Sichuan Provincial Key Laboratory of Quality and Innovation Research of Chinese Materia Medica, Sichuan Academy of Chinese Medicine Sciences, Chengdu 610041, PR China.
A⁎STAR-Chemopower Molecular Info-Tech Joint Lab, Singapore.
J Pharm Biomed Anal. 2022 Sep 5;218:114854. doi: 10.1016/j.jpba.2022.114854. Epub 2022 May 24.
Volatile oil, as an important bioactive fraction of medicinal herbs, is comprised of a diversity of compounds. At present, gas chromatography-mass spectrometry (GC-MS) is one of the mainstream approaches to profiling these complex components. However, GC-MS faces the major bottleneck in data analysis, such as co-elution of more than one compound, and interference caused by high background noise; this usually makes an operator have to spend a lot of time and effort in optimizing experimental conditions. Taking Chuanxiong Rhizoma (the dry rhizome of Ligusticum chuanxiong Hort., abbreviated as "CR") as an example, this study is intended to provide a feasible, quick and cost-effective solution for compound identification based on the chemometric method of entropy minimization (EM) algorithm. Ten batches of geo-authentic CR and eight batches of adulterants including Fuxiong (FX), Shanchuanxiong (SCX) and Cnidii Rhizoma (CNR) were determined by headspace GC-MS. FX and SCX were rhizomes of L. chuanxiong but subjected to improper harvest time. CNR was the dried rhizome of Cnidium officinale Makino. The co-eluting and overlapping peaks and low-concentration peaks with high background were precisely reconstructed by EM algorithm, and then the reconstructed pure mass spectra of each component were compared with the ion fragment information in NIST library for qualitative identification. EM algorithm proves to be capable of delivering results with increased accuracy and high confidence. Moreover, by the GC-MS approach established in this work, the volatile chemical profiles of FX, SCX, and CNR, were quite distinct from those of geo-authentic CR, suggesting that the adulterants should not be confused with CR in clinical practice and pharmaceutical industry. In brief, the advanced EM algorithm is envisioned to be applied to a variety of medicinal herbs, enabling rapid and accurate identification of volatile phytochemicals.
挥发油作为草药的重要生物活性成分,由多种化合物组成。目前,气相色谱-质谱联用(GC-MS)是分析这些复杂成分的主流方法之一。然而,GC-MS 在数据分析方面面临着主要的瓶颈,例如一种以上化合物的共洗脱以及高背景噪声引起的干扰;这通常使得操作人员不得不花费大量的时间和精力来优化实验条件。以川芎(伞形科藁本属植物川芎的干燥根茎,简称“CR”)为例,本研究旨在提供一种可行、快速且具有成本效益的基于信息最小熵算法(EM)的化合物鉴定方法。采用顶空 GC-MS 对 10 批地道川芎和 8 批包括抚芎(FX)、山川芎(SCX)和蛇床子(CNR)在内的掺伪品进行了测定。FX 和 SCX 是川芎的根茎,但收获时间不当。CNR 是蛇床子的干燥根茎。通过 EM 算法准确重建共洗脱和重叠峰以及高背景下的低浓度峰,然后将每个成分的重建纯质谱与 NIST 库中的离子碎片信息进行比较,进行定性鉴定。EM 算法被证明能够提供更准确和高可信度的结果。此外,通过本工作建立的 GC-MS 方法,FX、SCX 和 CNR 的挥发性化学成分特征与地道川芎的特征明显不同,提示在临床实践和制药行业中不应将掺伪品与 CR 混淆。总之,先进的 EM 算法有望应用于各种草药,实现快速准确地识别挥发性植物化学物质。