提高气体染色学-质谱分辨率和纯分析物发现,使用染色体内化特征匹配的染色体内化特征
Caitlin N Cain1, Robert E Synovec1
1Department of Chemistry, University of Washington, Box 351700, Seattle, WA, 98195, USA.
一种名为mzCompare的新算法通过改进分析物识别和量化来增强气体染色学质谱学 (GC-MS) 分析,特别是对于分辨率差的化合物. 这种方法在复杂样本中协助多变量曲线分辨率-交替最小平方 (MCR-ALS).
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 频谱学是一种光谱学.
背景情况:
- 多变量曲线分辨率-交替最小平方 (MCR-ALS) 在GC-MS中用于分析物识别和量化.
- MCR-ALS与具有低染色分辨率 (Rs) 和高光谱污染的分析物进行斗争.
研究的目的:
- 介绍mzCompare,一种新的计算算法,用于提高GC-MS中的MCR-ALS性能.
- 改进分析物识别和定量,用于解决不良和光谱污染的化合物.
主要方法:
- 开发了mzCompare算法,利用对分析物的质量通道 (m/z) 中类似的保留时间和峰值形状.
- 为MCR-ALS生成纯化档案作为平等约束.
- 通过实验和模拟的GC-MS数据验证.
主要成果:
- mzCompare辅助的MCR-ALS在实验中发现了低至0.05的Rs的未解决的分析物.
- 在航空燃料中发现了335种分析物,比传统方法增加了44%.
- 在模拟中实现了优异的识别和定量准确性,在Rs ~ 0.02中,超过了标准的MCR-ALS.
结论:
- mzCompare显著改善了GC-MS中的分析物识别和量化,特别是在具有挑战性的条件下.
- 该算法克服了由低染色分辨率和光谱干扰引起的MCR-ALS的局限性.
- mzCompare为GC-MS中复杂混合物分析提供了强大的解决方案.
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