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ARS Home » Northeast Area » Beltsville, Maryland (BHNRC) » Beltsville Human Nutrition Research Center » Food Composition and Methods Development Laboratory » Research » Publications at this Location » Publication #316994

Research Project: Metabolite Profiling and Chemical Fingerprinting Methods for Characterization of Foods, Botanical Supplements, and Biological Materials

Location: Food Composition and Methods Development Laboratory

Title: FlavonQ: An Automated Data Processing Tool for Profiling Flavone/flavonol Glycosides Using Ultra High-performance Liquid Chromatography Diode Array Detection and High-Resolution Accurate-Mass Mass Spectrometry (UHPLC HRAM-MS)

Author
item Zhang, Mengliang - Ohio University
item Sun, Jianghao
item Chen, Pei

Submitted to: Analytical Chemist
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 9/11/2015
Publication Date: 10/6/2015
Citation: Zhang, M., Sun, J., Chen, P. 2015. FlavonQ: An Automated Data Processing Tool for Profiling Flavone/flavonol Glycosides Using Ultra High-performance Liquid Chromatography Diode Array Detection and High-Resolution Accurate-Mass Mass Spectrometry (UHPLC HRAM-MS). Analytical Chemist. 87:9974-9981.

Interpretive Summary:

Technical Abstract: Flavonoids are well-known for their health benefits and can be found in nearly every plant. There are more than 5,000 known flavonoids existing in foods. Profiling flavonoids in natural products poses great challenges due to the diversity of flavonoids, the lack of commercially available standards, and the complexity of plant matrices. The ultra high-performance liquid chromatography diode array detection high-resolution accurate-mass mass spectrometry (UHPLC HRAM-MS) is one of the most important technique for the analysis of flavonoids, however, data-mining of the UHPLC HRAM-MS data is a very daunting, labor-intensive, and expertise-dependent process. An automated data processing tool that could transfer field-acquired expertise into data analysis will be very valuable for flavonoids research. FlavonQ is being developed as such an "expert system" for automated data analysis of flavone/flavonol glycosides, two important subclasses of flavonoids. FlavonQ is capable of automatic data format conversion, peak detection, flavone/flavonol glycosides peaks extraction, flavone and flavonol glycosides identification, and semi-quantitation. A new strategy was proposed in this study for tentative identification and semi-quantitation of flavone/flavonol glycosides using UHPLC HRAM-MSn and FlavonQ. The flavone/flavonol glycosides chromatographic peaks were firstly extracted from DAD chromatograms by FlavonQ based on their characteristic UV absorbance, filtered by similarity analysis, and then tentatively identified and semi-quantified. This approach was applied to the analysis of flavone/flavonol glycosides in 9 different plants with minimal user input. The data analysis process by FlavonQ was less than 1 minute per sample and both the quantitative and qualitative goals were achieved. Manual verification indicated that over 90% of flavone/flavonol glycosides in each plant were tentatively identified and semi-quantified correctly by the approach. The program is designed in a modular manner and allows substitution or addition of supplementary processing steps for the analysis of other classes of compounds. With FlavonQ, the time needed to perform flavone/flavonol glycosides data analysis can be significantly reduced (hours with human verification) as compared to days or weeks needed with manual data-mining. This project developed an expert system that used the latest chromatographic and MS technology to systematically determine flavone/flavonol glycosides in plant materials. The composition information accrued by the FlavonQ is the infrastructure necessary to evaluate the health effect of these plant compounds, which can ultimately be used to establish dietary recommendations.