Lower limit of detection achieved by raw band-target entropy minimization (rBTEM) for trace and coeluted gas chromatography-mass spectrometry components

The Chemistry of Green and Roasted Coffee by Selectable 1D/2D Gas Chromatography Mass Spectrometry with Spectral Deconvolution

Gas chromatography/mass spectrometry (GC/MS) is a long-standing technique for the analysis of volatile organic compounds (VOCs). When coupled with the Ion Analytics software, GC/MS provides unmatched selectivity in the analysis of complex mixtures and it reduces the reliance on high-resolution chromatography to obtain clean mass spectra. Here, we present an application of spectral deconvolution, with mass spectral subtraction, to identify a wide array of VOCs in green and roasted coffees. Automated sequential, two-dimensional GC-GC/MS of a roasted coffee sample produced the retention index and spectrum of 750 compounds. These initial analytes served as targets for subsequent coffee analysis by GC/MS. The workflow resulted in the quantitation of 511 compounds detected in two different green and roasted coffees. Of these, over 100 compounds serve as candidate differentiators of coffee quality, AAA vs. AA, as designated by the Coopedota cooperative in Costa Rica. Of these, 72 compounds survive the roasting process and can be used to discriminate green coffee quality after roasting.

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 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.