Chemometric Analysis for Identification of Botanical Raw Materials for Pharmaceutical Use: A Case Study Using Panax notoginseng

Abstract

The overall control of the quality of botanical drugs starts from the botanical raw material, continues through preparation of the botanical drug substance and culminates with the botanical drug product. Chromatographic and spectroscopic fingerprinting has been widely used as a tool for the quality control of herbal/botanical medicines. However, discussions are still on-going on whether a single technique provides adequate information to control the quality of botanical drugs. In this study, high performance liquid chromatography (HPLC), ultra performance liquid chromatography (UPLC), capillary electrophoresis (CE) and near infrared spectroscopy (NIR) were used to generate fingerprints of different plant parts of Panax notoginseng. The power of these chromatographic and spectroscopic techniques to evaluate the identity of botanical raw materials were further compared and investigated in light of the capability to distinguishing different parts of Panax notoginseng. Principal component analysis (PCA) and clustering results showed that samples were classified better when UPLC- and HPLC-based fingerprints were employed, which suggested that UPLC- and HPLC-based fingerprinting are superior to CE- and NIR-based fingerprinting. The UPLC- and HPLC- based fingerprinting with PCA were able to correctly distinguish between samples sourced from rhizomes and main root. Using chemometrics and its ability to distinguish between different plant parts could be a powerful tool to help assure the identity and quality of the botanical raw materials and to support the safety and efficacy of the botanical drug products.

Phytochemical fingerprint and chemometrics for natural food preparation pattern recognition: an innovative technique in food supplement quality control

“…However, since these chromatograms are highly complex and contain many classes of compounds, the comparison, often highly qualitative, can lead to missed features or unnecessarily tight requirements. The use of chemometric techniques to analyze the HPLC data could provide a higher level of assurance that important characteristics are not overlooked, and provide consistency in the final botanical drug products (Zhu et al 2014). For this reason, in this study, the quantitative data obtained by HPLC were further processed by PCA; the score plot shows the distribution of the samples along the PCs, while the loading plot shows the contribution of each variable to the PCs, which is known to be influenced by the angle between them: if the angle of the variable with a PC is closer to 0, the contribution of the variable to this component is strong.…”

“…However, in some cases, the limited information provided by conventional fingerprint may not be enough to reveal the quality characteristics of some extremely complex herbal products, comprehensively (Peng et al 2011): although it is possible to visually differentiate the different chromatograms, however, the process is subjective and not quantitative. In addition, the fingerprint chromatograms are complex multivariate data sets due to the complexity of herbal medicines, so minor differences between very similar chromatograms might be missed (Zhu et al 2014). When the compositions of the herbal medicine are too complex, e.g.…”

Induction of stress volatiles and changes in essential oil content and composition upon microwave exposure in the aromatic plant Ocimum basilicum

“…Also, the peaks are often overlapping or embedded, even when the chromatographic/spectral conditions are optimized (Wang et al, 2010). Chemometric approaches can be applied as a powerful method for characterizing botanical drugs of different origin and quality (Zhu et al, 2014). For pattern recognition, statistical approaches commonly applied are: principal component analysis (PCA), hierarchical cluster analysis (HCA), linear discriminant analysis (LDA), k-nearest neighbor (k-NN), and partial least squares-discrimination analysis (PLS-DA).…”

Determination and Identification of a Specific Marker Compound for Discriminating Shrub Chaste Tree Fruit from Agnus Castus Fruit Based on LC/MS Metabolic Analysis

“…Recently, phytochemical studies have been reported on the classification of plant samples and determining their marker compounds with differences involving geographical factors, species, and processing treatments. [10][11][12][13][14] From the LC/MS data set of four SCTF and four ACF samples, a total 6508 peaks were picked up using MarkerLynx software and subjected to OPLS-DA to identify compounds that could be used to discriminate SCTF from ACF. The OPLS-DA model explained 95.3% of the total variance (R 2 X) with a 94.3% prediction goodness parameter (Q 2 ).…”