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LC-MS/MS를 이용한 우유 중 덱사메타손의 잔류 분석법 개발 KCI 등재

Development of an Analytical Method for the Determination of Dexamethasone in Bovine Milk Using Liquid Chromatography Coupled to Tandem Mass Spectrometry

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한국식품위생안전성학회지 (Journal of Food Hygiene and Safety)
한국식품위생안전성학회 (Korean Society of Food Hygiene and Safety)
초록

본 연구는 우유 중에서 덱사메타손을 효과적으로 정량 분석하기 위한 LC-MS/MS법을 확립하고 이를 검증하기 위해 수행되었다. 확립된 LC-MS/MS에 대해 특이성, 검출 한계, 정량한계, 정확도 및 정밀도에 대한 검증을 통하여 유효성을 확인하였다. 표준용액을 이용하여 검량성을 작성한 결과, r2 > 0.999 이상의 직선성을 확인하였고, 덱사 메타손에 대한 검출한계와 정량한계는 각각 0.15와 0.5 ng/ mL이었다. 또한, 회수율은 98.9-109.6%로 나타났으며, 상대표준편차는 1.7-4.4%로 나타나 정확성이 우수하였으며, 이는 식품의약품안전처의 잔류동물용의약품 분석법에서 제시한 기준에 모두 적합한 수준이었다. 따라서 본 연구를 통해 개발된 LC-MS/MS법은 향후 우유 중 덱사메타손을 분석하는데 효과적으로 활용될 수 있을 것으로 사료된다.

An analytical method for the determination of dexamethasone (DM) in bovine milk samples was developed and validated using liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). Milk samples were extracted by the liquid-liquid extraction based on acetonitrile. The chromatographic separation was achieved on a reverse phase C18 column with gradient elution using a mobile phase of 0.1% formic acid in 95% acetonitrile. The procedure was validated according to the Ministry of Food and Drug Safety guideline determining accuracy, precision, limit of detection (LOD), and limit of quantification (LOQ). Mean recoveries of DM from spiked milk samples (25, 125, and 1,250 ng/mL) were 98.9-109.6%, and the relative standard deviation was between 1.7 and 4.4%. Linearity in concentration range of 12.5-1,250 ng/mL was obtained with the correlation coefficient (r2) of 0.9997. LOD and LOQ for the investigated DM were 0.15 and 0.5 ng/mL depending on milk samples, respectively. This method was reliable, sensitive, economical and suitable for routine monitoring of DM residues in bovine milk.

목차
Materials and Methods
  시약 및 재료
  표준원액 및 표준용액의 조제
  추출 및 정제
  기기분석조건
  분석법 검증
 Results and Discussion
  LC-MS/MS
  분석법의 검출한계 및 정량한계
  검량선, 회수율 및 정밀성
 국문요약
 References
저자
  • 차춘남(경상대학교 공과대학, 산업시스템공학부, Engineering Research Institute and Department of Industrial Systems Engineering, Gyeongsang National University, Chinju, Korea) | Chun-Nam Cha
  • 박은기(고신대학교 의과대학, 인문사회의학교실, Medical Humanities and Social Medicine, College of Medicine, Kosin University, Busan, Korea) | Eun-Kee Park
  • 유창열(경남도립남해대학, 스마트융합정보과, Department of Smart Information Convergence, Gyeongnam Provincial Namhae College, Namhae, Korea) | Chang-Yeul Yoo
  • 이성중(경상대학교 화학과·생명과학연구소, Department of Chemistry and Research Institute of Live Science, Gyeongsang National University, Chinju, Korea) | Sung Joong Lee
  • 손송이(경상대학교 수의과대학·동물의학연구소, Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University) | Song-Ee Son
  • 김석(경상대학교 수의과대학·동물의학연구소, Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University) | Suk Kim
  • 이후장(경상대학교 수의과대학·동물의학연구소, Institute of Animal Medicine, College of Veterinary Medicine, Gyeongsang National University) | Hu-Jang Lee Correspondence to