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Development of a Comprehensive Flavonoid Analysis Computational Tool for Ultrahigh-Performance Liquid Chromatography-Diode Array Detection-High-Resolution Accurate Mass-Mass Spectrometry Data.

Zhang M, et al. Anal Chem. 2017.


Liquid chromatography and mass spectrometry methods, especially ultrahigh-performance liquid chromatography coupled with diode array detection and high-resolution accurate-mass multistage mass spectrometry (UHPLC-DAD-HRAM/MSn), have become the tool-of-the-trade for profiling flavonoids in foods. However, manually processing acquired UHPLC-DAD-HRAM/MSn data for flavonoid analysis is very challenging and highly expertise-dependent due to the complexities of the chemical structures of the flavonoids and the food matrixes. A computational expert data analysis program, FlavonQ-2.0v, has been developed to facilitate this process. The program first uses UV-vis spectra for an initial stepwise classification of flavonoids into classes and then identifies individual flavonoids in each class based on their mass spectra. Step-wise identification of flavonoid classes is based on a UV-vis spectral library compiled from 146 flavonoid reference standards and a novel chemometric model that uses stepwise strategy and projected distance resolution (PDR) method. Further identification of the flavonoids in each class is based on an in-house database that contains 5686 flavonoids analyzed in-house or previously reported in the literature. Quantitation is based on the UV-vis spectra. The stepwise classification strategy to identify classes significantly improved the performance of the program and resulted in more accurate and reliable classification results. The program was validated by analyzing data from a variety of samples, including mixed flavonoid standards, blueberry, mizuna, purple mustard, red cabbage, and red mustard green. Accuracies of identification for all samples were above 88%. FlavonQ-2.0v greatly facilitates the identification and quantitation of flavonoids from UHPLC-HRAM-MSn data. It saves time and resources and allows less experienced people to analyze the data.


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