Comparative Investigation for Rotten Xylem (kuqin) and Strip Types (tiaoqin) of Scutellaria baicalensis Georgi Based on Fingerprinting and Chemical Pattern Recognition

Abstract

Scutellaria baicalensis Georgi (SBG) is not just as a traditional herbal medicine but also a popular functional food in China and other Asian countries. A sensitive simple strategy was developed for the first time to analyze SBG from eight different geographical sources using high-performance liquid chromatography (HPLC) coupled with multivariate chemometric methods. Two unsupervised pattern recognition models, hierarchical cluster analysis (HCA) and principal components analysis (PCA), and a supervised pattern recognition model, partial least squares discriminant analysis (PLS-DA), were used to analyze the chemical compositions and physical traits of SBG. The important chemical markers baicalin, baicalein, and wogonoside were analyzed quantitatively and with PLS-DA. These methods distinguished rotten xylem (kuqin) and strip types (tiaoqin) of SBG and found that the thickness of the slice had a significant impact on the classification of SBG. Two classes of strip types were identified: one as the uncut pharmaceutical, which was sectioned with a thickness >3 mm; the other as a thin-sectioned strip type, with a thickness of <2 mm. This fingerprinting technique coupled to a chemometric analysis was used for the simultaneous quantitation of three components (chemical markers) of SBG, and greatly simplified the complicated identification of the multiple components of this plant relative to traditional methods. The strategy can clearly distinguish between kuqin and tiaoqin of SBG, and suggests that the thickness of the slice can be used as the basis for evaluation of SBG. These data provide a theoretical basis and scientific evidence for the development and utilization of SBG.

Quantitative Comparison and Chemical Profile Analysis of Different Medicinal Parts of Perilla frutescens (L.) Britt. from Different Varieties and Harvest Periods

“…Chromatographic data from 18 batches (S1–S18), collected from UPLC-Q-TOF-MS/MS, were imported into R software and Simca-P 14.1 for multivariate statistical analysis. HCA and PCA are the unsupervised pattern recognition methods mainly used for groups of samples that have not yet been clearly classified . The samples can be categorized intuitively based on the characteristics of their variables.…”

Correlation between Quality and Geographical Origins of Cortex Periplocae, Based on the Qualitative and Quantitative Determination of Chemical Markers Combined with Chemical Pattern Recognition

“…Chromatographic data from 14 batches, collected from UPLC-Q-TOF-MS/MS, were imported into R software and Simca-P 14.1 for multivariate statistical analysis. HCA is mainly used for groups of samples that have not yet been clearly classified [22]. The samples can be categorized intuitively based on the characteristics of their variables.…”

Raman Spectroscopy Study of Phosphorites Combined with PCA-HCA and OPLS-DA Models

“…PCA is an unsupervised model which simplifies the interpretation of variables between samples by dimensionality reduction [25][26][27][28][29][30]. The dimensionality reduction of spectral data by unsupervised PCA not only reflects most of the original spectral information, but also reflects the difference between the sample points [25][26][27][28]. HCA is a grouping structure method which defines sample data by dendrogram [25,26,29].…”