Current trends in ŒNO-NMR based metabolomics

¹ Departamento de Química Orgánica, Escuela Nacional de Ciencias Biológicas, Instituto Politécnico Nacional, Prolongación de Carpio y Plan de Ayala s/n, Colonia Santo Tomás, 11340 Ciudad de México
² Consejo Mexicano Vitivinícola A.C. Montecito No 38 Piso 15 Despacho 22 - WTC México, C.P. 03810, Ciudad de México
³ Casa Madero, Carretera Paila 102- Parras Km. 18.2; Hacienda San Lorenzo. Parras de la Fuente, C.P. 27980 Coahuila, Mexico
⁴ Bodegas de Santo Tomás, Km 49 Carretera Federal No. 1 Ensenada – La Paz, Ensenada Baja California, Mexico
⁵ Secretaria de Agricultura y Desarrollo Rural, Municipio Libre No. 377, Colonia Santa Cruz Atoyac, CP 03310 Alcaldía Benito Juárez, Ciudad de México, Mexico

Abstract

Present work discusses strengths and limitations of two Nuclear Magnetic Resonance outliers obtained with a water-to-ethanol solvent multi pre saturation acquisition method, recently included in the Compendium of International Methods of Analysis of Wines and Musts, published as OIV-MA-AS316-01, and their accuracy for metabolomics analysis. Furthermore, it is also presented an alternative to produce more discriminant and sensitive NMR data matrices for metabolomics studies, comprising the use of a novel NMR acquisition strategy in wines, the double pulsed-field gradient echo (DPFGE) NMR scheme, with a refocusing band-selective uniform-response pure-phase selective pulse, for a selective excitation of the 5-10 ppm chemical shift range of wine samples, that reveals novel broad aromatic ¹H resonances, directly associated to complex polyphenols. Both aromatics and full binned OIV-MA-AS316-01,as well as the selective 5-10 ppm DPFGE NMR outliers were statistically analyzed with diverse non-supervised Principal Component Analysis (PCA) and supervised Partial Least Squares -Discriminant Analysis (PLS-DA), sparse (sPLS-DA) least squares- discriminant analysis, and orthogonal projections to latent structures discriminant analysis (OPLS-DA). Supervised multivariate statistical analysis of DPFGE and aromatics’ binned OIV-MA-AS316-01NMR data have shown their robustness to broadly discriminate geographical origins and narrowly differentiate between different fermentation schemes of wines from identical variety and region.