Fast and high-resolution stereochemical analysis by nonuniform sampling and covariance processing of anisotropic
Olivier Lafon1, Bingwen Hu, Jean-Paul Amoureux
1Unité de Catalyse et de Chimie du Solide, UMR-CNRS 8181, Université de Lille Nord de France, Université de Lille 1, École Nationale Supérieure de Chimie de Lille (ENSCL), Villeneuve d'Ascq, France. olivier.lafon@ensc-lille.fr
Abstract:
Natural abundance deuterium (NAD) 2D NMR spectroscopy using chiral or achiral liquid crystals is an efficient analytical tool for the stereochemical analysis of enantio- or diastereomers by the virtue of proton-to-deuterium substitution. In particular, it allows the measurement of enantiopurity of organic synthetic molecules or the determination of the natural isotopic (1)H/(2)H fractionation in biological molecules, such as fatty acid methyl esters (FAME). So far, the NAD 2D spectra of solutes were acquired by using uniform sampling (US) and processed by conventional 2D Fourier transform (FT), which could result in long measurement times for medium-sized analytes or low solute concentrations. Herein, we demonstrate that this conventional approach can be advantageously replaced by nonuniform sampling (NUS) processed by covariance (Cov) transform. This original spectral reconstruction provides a significant enhancement of spectral resolution, as well as a reduction of measurement times. The application of Cov to NUS data has required the introduction of a regularization procedure in the time domain for the indirect dimension. The analytical potential of combining Cov and NUS is demonstrated by measuring the enantiomeric excess of a scalemic mixture of 2-ethyloxirane and by determining the diastereomeric excess of methyl vernoleate, a natural FAME. These two organic compounds were aligned in a polypeptide (poly(γ-benzyl-L-glutamate)) mesophase. In the case of NAD 2D NMR spectroscopy, we show that Cov and NUS methods allow a decrease in measurement time by a factor of two compared with Cov applied to US data and a factor of four compared with FT applied to US data.
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