Food Science and Preservation (한국식품저장유통학회지)

The Korean Society of Food Preservation (한국식품저장유통학회)
권호 표지
DOI QR코드

DOI QR Code

Mathematical modeling of growth of Escherichia coli strain RC-4-D isolated from red kohlrabi sprout seeds

적콜라비 새싹채소 종자에서 분리한 Escherichia coli strain RC-4-D의 생장예측모델

  • Choi, Soo Yeon (Microbial Safety Team, National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Ryu, Sang Don (Microbial Safety Team, National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Park, Byeong-Yong (Microbial Safety Team, National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Kim, Se-Ri (Microbial Safety Team, National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Kim, Hyun-Ju (Microbial Safety Team, National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Lee, Seungdon (Microbial Safety Team, National Institute of Agricultural Sciences, Rural Development Administration) ;
  • Kim, Won-Il (Microbial Safety Team, National Institute of Agricultural Sciences, Rural Development Administration)
  • 최수연 (농촌진흥청 국립농업과학원 농산물안전성부 유해생물팀) ;
  • 류상돈 (농촌진흥청 국립농업과학원 농산물안전성부 유해생물팀) ;
  • 박병용 (농촌진흥청 국립농업과학원 농산물안전성부 유해생물팀) ;
  • 김세리 (농촌진흥청 국립농업과학원 농산물안전성부 유해생물팀) ;
  • 김현주 (농촌진흥청 국립농업과학원 농산물안전성부 유해생물팀) ;
  • 이승돈 (농촌진흥청 국립농업과학원 농산물안전성부 유해생물팀) ;
  • 김원일 (농촌진흥청 국립농업과학원 농산물안전성부 유해생물팀)
  • Received : 2017.09.06
  • Accepted : 2017.10.25
  • Published : 2017.10.30
Download PDF
⟨ Previous Next ⟩

Abstract

This study was conducted to develop a predictive model for the growth of Escherichia coli strain RC-4-D isolated from red kohlrabi sprout seeds. We collected E. coli kinetic growth data during red kohlrabi seed sprouting under isothermal conditions (10, 15, 20, 25, and $30^{\circ}C$). Baranyi model was used as a primary order model for growth data. The maximum growth rate (${\mu}max$) and lag-phase duration (LPD) for each temperature (except for $10^{\circ}C$ LPD) were determined. Three kinds of secondary models (suboptimal Ratkowsky square-root, Huang model, and Arrhenius-type model) were compared to elucidate the influence of temperature on E. coli growth rate. The model performance measures for three secondary models showed that the suboptimal Huang square-root model was more suitable in the accuracy (1.223) and the suboptimal Ratkowsky square-root model was less in the bias (0.999), respectively. Among three secondary order model used in this study, the suboptimal Ratkowsky square-root model showed best fit for the secondary model for describing the effect of temperature. This model can be utilized to predict E. coli behavior in red kohlrabi sprout production and to conduct microbial risk assessments.

본 연구는 시중 유통되고 있는 새싹채소 재배용 적콜라비 종자에서 분리한 E. coli strain RC-4-D의 생장예측모델을 개발하기 위해 수행되었다. 각 온도조건(10, 15, 20, 25, $30^{\circ}C$) 별로 적콜라비 중 E. coli strain RC-4-D 밀도 변화를 조사하였고 Baranyi model을 1차 생장예측모델로 이용하였고 각 온도별로 최대생장률(${\mu}max$)과 $10^{\circ}C$를 제외한 유도기(LPD) 값을 도출하였다. E. coli strain RC-4-D의 최대생장률에 대한 2차 생장예측모델로써 suboptimal Ratkowsky square-root, suboptimal Huang square-root, suboptimal Arrhenius-type 세 종류의 모델을 비교하였다. 모델 적합성 검정 결과, suboptimal Huang square-root 모델이 정확도가 가장 높고 suboptimal Ratkowsky square-root 모델이 편차가 가장 적은 것으로 나타났다. 종합적으로, RMSE가 0.100, $A_f$가 1.255, $B_f$가 0.999인 suboptimal Ratkowsky square-root 모델이 온도의 영향을 설명하는 가장 적합한 2차 생장예측 모델인 것으로 나타났다. 본 연구에서 개발한 모델은 적콜라비 새싹채소 생산에 있어서 E. coli의 생장을 예측하고 미생물 위해성평가를 수행하는데 활용될 것으로 기대된다.

