基于UHPLC-IM-Q-TOF-MS的黄柏和关黄柏生物碱类成分分析

Characterization and identification of alkaloids in Phellodendri Chinensis Cortex and Phellodendri Amurensis Cortex based on UHPLC-IM-Q-TOF-MS

该文基于UHPLC-IM-Q-TOF-MS建立了结合碰撞截面积(collision cross section,CCS)预测区间和定量结构保留关系(quantitative structure-retention relationship,QSRR)模型的异喹啉和喹啉类生物碱鉴定策略,并将其应用于黄柏和关黄柏的化学成分分析。该策略主要包括3个部分:①通过分子特征提取算法提取原始分子特征离子;②通过建立的异喹啉和喹啉类生物碱的CCSvs质荷比(m/z)过滤区间,对原始分子特征离子进行过滤,筛选出潜在的异喹啉和喹啉类生物碱③根据QSRR模型预测的候选化合物的保留时间,结合二级质谱特征碎片离子以及裂解规律对化学成分进行鉴定。基于该策略,从黄柏和关黄柏中共推测鉴定80个成分,经对照品比对确证15个。通过结合CCS筛选和QSRR模型预测候选化合物的色谱保留行为,可提高中药小分子结构类似物的鉴定效率。

A strategy combining collision cross section(CCS)prediction and quantitative structure-retention relationship(QSRR)model for quinoline and isoquinoline alkaloids was established based on UHPLC-IM-Q-TOF-MS and applied to Phellodendri Chinensis Cortex and Phellodendri Amurensis Cortex.The strategy included the following three steps.The molecular features were extracted by the"find features"algorithm.②The potential quinoline and isoquinoline alkaloids were screened by filtering the original characteristic ions extracted from Phellodendri Chinensis Cortex and Phellodendri Amurensis Cortex by the established CCS vs m/z prediction interval.③According to the retention time of candidate compounds predicted by QSRR model,the chemical constituents were identified in combination with the characteristic fragment ions and pyrolysis law of secondary mass spectrometry.With the strategy,a total of 80 compounds were predicted,and 15 were identified accurately.The strategy is effective for the identification of small analogs of traditional Chinese medicine.