis 2001;32:1201-6.   DOI
4 Allos BM, Blaser MJ. Campylobacter jejuni and the expanding spectrum of related infections. Clin Infect Dis 1995;20:1092-9.   DOI
5 Friedman CR, Neimann J, Wegener HC, Tauxe RV. Epidemiology of Campylobacter jejuni infections in the United States and other industrialized nations. 2nd ed. Washington, DC, USA: ASM International Press; 2000.
6 Altekruse SF, Stern NJ, Fields PI, Swerdlow DL. Campylobacter jejuni-an emerging foodborne pathogen. Emerg Infect Dis 1999;5:28-35.   DOI
7 Fricker CR, Park RWA. A two‐year study of the distribution of ‘thermophilic’ campylobacters in human, environmental and food samples from the Reading area with particular reference to toxin production and heat‐stable serotype. J Appl Bacteriol 1989;66:477-90.   DOI
8 Penner JL. The genus Campylobacter: a decade of progress. Clin Microbiol Rev 1988;1:157-72.   DOI
9 Allos BM. Association between Campylobacter infection and Guillain-Barre syndrome. J Infect Dis 1997;176(Suppl 2):S125-8.   DOI
10 Nauta MJ. Separation of uncertainty and variability in quantitative microbial risk assessment models. Int J Food Microbiol 2000;57:9-18.   DOI
11 Codex. Principles and guidelines for the conduct of microbiological risk assessment. CAC/GL 30. 1999. [cited 2018 March 15]. Available from: www.fao.org/docrep/004/y1579e/y1579e05.htm
12 FSAI (Food Safety Authority of Ireland), Guidelines for the interpretation of results of microbiological testing of ready-to-eat foods placed on the market (Revision 2). Dublin, Ireland: Food Safety Authority of Ireland; 2016.
13 Duffy G, Cummins E, Nally P, O’Brien S, Butler F. A review of quantitative microbial risk assessment in the management of Escherichia coli O157:H7 on beef. Meat Sci 2006;74:76-88.   DOI
14 Notermans S, Teunis P. Quantitative risk analysis and the production of microbiologically safe food: an introduction. Int J Food Microbiol 1996;30:3-7.   DOI
15 CFS (Centre for Food Safety), Microbiological guidelines for food. Queensway, Hong Kong: Centre for Food Safety; 2014.
16 Yamazaki-Matsune W, Taguchi M, Seto K, et al. Development of a multiplex PCR assay for identification of Campylobacter coli, Campylobacter fetus, Campylobacter hyointestinalis subsp. hyointestinalis, Campylobacter jejuni, Campylobacter lari and Campylobacter upsaliensis. J Med Microbiol 2007;56:1467-73.   DOI
17 Wang RF, Slavik MF, Cao WW. A rapid PCR method for direct detection of low numbers of Campylobacter jejuni. J Rapid Methods Autom Microbiol 1992;1:101-8.   DOI
18 van Boekel MA. On the use of the Weibull model to describe thermal inactivation of microbial vegetative cells. Int J Food Microbiol 2002;74:139-59.   DOI
19 Baranyi J, Ross T, McMeekin TA, Rogerts TA. Effects of parameteriza¬tion on the performance of empirical models used in ‘predictive microbiology’. Food Microbiol 1996;13:83-91.   DOI
20 Jung H. Consumer survey and hazard analysis for the improvement of food hygiene and safety in purchase. [master's thesis]. Seoul, Korea: Korea University; 2011.
21 Lee J, Ha J, Kim S, et al. Quantitative microbial risk assessment for Campylobacter spp. on ham in Korea. Korean J Food Sci Anim Resour 2015;35:674-82.   DOI
22 Teunis P, Havelaar A. The beta Poisson dose-response model is not a single-hit model. Risk Anal 2000;20:513-20.   DOI
23 Kim S, Jeong J, Lee H, et al. Kinetic behavior of Campylobacter jejuni in beef tartare at cold temperatures and transcriptomes related to its survival. J Food Protect 2017;80:2127-31.   DOI
24 Nauta MJ, Jacobs-Reitsma WF, Havelaar AH. A risk assessment model for Campylobacter in broiler meat. Risk Anal 2007; 27:845-61.   DOI
25 Jeong J, Lee J, Lee H, et al. Quantitative microbial risk assessment for Campylobacter foodborne illness in raw beef offal consumption in South Korea. J Food Protect 2017;80:609-18.   DOI
26 Mafart P, Couvert O, Gailard S, Leguérinel I. On calculating sterility in thermal preservation methods: application of the Weibull frequency distribution model. Int J Food Microbiol 2002;72:107-13.   DOI
27 Calicioglu M, Sofos JN, Kendall PA. Influence of marinades on survival during storage of acid-adapted and nonadapted Listeria monocytogenes inoculated post-drying on beef jerky. Int J Food Microbiol 2003;86:283-92.   DOI
28 MFDS (Ministry of Food and Drug Safety), Microbial Risk Assessment for Campylobacter spp. in Meat and Processed Meat Products; 2016. Report No.: 1475009182.
29 Pegg RB, Amarowicz R, Code WE. Nutritional characteristics of emu (Dromaius novaehollandiae) meat and its value-added products. Food Chem 2006;97:193-202.   DOI
30 Han DJ, Jeong JY, Choi JH, et al. Effects of drying conditions on quality properties of pork jerky. Korean J Food Sci Anim Resour 2007;27:29-34.   DOI
31 Keene WE, Sazie E, Kok J, et al. An outbreak of Escherichia coli 0157: H7 infections traced to jerky made from deer meat. JAMA 1997;277:1229-31.   DOI