A novel strategy for quantitative analysis of the formulated complex system using chromatographic fingerprints combined with some chemometric techniques

The contribution ratio of various characteristic tea compounds in antioxidant capacity by DPPH assay

Tea is a worldwide health beneficial beverage for its antioxidant ability. 1,1-diphenyl-2-picryl-hydrazyl (DPPH) assay is a common method to measure the antioxidant capacity of tea compounds, yet the contribution ratio of various characteristic tea compounds is still unclear. high-performance liquid chromatography (HPLC) was used to examine the content of polyphenol compounds in 24 tea samples from four tea categories (green tea, white tea, oolong tea, and black tea). Based on the results of DPPH and HPLC, contribution ratio of each tea compound was analyzed by the Pearson correlation analysis and the partial least squares regression (PLSR). The Pearson correlation analysis showed that the order of correlation between the area of 13 peaks and the antioxidant ability of tea samples was x8 > x6 > x3 > x13 > x10 > x7 > x2 > x5 > x11 > x9 > x4 > x12 > x1; the regression equation fit by PLSR was Y = 47.258 - 0.760x1 + 0.287x2 - 1.484x3 - 0.569x4 + 0.674x5 + 2.257x6 + 1.698x7 + 1.389x8 - 0.188x9 + 0.467x10 + 0.297x11 + 1.314x12 + 0.963x13. We identified nine common peaks by reference standard substances: the x3 was gallic acid (GA), x4 was theobromine, x5 was catechuic acid (CA), x6 was epigallocatechin (EGC), x8 was epigallocatechin gallate (EGCG), x9 was caffeine, x10 was epicatechin (EC), x12 was epicatechin gallate (ECG), and x13 was gallocatechin gallate (GCG). Based on the study of spectrum-effect correlation, we obtain a better understanding of the antioxidant activity of complex tea polyphenols component. PRACTICAL APPLICATIONS: Identify the contribution of specific chemical compound to antioxidant activity by the coefficients in PLSR equation and provide a deeper insight into the joint effect of multiple ingredients of tea. Further, we can infer the DPPH free radical scavenging ability of a new kind of tea by the PLSR equation without chemical detection.

Advances in Synergistic Combinations of Chinese Herbal Medicine for the Treatment of Cancer

The complex pathology of cancer development requires correspondingly complex treatments. The traditional application of individual single-target drugs fails to sufficiently treat cancer with durable therapeutic effects and tolerable adverse events. Therefore, synergistic combinations of drugs represent a promising way to enhance efficacy, overcome toxicity and optimize safety. Chinese Herbal Medicines (CHMs) have long been used as such synergistic combinations. Therefore, we summarized the synergistic combinations of CHMs used in the treatment of cancer and their roles in chemotherapy in terms of enhancing efficacy, reducing side effects, immune modulation, as well as abrogating drug resistance. Our conclusions support the development of further science-based holistic modalities for cancer care.

Rapid identification of the quality decoction pieces by partial least squares -based pattern recognition: grade classification of the decoction pieces of Saposhnikovia divaricata

Herbal medicines are commonly used in many countries after they undergo processing. Quality decoction pieces are a guarantee of the efficacy and safety of the herbal medical products. Here, a strategy based on chemical analysis combined with chemometric techniques was proposed for the classification and prediction of the different grades of the decoction pieces. Considering the necessity for a shared and simple method for the grade classification for the public, in this paper, the characterization of the chemical constituents was determined by utilizing high-performance liquid chromatography (HPLC)/diode array detection. HPLC was first established for the characterization of the chemical constituents of the different grade decoction pieces. Furthermore, a simultaneous quantification of several of the marker compounds in these decoction pieces was obtained. Finally, a partial least squares-based pattern recognition method was utilized to obtain a predictive model for the grade classification of the decoction pieces. Saposhnikovia divaricata (Turcz.) Schischk was used as a case study. The partial least squares -based pattern recognition for the grade classification of the decoction pieces of S. divaricata demonstrated good sensitivity, specificity and prediction performance, which may efficiently validate the identification results of appearance assessment. The proposed strategy is expected to provide a new insight for the grade classification and quality control of the decoction pieces. Copyright © 2016 John Wiley & Sons, Ltd.