Resolution of GC-MS data of complex PAC mixtures and regression modeling of mutagenicity by PLS
Resolution of GC-MS data of complex PAC mixtures and regression modeling of mutagenicity by PLS
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DOI：
10.1021/es000154e
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发表时间：
2001-06-01
影响因子：
11.4
通讯作者：
Kvalheim, O
中科院分区：
文献类型：
--
作者：
Eide, I;Neverdal, G;Kvalheim, O
目前的工作描述了一种策略，通过气相色谱-质谱[混合物的 GC-MSI 模式，每个混合物平均含有 260 种化合物]来预测非常复杂的多环芳香族化合物 (PAC) 混合物的致突变性。该混合物是废气颗粒的 13 种有机提取物，通过全扫描 GC-MS 进行了表征。通过自动曲线解析程序将数据解析为各个化合物的峰和光谱。使用 0.8 的相似性指数评估 4 分钟时间间隔内出现的峰的光谱之间的相似性，以确定所有样品中均由相同的变量名称（保留时间）代表相同的化合物。对解析的色谱图进行积分，得到大小为 13 x 721 的预测矩阵，将其用作多元回归模型的输入。使用潜在结构的偏最小二乘投影 (PLS) 将 GC-MS 色谱图与艾姆斯沙门氏菌测定中测量的致突变性相关联。最佳模型（高 r(2) 和 Q(2)）是通过 52 个变量获得的。这些变量与观察到的致突变性共变，并且随后可以进行化学鉴定。此外，回归模型还可用于根据其他有机提取物的 GC-MS 色谱图预测致突变性。
The present work describes a strategy to predict the mutagenicity of very complex mixtures of polycyclic a romatic compounds (PAC) from gas chromatography-mass spectrometry [GC-MSI patterns of the mixtures, each containing 260 compounds on,average. The mixtures, 13 organic extracts of exhaust particles, were characterized by full scan GC-MS. The data were resolved into peaks and spectra for individual compounds by an automated curve resolution Procedure. Similarity between spectra was evaluated for peaks that appeared within a time interval of 4 min, using a similarity index of 0.8 to ascertain that the same compound was represented: by the same variable name (retention time) in all samples. The resolved chromatograms were integrated, resulting in a predictor matrix of size 13 x 721, which was used as input to a multivariate regression model. Partial least-squares projections to latent structures (PLS) were used to correlate the GC-MS chromatograms to mutagenicity as measured in the Ames Salmonella assay. The best model (high r(2) and Q(2)) was obtained with 52 variables. These variables covary with: the observed mutagenicity, and may subsequently be identified chemically. Furthermore, the regression model can be used to predict mutagenicity from GC-MS chromatograms of other organic extracts.