基于GC-MS指纹图谱和LASSO-PLS-DA区分2个不同产地的石菖蒲

摘要

本文旨在研究中国四川和安徽2个产地石菖蒲样品的化学差异.首先通过气相色谱-质谱联用(GC-MS)技术建立石菖蒲的定量化学指纹图谱,基于色谱、质谱信息和保留指数定性和定量了石菖蒲中104种挥发性化合物.在此基础上,采用一种稀疏正则化方法来提高偏最小二乘-判别分析(PLS-DA)模型的分类能力,使得分类精度从82.76%上升到96.55%.最后,结合最小绝对收缩与选择算子(LASSO)与二次采样筛选出区别于2个产地的3个化学标记物:β-榄香烯,α-芹菜素和α-细辛醚.本文采用的最小绝对收缩与选择算子-偏最小二乘-判别分析(LASSO-PLS-DA)算法可以作为筛选中草药中标志性化学成分和进行地理草药学研究的有效方法.%This study is intended to explore the chemical differences of Acori Tatarinowii Rhizoma (ATR) samples collected from two habitats, Sichuan and Anhui provinces, China. Gas chromatography-mass spectrometry (GC-MS) was applied to establishing the quantitative chemical fingerprints of ATRs. A total of 104 volatile compounds were identified and quantified with the information of mass spectra and retention index (RI). Furthermore, least absolute shrinkage and selection operator (LASSO), a sparse regularization method, combined with subsampling was employed to improve the classification ability of partial least squares-discriminant analysis (PLS-DA). After variable selection by LASSO, three chemical markers, β-elemene, α-selinene and α-asarone, were identified for the discrimination of ATRs from two habitats, and the total classification correct rate was increased from 82.76% to 96.55%. The proposed LASSO-PLS-DA method can serve as an efficient strategy for screening marked chemical components and geo-herbalism research of traditional Chinese medicines.