DOI QR코드

DOI QR Code

어린잎채소의 생산·가공 공정 중 미생물 오염도 분석 및 총균수 예측모델 개발

Analysis of Microbial Contamination in Microgreen from Harvesting and Processing Steps and the Development of the Predictive Model for Total Viable Counts

  • 강미선 (과학기술연합대학원대학교 식품생명공학) ;
  • 김현정 (과학기술연합대학원대학교 식품생명공학)
  • Kang, Mi Seon (Department of Food Biotechnology, University of Science and Technology) ;
  • Kim, Hyun Jung (Department of Food Biotechnology, University of Science and Technology)
  • 투고 : 2021.11.30
  • 심사 : 2021.12.16
  • 발행 : 2021.12.31

초록

This study was performed to assess the microbiological quality and safety of microgreen sampled from harvesting farms and food processing plant in Korea. The samples were analyzed for total viable counts, coliforms, Enterobacteriaceae, Escherichia coli, Salmonella spp., Listeria monocytogenes, Vibrio parahaemolyticus, Bacillus cereus, and Staphylococcus aureus. Total viable counts were highly contaminated in samples collected from farms (7.7~8.2 log CFU/g) and the final products (5.8~7.8 log CFU/g), respectively. B. cereus was detected less than 100 CFU/g, which was satisfied with Korean standards (<1,000 CFU/g) of fresh-cut produce. A predictive model was developed for the changes of total viable counts in microgreens during storage at 5~35℃. The predictive models were developed using the Baranyi model for the primary model and the square root model for the secondary model. The results obtained in this study can be useful to develop the safety management options along the food chain, including fresh-cut produce storage and distribution.

키워드

과제정보

This research was supported by a research grant of Korea Food Research Institute and of Export promotion technology development program by iPET (Korea Institute of Planning and Evaluation for Technology in Food, Agriculture, Forestry and Fisheries). We appreciate for the technical service provided by Tae Young Oh.

참고문헌

  1. Baranyi J, Roberts TA. 1994. A dynamic approach to predicting bacterial growth in food. Int. J. Food Microbiol., 23(3-4):277-294. https://doi.org/10.1016/0168-1605(94)90157-0
  2. Carstens CK, Salazar JK,Darkoh C. 2019. Multistate outbreaks of foodborne illness in the United States associated with fresh produce from 2010 to 2017. Front. Microbiol., 10:2667. https://doi.org/10.3389/fmicb.2019.02667
  3. ISO. 2004. Microbiology of food and animal feeding stuffs-horizontal methods for detection and enumeration of Enterobacteriaceae. International Organization for Standardization, Switzerland.
  4. Kim HJ. 2009. New Technology-Application of Predictive Microbiology in Food Safety. Bull. Food Tech., 22(2):339-348.
  5. Kim SR, Chu H, Yi SW, Jang YJ, Shim WB, Nguyen BH, Kim WI, Kim HJ, Ryu K. 2019a. Investigation of hazardous microorganisms in baby leafy vegetables collected from a Korean market and distribution company. J. Food Hyg. Saf., 34(6):526-533. https://doi.org/10.13103/JFHS.2019.34.6.526
  6. Kim SR, Lee JY, Lee SH, Kim WI, Park KH, Yun HJ, Kim BS, Chung DH, Yun JC, Ryu KY. 2011. Evaluation of microbiological safety of lettuce and cultivation area. J. Food Hyg. Saf., 26(4):289-295.
  7. Kim SH, Lee GY, Heo SY, Lee W. 2019b. Research on Fresh-cut Fruits and Vegetables. Korea Rural Economic Institue Research Report, pp1-307.
  8. Lee CG. 2012. Production trend and task of fresh-cut produce. Food Preserv. Process. Ind., 11(2):12-18.
  9. Lee ES, Kwak MG, Kim WI, An HM, Lee HS, Ryu SH, Kim HY, Ryu JG, Kim SR. 2016. Investigation of microbial contamination level during production of baby leafy vegetables. J. Food Hyg. Saf., 31(4):264-271. https://doi.org/10.13103/JFHS.2016.31.4.264
  10. Oh TY, Baek SY, Choi JH, Jeong MC, Koo OK, Kim SM, Kim HJ. 2016. Analysis of foodborne pathogens in Brassica campestris var. narinosa microgreen from harvesting and processing steps. J. Appl. Biol. Chem., 59(1):63-68. https://doi.org/10.3839/jabc.2016.012
  11. Park GJ, Gu MS, Jeong MC. 2016. Sterilization and washing technology of fresh-cut produce. Food Preserv. Process. Ind., 15(1): 18-25.
  12. Ratkowsky DA, Olley J, McMeekin T, Ball A. 1982. Relationship between temperature and growth rate of bacterial cultures. J. Bacteriol., 149(1):1-5. https://doi.org/10.1128/jb.149.1.1-5.1982
  13. Uphoff H, Hedrich B, Strotmann I, Arvand M, Bettge-Weller G,Hauri A. 2014. A prolonged investigation of an STEC-O104 cluster in Hesse, Germany, 2011 and implications for outbreak management. J. Public Health, 22(1):41-48. https://doi.org/10.1007/s10389-013-0595-2
  14. Van Abel N, Schoen ME, Kissel JC,Meschke JS. 2017. Comparison of risk predicted by multiple norovirus dose-response models and implications for quantitative microbial risk assessment. Risk Anal., 37(2):245-264. https://doi.org/10.1111/risa.12616
  15. Yoon Y-H. 2010. Principal theory and application of predictive microbiology. Food Sci. Ind., 43(1):70-74. https://doi.org/10.23093/FSI.2010.43.1.70
  16. Ministry of Food and Drug Safety (MFDS), 2021a, Food Code. Available from https://www.foodsafetykorea.go.kr/foodcode, [cited November, 2021]
  17. Ministry of Food and Drug Safety (MFDS), 2021b, Food Sanitation Act, Available from https://www.law.go.kr/법령/식품위생법시행규칙, [cited November, 2021]
  18. National Agricultural Products Quality Management Service (NAQS), 2021, Agricultural products standards, Available from https://www.law.go.kr/행정규칙/농산물표준규격/(2020-16,20201014), [cited November, 2021]
  19. U.S. Food and Drug Administration (FDA), 2021, FSMA Final Rule on Produce Safety. Standards for the Growing, Harvesting, Packing, and Holding of Produce for Human Consumption. Available from https://www.fda.gov/food/food-safety-modernization-actfsma/fsma-final-rule-produce-safety, [cited November, 2021]