Quality Evaluation of Phellodendri Chinensis Cortex by Fingerprint⁻Chemical Pattern Recognition

Abstract

Phellodendri Chinensis Cortex (PCC) and Phellodendri Amurensis Cortex (PAC) are increasingly being used as traditional herbal medicines, but they are often mistaken for each other. In this study, the fingerprints of PCC from six different geographical sources were obtained by high-performance liquid chromatography, and multivariate chemometric methods were used for comprehensive analysis. Two unsupervised pattern recognition models (principal component analysis and hierarchical cluster analysis) and a supervised pattern recognition model (partial least squares discriminant analysis) were established on the basis of the chemical composition and physical traits of PCC and PAC. PCC and PAC were found to be distinguishable by these methods. The PCC category was divisible into two categories, one with more crude cork and a maximum thickness of ~1.5 mm, and the other with less net crude cork and a maximum thickness of 0.5 mm. According to the model established by partial least squares discriminant analysis (PLS-DA), the important chemical marker berberine hydrochloride was obtained and analyzed quantitatively. From these results combined with chemometric and content analyses, the preliminary classification standards for phellodendron were established as three grades: superior, first-order and mixed. Compared with the traditional identification methods of thin layer chromatography identification and microscopic identification, our method for quality evaluation is relatively simple. It provides a basis and reference for identification of PCC and enables establishment of grade standards. It also could be applied in quality control for compound preparations containing PCC.

Keywords: Phellodendri Amurensis Cortex (PAC); Phellodendri Chinensis Cortex (PCC); chemical pattern recognition; fingerprint; quality evaluation.

Figure 1

HPLC chromatographic fingerprints of PCC from six different geographical regions.

Figure 2

HCA of PCC and PAC (S1: PCC-more crude cork and a maximum thickness of ~1.5 mm; S2: PCC-less net crude cork and a maximum thickness of 0.5 mm; S3: PAC).

Figure 3

2D PCA score plot (PC1 versus PC2) of PCC and PAC samples, as listed in Table 1 (S1: PCC-more crude cork and a maximum thickness of ~1.5 mm; S2: PCC-less net crude cork and a maximum thickness of 0.5 mm; S3: PAC).

Figure 4

3D PLS-DA score plot (PC1 versus PC2) of PCC and PAC samples, as listed in Table 1 (S1: PCC-more crude cork and a maximum thickness of ~1.5 mm; S2: PCC-less net crude cork and a maximum thickness of 0.5 mm; S3: PAC).

Figure 5

VIP plot of PLS-DA.

Figure 6

Unknown sample prediction by PLS-DA. (S1: PCC-more crude cork and a maximum thickness of ~1.5 mm; S2: PCC-less net crude cork and a maximum thickness of 0.5 mm; S3: PAC.) (a) Training set; (b) prediction set.