4.7 Article

Chemistry and biological properties of berry volatiles by two-dimensional chromatography, fluorescence and Fourier transform infrared spectroscopy techniques

Journal

FOOD RESEARCH INTERNATIONAL
Volume 83, Issue -, Pages 74-86

Publisher

ELSEVIER
DOI: 10.1016/j.foodres.2016.02.017

Keywords

Volatile substances; Bioactivity; Three-dimensional fluorescence; Two-dimensional gas chromatography with time-of-flight mass spectrometry; Binding properties; Fourier transform infrared spectroscopy

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In this study, three-dimensional fluorescence spectroscopy in combination with ultraviolet visible (UV-Vis) absorption spectroscopy, Fourier transform infrared spectroscopy (FTIR) and two-dimensional chromatography techniques were employed to investigate the main compounds in gooseberries, blueberries and cranberries. The determination of the terpenes (the main group of secondary metabolites) in the three berries was done by headspace solid-phase microextraction coupled with comprehensive two-dimensional gas chromatography with time-of-flight mass spectrometry (HS-SPME/GC x GC-TOFMS). Main volatiles were assigned in each of the three berries' chromatograms. The compounds were organized in different groups: monoterpene hydrocarbons and monoterpene oxygen-containing compounds (oxides, alcohols, aldehydes, and ketones). The highest amount of alcohol and ester compounds (85%) was estimated in blueberry; carboxylic acids, ketones and aldehydes were found in cranberry (62%) and terpenes in cape gooseberry (8%). Human serum albumin (HSA) has been used as a model protein to study drug-protein interaction. Specific binding of polyphenols from berries to HSA under the physiological conditions was a result of the formation of a polyphenol-HSA complex. The berries' extracts interact with HSA before and after incubation with different binding affinities which are related to their antioxidant properties. The effect of the complexation on the secondary protein structure was verified in the changes of amide bands. Principal component analysis (PCA) was applied to discriminate the differences among the samples' compositions. (C) 2016 Elsevier Ltd. All rights reserved.

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