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ABSTRACT
A strategy for monitoring and analyzing the chemical stability of Xuebijing injection (XBJ) by multiwavelength chromatographic fingerprints and multivariate classification techniques is presented in this paper. Multiwavelength chromatographic fingerprints were constructed using chromatographic data obtained at four wavelengths (260, 280, 320, and 400 nm). The raw chromatography data were preprocessed by noise reduction, baseline correction, data normalization, and interval correlation optimized shifting (icoshift). Using this method, fingerprints of 166 samples of XBJ subjected to different forced degradation conditions (irradiation, high temperature, and a range of pH values) were properly represented. Forty-one chemical components were identified using the iPeak program. In addition, the identified peak area profiling of chemical components were used for multivariate classification analysis. Principal component analysis (PCA) and Ward’s method were used to classify different XBJ degradation samples. The PCA score plot showed that XBJ degradation samples were clustered into four groups, and the results are confirmed by Ward’s method. Ten key chemical markers under different degradation conditions were found and identified by counterpropagation artificial neural networks (CP-ANN), statistical t-tests, and UPLC-Q-TOF-MS. The results suggest that the proposed strategy could be successfully applied to the comprehensive analysis of complex chemical systems.
GRAPHICAL ABSTRACT
Acknowledgments
The authors would like to thank Honglei Wang for his language support.
