Improved lipid mixtures profiling by 1H NMR using reference lineshape adjustment and deconvolution techniques

Abstract

Analysisof one-dimensional 1H NMR spectra of complex mixtures, such as lipids from natural extracts, is hampered by the small spectral width leading to a great number of overlapped signals. Additional complications including lineshape broadening and distortion may occur due to magnetic field inhomogeneity. Quantitation of such spectra is therefore challenging. We present in this work a quantitation approach based on deconvolution after correction of spectra by means of reference lineshape adjustment (RLA), also known as reference deconvolution. Spectral fit and precision obtained on deconvoluted peaks were used as indicators to iteratively improve the deconvolution process. This approach was tested on 1H NMR spectra of olive oil samples and allowed extraction of 77 peaks (available as peak intensities or areas), whereas spectral integration afforded 5 variables when only well-resolved signals were considered and 29 variables when a bucket around each discernible peak was integrated. Deconvoluted peak intensities and areas were obtained with improved precision after RLA of raw spectra. The use of these spectral variables as predictors in multivariate statistical analysis enhanced the classification of olive oil samples according to the altitude of the olive field or to the color of the olive drupes. The same variables allowed quantitation of oleic, palmitoleic, and vaccenic acids within triacylglycerols, which was not possible by 1H NMR, and improved quantitation of linoleic and linolenic acids. These results proved the high potential of the presented approach in the characterization and authentication of complex mixtures by 1H NMR spectroscopy.