Toxicological evaluation of complex mixtures by pattern recognition: correlating chemical fingerprints to mutagenicity.
Toxicological evaluation of complex mixtures by pattern recognition: correlating chemical fingerprints to mutagenicity.
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DOI：
10.1289/ehp.02110s6985
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发表时间：
2002-12
影响因子：
10.4
通讯作者：
Kvalheim OM
中科院分区：
文献类型：
--
作者：
Eide I;Neverdal G;Thorvaldsen B;Grung B;Kvalheim OM
我们描述了使用模式识别和多元回归评估复杂的混合物相关的化学指纹的致突变性的混合物。混合物是20种废气颗粒的有机提取物，每种含有102-170种单独的化合物，如多环芳烃（PAH），硝基-PAH，氧-PAH和饱和烃。通过全扫描GC-MS（气相色谱-质谱）表征混合物。通过自动化曲线解析程序将数据解析为单个化合物的峰和光谱。对解析的色谱图进行积分，得到预测矩阵，其用作主成分分析的输入以评估混合物之间的相似性（即，分类）。此外，使用对潜在结构的偏最小二乘投影将GC-MS数据与致突变性相关联，如在艾姆斯沙门氏菌试验中测量的（即，校准）。最佳模型（高r2和Q2）确定了与观察到的致突变性共变的变量。这些变量随后可能会被更详细地确定。此外，该回归模型可用于预测其他有机提取物的GC-MS色谱图的致突变性。我们强调，化学指纹以及详细的数据组成可以用于模式识别。
We describe the use of pattern recognition and multivariate regression in the assessment of complex mixtures by correlating chemical fingerprints to the mutagenicity of the mixtures. Mixtures were 20 organic extracts of exhaust particles, each containing 102-170 individual compounds such as polycyclic aromatic hydrocarbons (PAHs), nitro-PAHs, oxy-PAHs, and saturated hydrocarbons. Mixtures were characterized by full-scan GC-MS (gas chromatography-mass spectrometry). Data were resolved into peaks and spectra for individual compounds by an automated curve resolution procedure. Resolved chromatograms were integrated, resulting in a predictor matrix that was used as input to a principal component analysis to evaluate similarities between mixtures (i.e., classification). Furthermore, partial least-squares projections to latent structures were used to correlate the GC-MS data to mutagenicity, as measured in the Ames Salmonella assay (i.e., calibration). The best model (high r2 and Q2) identifies the variables that co-vary with the observed mutagenicity. These variables may subsequently be identified in more detail. Furthermore, the regression model can be used to predict mutagenicity from GC-MS chromatograms of other organic extracts. We emphasize that both chemical fingerprints as well as detailed data on composition can be used in pattern recognition.
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影响因子：
6.1
作者：
Bostrom, E;Engen, S;Eide, I
通讯作者：
Eide, I
影响因子：
6.2
作者：
Shen, HL;Grung, B;Eide, I
通讯作者：
Eide, I
影响因子：
11.4
作者：
Eide, I;Neverdal, G;Kvalheim, O
通讯作者：
Kvalheim, O
DOI：
10.1016/0169-7439(92)80092-i
发表时间：
1992-04-01
影响因子：
3.9
作者：
KETTANEHWOLD, N
通讯作者：
KETTANEHWOLD, N
影响因子：
7.4
作者：
SCHUETZLE, D;LEWTAS, J
通讯作者：
LEWTAS, J