Keywords

References

  1. Rickman JC, Bruhn CM, Barrett DM (2007) Nutritional comparison of fresh, frozen, and canned fruits and vegetables II. Vitamin A and carotenoids, vitamin E, minerals and fiber. J Sci Food Agric, 87, 1185-1196 https://doi.org/10.1002/jsfa.2824
  2. Um YC, Jang YA, Yun HK, Seo MH, Lee HE, Lee JG (2013) Spouts and baby leaf. National Institute of Horticultural and Herbal Science, p 15
  3. Park WT, Kim JK, Park S, Lee S-W, Li X, Kim YB, Uddin MR, Park NI, Kim S-J, Park SU (2012) Metabolic profiling of glucosinolates, anthocyanins, carotenoids, and other secondary metabolites in Kohlrabi (Brassica oleracea var. gongylodes). J Agric Food Chem, 60, 8111-8116 https://doi.org/10.1021/jf301667j
  4. Callejon RM, Rodriguez-Naranjo MI, Ubeda C, Hornedo-Ortega R, Garcia-Parrilla MC, Troncoso AM (2015) Reported foodborne outbreaks due to fresh produce in the United States and European Union: trends and causes. Foodborne pathog Dis, 12, 32-38 https://doi.org/10.1089/fpd.2014.1821
  5. Nsoesie EO, Gordon SA, Brownstein JS (2014) Online reports of foodborne illness capture foods implicated in official foodborne outbreak reports. Prev Med, 67, 264-269 https://doi.org/10.1016/j.ypmed.2014.08.003
  6. Centers for Disease Control and Prevention: Foodborne outbreak online database (FOOD Tool). https://wwwn.cdc.gov/foodborneoutbreaks/. (Accessed August 2014)
  7. Portnoy BL, Goepfert JM, Harmon SM (1976) An outbreak of Bacillus cereus food poisoning resulting from contaminated vegetable sprouts. Am J Epidemiol, 103, 589-594 https://doi.org/10.1093/oxfordjournals.aje.a112263
  8. Buchholz U, Bernard H, Werber D, Boohmer MM, Remschmidt C, Wilking H, Delere Y, an der Heiden M, Adlhoch C, Dreesman J, Ehlers J, Ethelberg S, Faber M, Frank C, Fricke G, Greiner M, Hohle M, Ivarsson S, Jark U, Kirchner M, Koch J, Krause G, Luber P, Rosner B, Stark K, Kuhne M (2011) German Outbreak of Escherichia coli O104:H4 Associated with Sprouts. N Engl J Med, 365, 1763-1770 https://doi.org/10.1056/NEJMoa1106482
  9. CDC (2013) Outbreak of Escherichia coli O104:H4 infections associated with sprout consumption-Europe and North America, May-July 2011. MMWR Morb Mortal Wkly Rep, 62, 1029-1031
  10. NACMCF (1999) Microbiological safety evaluations and recommendations on sprouted seeds. Int J Food Microbiol, 52, 123-153 https://doi.org/10.1016/S0168-1605(99)00135-X
  11. Taormina PJ, Beuchat LR, Slutsker L (1999) Infections associated with eating seed sprouts: an international concern. Emerg Infect Dis, 5, 626-634 https://doi.org/10.3201/eid0505.990503
  12. Castro-Rosas J, Escartin EF (2000) Survival and growth of Vibrio cholerae O1, Salmonella typhi, and Escherichia coli O157: H7 in alfalfa sprouts. J Food Sci, 65, 162-165 https://doi.org/10.1111/j.1365-2621.2000.tb15973.x
  13. Ross T, McMeekin TA (2003) Modeling microbial growth within food safety risk assessments. Risk Anal, 23, 179-197 https://doi.org/10.1111/1539-6924.00299
