Biomolecular Measurement Division, National Institute of Standards and Technology , Gaithersburg, Maryland 20899-8362, United States.
Anal Chem. 2014 Oct 21;86(20):10231-8. doi: 10.1021/ac502379x. Epub 2014 Oct 1.
An analytical methodology has been developed for extracting recurrent unidentified spectra (RUS) from large GC/MS data sets. Spectra were first extracted from original data files by the Automated Mass Spectral Deconvolution and Identification System (AMDIS; Stein, S. E. J. Am. Soc. Mass Spectrom. 1999 , 10 , 770 - 781 ) using settings designed to minimize spurious spectra, followed by searching the NIST library with all unidentified spectra. The spectra that could not be identified were then filtered to remove poorly deconvoluted data and clustered. The results were assumed to be unidentified components. This was tested by requiring each unidentified spectrum to be found in two chromatographic columns with slightly different stationary phases. This methodology has been applied to a large set of pediatric urine samples. A library of spectra and retention indices for derivatized urine components, both identified and recurrent unidentified, has been created and is available for download.
已经开发出一种分析方法，用于从大型 GC/MS 数据集提取反复出现的未识别光谱（RUS）。首先使用旨在最大程度减少虚假光谱的设置，通过自动质谱解卷积和识别系统（AMDIS；Stein，S.E. J. Am. Soc. Mass Spectrom. 1999，10，770-781）从原始数据文件中提取光谱，然后用 NIST 库搜索所有未识别的光谱。然后过滤无法识别的光谱以去除解卷积不良的数据并进行聚类。假定这些结果是未识别的成分。通过要求每个未识别的光谱在两个具有略有不同固定相的色谱柱中找到来进行测试。该方法已应用于大量儿科尿液样本。已创建并可下载包含衍生尿液成分的光谱和保留指数的库，其中包括已识别和反复出现的未识别成分。
