A comprehensive characterization of C-glycosyl flavones in wheat germ has been conducted using multi-stage high resolution mass spectrometry (HRMSn) in combination with a mass defect filtering (MDF) technique. MDF performed the initial search of raw data with defined C-glycosyl flavone mass windows and mass defect windows to generate the noise-reduced data focusing on targeted flavonoids. The high specificity of the exact mass measurement permits the unambiguous discrimination of acyl groups (nominal masses of 146, 162 and 176.) from sugar moieties (rhamnose, glucose or galactose and glucuronic acid). A total of 72 flavone C-glycosyl derivatives, including 2 mono-C-glycosides, 34 di-C-glycosides, 14 acyl di-C-glycosides and 7 acyl tri-C-glycosides, were characterized in wheat germ, some of which were considered to be important marker compounds for differentiation of whole grain and refined wheat products. The 7 acylated mono-O-glycosyl-di-C-glycosyl flavones and some acylated di-C-glycosyl flavones are reported in wheat for the first time. The frequent occurrence of numerous isomers is a remarkable feature of wheat germ flavones. Both UV and mass spectra are needed to maximize the structure information obtained for data interpretation.
Profiling flavonoids in natural products poses a great challenge due to the diversity of flavonoids, the lack of commercially available standards, and the complexity of plant matrixes. The increasingly popular use of ultra-high-performance liquid chromatography-diode array detection-high resolution accurate mass-mass spectrometry (UHPLC-HRAM-MS) for the analysis of flavonoids has provided more definitive information but also vastly increased amounts of data. Thus, mining of the UHPLC-HRAM-MS data is a very daunting, labor-intensive, and expertise-dependent process. An automated data processing tool, FlavonQ, was developed that can transfer field-acquired expertise into data analysis and facilitate flavonoid research. FlavonQ is an "expert system" designed for automated data analysis of flavone and flavonol glycosides, two important subclasses of flavonoids. FlavonQ is capable of data format conversion, peak detection, flavone and flavonol glycoside peak extraction, flavone and flavonol glycoside identification, and production of quantitative results. An expert system was applied to the determination of flavone and flavonol glycosides in nine different plants with an average execution time of less than 1 min. The results obtained by FlavonQ were in good agreement with those determined conventionally by a flavonoid expert.
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