...
首页> 外文期刊>Fresenius environmental bulletin >A NEW MATCHING ALGORITHM IN LC-MS REPLICATES SPECTRUM SIGNAL PROCESSING USED FOR ORGANIC COMPOUND MONITORING AREA
【24h】

A NEW MATCHING ALGORITHM IN LC-MS REPLICATES SPECTRUM SIGNAL PROCESSING USED FOR ORGANIC COMPOUND MONITORING AREA

机译:LC-MS中的一种新的匹配算法替代了用于有机化合物监测区域的光谱信号处理

获取原文

摘要

Liquid chromatography-mass spectrometry (LC-MS) can separate the organic components of samples, which is widely used for qualitative and quantitative analysis of pesticide and veterinary drug residues, environmental pollutants and allelochemi- cals in soil, water and air science research. It also an effective tool in the determination of non-volatile compounds, polar compounds, thermo-unstable compounds and macromolecular weight compounds including proteins, polypeptides, polymers, etc. In this paper, a new matching algorithm was proposed to solve the problems of low accuracy and coverage of peptide in LC-MS replicates spectrum. At present, the key is to match the LC peaks and analyze the differences when peptide signals are detected. Generally, most algorithms are based on time warping functions. However, the difference of elution time between replicate spectrum is randomly generated. Besides time feature, the isotope feature is introduced in this paper for building a classification model under the hypothesis that the same peptide obeys same isotope distribution in repeated experiments. This isotope classification model was generated and tested by training and testing peptide signal which was detected by LC-MS/MS. At last, the accuracy of this new model was over 95%, while 90% coverage rate showed that the isotope classification model was efficient.
机译:液相色谱-质谱法(LC-MS)可以分离样品中的有机成分,广泛用于土壤,水和空气科学研究中农药和兽药残留,环境污染物和化肥的定性和定量分析。它也是测定非挥发性化合物,极性化合物,热不稳定化合物和大分子化合物(包括蛋白质,多肽,聚合物等)的有效工具。本文提出了一种新的匹配算法来解决低分子量的问题。 LC-MS中肽段的准确度和覆盖范围可复制质谱图。目前,关键是要匹配LC峰并分析检测到肽信号时的差异。通常,大多数算法都基于时间扭曲函数。但是,重复谱图之间洗脱时间的差异是随机产生的。除了时间特征外,本文还介绍了同位素特征,以在重复实验中相同肽服从相同同位素分布的假设下建立分类模型。通过训练和测试由LC-MS / MS检测到的肽信号来生成和测试该同位素分类模型。最后,该新模型的准确率超过95%,覆盖率达到90%,表明同位素分类模型是有效的。

著录项

相似文献

  • 外文文献
  • 中文文献
  • 专利
获取原文

客服邮箱:kefu@zhangqiaokeyan.com

京公网安备:11010802029741号 ICP备案号:京ICP备15016152号-6 六维联合信息科技 (北京) 有限公司©版权所有
  • 客服微信

  • 服务号