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18 October 2022 Spectral fingerprinting based on HPLC-DAD and chemical pattern recognition for quality evaluation of Polygonatum sibiricum from different areas
Ting Chen, Liang Zhang, Bin Huang
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Proceedings Volume 12349, International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022); 123490Q (2022) https://doi.org/10.1117/12.2657115
Event: International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022), 2022, Zhengzhou, China
Abstract
Based on High Performance Liquid Chromatography-Diode Array Detection (HPLC-DAD) and chemical pattern recognition, this study constructed 12 batches of polygonatum sibiricum spectral fingerprinting from different areas and sources. Pattern recognition techniques such as similarity evaluation, Principal Component Analysis (PCA) and Orthogonal Partial Least Squares Discriminant Analysis (OPLS-DA) were used in the research process to screen out the characteristic components as Quality Markers (Q-Markers) of polygonatum sibiricum. A total of 16 common peaks were found in spectral fingerprinting, with similarity evaluation in the range of 0.686 to 0.918. In the PCA analysis, it was found that the three principal components (PC1, PC2 and PC3) characterized the variation information (35.04%, 25.15% and 14.56%) of the original variables, and the cumulative contribution value reached 74.75%. OPLS-DA analysis was used to screen out 6 characteristic components, which are considered potential Q-Markers of polygonatum sibiricum. In this study, the characteristic components were selected as Q-Markers, which provided a reference for the improvement of the quality standard of polygonatum sibiricum.
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Ting Chen, Liang Zhang, and Bin Huang "Spectral fingerprinting based on HPLC-DAD and chemical pattern recognition for quality evaluation of Polygonatum sibiricum from different areas", Proc. SPIE 12349, International Conference on Agri-Photonics and Smart Agricultural Sensing Technologies (ICASAST 2022), 123490Q (18 October 2022); https://doi.org/10.1117/12.2657115
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KEYWORDS
Principal component analysis
Pattern recognition
Chemical analysis
Analytical research
Detector arrays
Quality systems
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