中文摘要：

建立了一种基于气相色谱-质谱技术（GC-MS）的化学指纹图谱,以发现当归及其不同炮制品潜在标志物的方法。利用GC-MS获得当归及其不同炮制品挥发油化学指纹图谱,对产生的75样本×259变量数据进行归一化、修正80%规则和数据缩放等方法预处理,通过正交校正偏最小二乘法（OPLS）模式识别方法对样品进行模式识别,根据模型的变量重要性因子（VIP）和非参数检验结果筛选出12个潜在标志物。经相关分析和结构鉴定,其中11个化合物分别被鉴定为丁内酯、萜烯醇、6-丁基-1,4环庚二烯、2-壬酮、6-十一烷酮、2-甲氧基苯酚、δ-榄香烯、4,5,6,7-四甲基苯酞、Z-丁烯基酞内酯、亚油酸甲酯、1,7-异丙基-4-甲基-1,4,5,6,7,7a-六氢-2H-茚-2-酮。

英文摘要：

A GC-MS technique for identifying potential biomarkers in volatile oils extracted by five different processes from Angelica has been developed.The chemical fingerprints of GC-MS have been discovered in volatile extracted oils from Angelica.Seventy-five variable data pretreatment includes data normalization and scaling,corrected 80% rule and dataset division.According to fingerprint chromatogram,Radix Angelicae Sinensis and their different processed products were separated by orthogonal partial least square（OPLS）.Twelve potential biomarkers screening was performed according to VIP value and significant test.The eleven of the biomarkers were identified as butyrolactone,terpinen-4-ol,6-butyl-1,4-cycloheptadiene,2-nonanone,6-undecanone,δ-elemene,2-methoxy-4-vinylphenol,4,5,6,7-teramethyl phthalide,1.7-isopropyl-4-methyl-1,4,5,6,7,7a-hexahydro-2H-inden-2-one,Methylinoleate,Z-butylidene phthalide.