Profiling complex volatile components by HS-GC-MS and entropy minimization software: An example on Ligusticum chuanxiong Hort

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 GCMS. 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.

Automated summary

This study developed a rapid and accurate compound identification solution based on the chemometric method of entropy minimization algorithm. Using headspace GC-MS, the volatile chemical profiles of Chuanxiong Rhizoma (CR) and its adulterants were analyzed. The EM algorithm was proven to deliver accurate and high-confidence results, and the chemical profiles of the adulterants were distinct from that of the authentic CR.