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基于投影寻踪回归法的顶空固相微萃取-气相色谱-质谱联用技术对藏红花香气成分的定量结构-保留关系研究

Quantitative Structure-Retention relationship study of the constituents of saffron aroma in SPME-GC-MS based on the projection pursuit regression method.

作者信息

Du Hongying, Wang Jie, Hu Zhide, Yao Xiaojun

机构信息

Department of Chemistry, Lanzhou University, Lanzhou 730000, China.

出版信息

Talanta. 2008 Oct 19;77(1):360-5. doi: 10.1016/j.talanta.2008.06.038. Epub 2008 Jul 4.

Abstract

Quantitative Structure-Retention Relationship (QSRR) studies were performed for predicting the retention times of 43 constituents of saffron aroma, which were analyzed by solid-phase micro-extraction gas chromatography-mass spectrometry (SPME-GC-MS). The chemical descriptors were calculated from the molecular structures of the constituents of saffron aroma alone, and the linear and non-linear QSRR models were constructed using the Best Multi-Linear Regression (BMLR) and Projection Pursuit Regression (PPR) methods. The predicted results of the two approaches were in agreement with the experimental data. The coefficients of determination (R(2)) of the linear BMLR model were 0.9434 and 0.8725 for the training and test sets, respectively. The other non-linear PPR model gave a more accurate prediction with R(2) values of 0.9806 (training set) and 0.9456 (test set). The proposed models could also identify and provide some insights into structural features that may play a role on the retention behaviors of the constituents of saffron aroma in the SPME-GC-MS system. This study affords a simple but efficient approach for studying the retention behaviors of other similar plants and herbs.

摘要

进行了定量结构-保留关系(QSRR)研究,以预测藏红花香气中43种成分的保留时间,这些成分通过固相微萃取气相色谱-质谱联用(SPME-GC-MS)进行分析。化学描述符仅根据藏红花香气成分的分子结构计算得出,并使用最佳多元线性回归(BMLR)和投影寻踪回归(PPR)方法构建了线性和非线性QSRR模型。两种方法的预测结果与实验数据一致。线性BMLR模型的训练集和测试集的决定系数(R(2))分别为0.9434和0.8725。另一个非线性PPR模型给出了更准确的预测,训练集和测试集的R(2)值分别为0.9806和0.9456。所提出的模型还可以识别并提供一些关于结构特征的见解,这些特征可能对藏红花香气成分在SPME-GC-MS系统中的保留行为起作用。这项研究为研究其他类似植物和草药的保留行为提供了一种简单而有效的方法。

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