According to the Chinese Pharmacopoeia (2005 edition), identification and quantification of chemical markers are crucial to the quality control of herbal medicines. A total of 525 quantitative assays of chemical markers were documented in the Chinese Pharmacopoeia (2005 edition) for assessment of herbal medicinal materials, plant lipids, herbal extracts and formulations . Chemistry of these markers is known and their analytical procedures and reference standards are available for quality control. Chau et al. used near infrared spectroscopy (NIR) to quantitatively determine the content of berberine and total alkaloid in Cortex Phellodendri (Guanhuangbo) . The content of berberine determined by high-performance liquid chromatography-diode array detection (HPLC-DAD) was used as a critical parameter to confirm the accuracy of the data obtained from NIR according to a linear model of partial least squares (PLS) regression. In another study, high-performance liquid chromatography (HPLC) and HPLC-DAD were used to assess the quality consistency of a formulated Chinese medicine Qingfu Guanjie Shu (capsule) using four marker compounds, namely sinomenine, paeoniflorin, paeonol and curcumin. . Lin et al. used liquid chromatography-tandem mass spectrometers (LC-MS/MS), solid phase extraction, and the marker glycyrrhetic acid to simultaneously validate Radix Glycyrrhizae (Gancao) and quantify the target compound in the samples . Quantitative studies of markers and identification of active ingredients were carried out for the quality control of herbal medicines [31–34]. Gas chromatography (GC), gas chromatography-mass spectroscopy (GC-MS), thin layer chromatography (TLC), thin layer chromatography-ultraviolet spectrophotometry (TLC-UV), capillary electrophoresis (CE) and capillary zone electrophoresis (CZE) were also proposed for the quality control of herbal medicines [35–39].
Compared with the marker approach, the multi-compound approach uses multiple compounds with known chemical properties and does not necessarily require chemical markers. Chemometric deconvolution and resolution are major methods in this approach. In the Chinese Pharmacopoeia (2005 edition) , multiple compounds, instead of a single compound, are recommended for the quality control of herbal medicines. For example, total flavonol glycosides (i.e. quercetin, isorhamnetin and kaempferol) as well as total terpene lactones (i.e. bilobalide, and ginkgolides A, B and C) were used for the quality control of a ginkgo leaf product . However, analyzing multiple compounds in a single chromatogram may not be easy. These chromatograms often contain overlapping peaks, which may not be resolved by changing chromatographic conditions. One possible solution is the use of chemical and/or instrumental methods that take advantage of spectra with very close retention times, e.g. mass spectra, ultraviolet spectra or other chemical properties containing variations large enough to resolve overlapping chromatographic profiles [40–43].
Chemometric resolution methods (CRM) were used extensively in the past decades to 'purify' chromatographic peak profiles of complex mixture systems such as herbal medicines . The qualitative and quantitative chemical information obtained by CRM did help discover the active ingredients of herbal medicines and study the synergistic effects of the ingredients .
Previously, both iterative and non-iterative resolution methods were used to study the volatile and non-volatile components in herbal medicines [44–46]. Many non-iterative resolution methods such as heuristic evolving latent projection (HELP), alternative moving window factor analysis (AMWFA), (subwindow factor analysis) SFA, evolving window orthogonal projection (EWOP) were useful in discovering more than ten components of herbal medicines [47–51]. Using GC-MS coupled with HELP, Li et al. identified 38 volatile chemical components of Radix Paeoniae Rubra (Chishao), which accounted for 95.21% of all detectable components . In another study, 69 components of Radix Rehmanniae Preparata (Shudihuang) were separated, of which 59 were identified using standard spectra in the database of the National Institute of Standards and Technology (NIST). The 26 identified methyl esters accounted for about 94.29% of the total number of components . Most of the iterative methods including (orthogonal projection approach) OPA and (iterative orthogonal projection resolution) IOP were applied to determine the chemical composition of herbal medicines [45, 46]. With these chemometric methods, 65 volatile chemical constituents of Rhizoma et Radix Notopterygii (Qianghuo) were identified out of the 98 separated chemicals. Qi et al. resolved the overlapping chromatographic peaks in Resina Draconis (Xuejie) using HPLC-DAD. Therefore, using chemometric methods with hyphenated instruments was powerful in the analysis of herbal medicines . Zeng et al. used PCA and generalized rank annihilation factor analysis (GRAFA) to process HPLC-DAD data sets obtained from Radix Salviae Miltiorrhizae (Danshen) and Radix Notoginseng (Sanqi) . Ye et al. simultaneously analyzed seven major saponins of Danshen Diwan with HPLC-DAD and electron spray ionization-mass spectrometry (ESI-MS) .
Chemometric methods including spectral correlative chromatography (SCC), multi-component spectral correlative chromatography (MSCC), AMWFA were proposed for comparing three-dimensional (3D) or two-dimensional (2D) chromatographic profiles and integrating presence or absence information [49, 57–59]. SCC was used to compare pure or selective herbal medicine components . Both MSCC and AMWFA were used to analyze complex herbal medicines. The main feature of MSCC is the construction of an orthogonal projection matrix using abstract spectra acquired from decomposition of original fingerprint data sets. For comparison, the pure and mixed spectra are projected to the matrix for presence or absence information of target components. AMWFA was used to extract pure chromatographic and spectral profiles of common components of related herbal medicines [49, 58, 59]. All these new chemometric algorithms were applied in the identification, quantification, comparison of chemical components and quality control of herbal medicines [60–66].