田 鹏,张 庆,李艳红,于晓章.分散固相萃取-液相色谱-质谱法测定水稻叶片中的10种代谢物[J].食品安全质量检测学报,2022,13(11):3644-3651
分散固相萃取-液相色谱-质谱法测定水稻叶片中的10种代谢物
Determination of 10 kinds of metabolomics in rice shoot by dispersive solid phase extraction-liquid chromatography-mass spectrometry
投稿时间:2021-12-22  修订日期:2022-05-17
DOI:
中文关键词:  代谢物  分散固相萃取  水稻叶片  液相色谱-质谱法
英文关键词:metabolomics  dispersive solid phase, rice shoot  liquid chromatography-mass spectrometry
基金项目:国家自然科学基金项目(41877493)、广西高校中青年教师科研基础能力提升项目(2020KY06038)
作者单位
田 鹏 桂林理工大学环境科学与工程学院 
张 庆 桂林理工大学环境科学与工程学院 
李艳红 广西环境污染控制理论与技术重点实验室 
于晓章 广西环境污染控制理论与技术重点实验室 
AuthorInstitution
TIAN Peng School of Environmental Science and Engineering, Guilin University of Technology 
ZHANG Qing School of Environmental Science and Engineering, Guilin University of Technology 
LI Yan-Hong Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology 
YU Xiao-Zhang Guangxi Key Laboratory of Environmental Pollution Control Theory and Technology 
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中文摘要:
      目的 建立分散固相萃取-液相色谱-质谱法(liquid chromatography-mass spectrometry, LC-MS)测定水稻叶片中10种代谢物的分析方法。方法 水稻叶片样品经甲醇-乙腈-水溶液(2:2:1, V:V:V)提取, 无水硫酸钠盐析, 以乙二胺-N-丙基硅烷(primary secondary amine, PSA)和石墨化炭黑(graphitized carbon black, GCB)作为分散吸附剂进行净化, 最后进入液相色谱-质谱仪进行检测。结果 方法在12 min内完成了10种代谢物的定量分析。在3个添加水平(0.50、10.00、100.00 ng/L)下, 水稻叶片中10种代谢物的平均回收率为75.1%~122.2%, 相对标准偏差(relative standard deviations, RSDs)为3.8%~8.9% (n=6)。10种代谢物的标准工作曲线相关系数(r)均大于0.99879, 检出限(limits of detection, LODs)为0.016~0.039 ng/L。结论 本方法具有相对简便、节省时间、灵敏度较高等优势, 可以用于水稻叶片中代谢产物的定量检测。
英文摘要:
      Objective To establish a method for the determination of 10 kinds of metabolites in rice shoot by dispersive solid phase extraction-liquid chromatography-mass spectrometry (LC-MS). Methods Rice shoot samples were extracted by methanol-acetonitrile-aqueous solution (2:2:1, V:V:V), salted out by anhydrous sodium sulfate, ethylenediamine-N-propyl silane (PSA) and graphitized carbon black (GCB) were used as dispersive adsorbent for purification, and then detected by LC-MS. Results The quantitative analysis of 10 kinds of metabolites was completed within 12 min, the average recoveries of 10 kinds of metabolomics in rice shoot were 75.1%?122.2% at three spiked levels (0.50, 10.00, 100.00 ng/L), and the relative standard deviations (RSDs) were from 3.8% to 8.9% (n=6). The calibration curves of 10 kinds of metabolomics in rice shoot showed correlation coefficients (r) higher than 0.99879, and the limits of detection (LODs) were 0.016?0.039 ng/L. Conclusion The method is relatively simple, time-saving and high sensitivity, and is expected to be used for quantitative determination of metabolites in rice shoot.
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