  14. 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
  15. Baranyi J, Roberts TA (1995) Mathematics of predictive food microbiology. Int J Food Microbiol, 26, 199-218 https://doi.org/10.1016/0168-1605(94)00121-L
  16. Danyluk MD, Friedrich LM, Schaffner DW (2014) Modeling the growth of Listeria monocytogenes on cut cantaloupe, honeydew and watermelon. Food Microbiol, 38, 52-55 https://doi.org/10.1016/j.fm.2013.08.001
  17. Franz E, Semenov AV, Van Bruggen AH (2008) Modelling the contamination of lettuce with Escherichia coli O157:H7 from manure-amended soil and the effect of intervention strategies. J Appl Microbiol, 105, 1569-1584 https://doi.org/10.1111/j.1365-2672.2008.03915.x
  18. Sant'Ana AS, Franco BD, Schaffner DW (2012) Modeling the growth rate and lag time of different strains of Salmonella enterica and Listeria monocytogenesin ready-to-eat lettuce. Food Microbiol, 30, 267-273 https://doi.org/10.1016/j.fm.2011.11.003
  19. Baranyi J, Roberts TA (1995) Mathematics of predictive food microbiology. Int J Food Microbiol, 26, 199-218 https://doi.org/10.1016/0168-1605(94)00121-L
  20. Huang L (2014) IPMP 2013--a comprehensive data analysis tool for predictive microbiology. Int J Food Microbiol, 171, 100-107 https://doi.org/10.1016/j.ijfoodmicro.2013.11.019
  21. Ross T (1996) Indices for performance evaluation of predictive models in food microbiology. J Appl Bacteriol, 81, 501-508
  22. Barret M, Briand M, Bonneau S, Preveaux A, Valiere S, Bouchez O, Hunault G, Simoneau P, Jacques M-A (2015) Emergence Shapes the Structure of the Seed Microbiota. Appl Environ Microbiol, 81, 1257-1266 https://doi.org/10.1128/AEM.03722-14
  23. Kim SA, Kim OM, Rhee MS (2013) Changes in microbial contamination levels and prevalence of foodborne pathogens in alfalfa (Medicago sativa) and rapeseed (Brassica napus) during sprout production in manufacturing plants. Lett Appl Microbiol, 56, 30-36 https://doi.org/10.1111/lam.12009
  24. Ingraham JL, and A. G. Marr (1965) Control of enzyme biosynthesis at temperatures near the minimum for growth of E. coli. Colloq Int CNRS, 124, 319-328
  25. Maxwell KS, Allen GM, John LI (1971) Determination of the minimal temperature for growth of Escherichia coli. J Bacteriol, 105, 683-684
  26. Tokumasu S, Kanada I, Kato M (1985) Germination Behaviour of Seeds as Affected by Different Temperatures in Some Species of Brassica. J Japan Soc Hort Sci, 54, 364-370 https://doi.org/10.2503/jjshs.54.364
  27. Son NR, Kim AN, Choi WS, Yoon SH, Suh SH, Joo IS, Kim SH, Kwak HS, Cho JI (2017) Development of a predictive model describing the growth of Staphylococcus aureus in processed meat product galbitang. Korean J Food Sci Technol, 49, 274-278
  28. Hong CH, Sim WC, Chun SJ, Kim YS, Oh DH, Ha SD, Choi WS, Bahk GJ (2005) Predictive growth model of native isolated Listeria monocytogenes on raw pork as a function of temperature and time. Korean J Food Sci Technol, 37, 850-855