Comparative NMR Metabolomics Profiling between Mexican Ancestral & Artisanal Mezcals and Industrialized Wines to Discriminate Geographical Origins, Agave Species or Grape Varieties and Manufacturing Processes as a Function of Their Quality Attributes

Abstract

The oenological industry has benefited from the use of Nuclear Magnetic Resonance (1H-NMR) spectroscopy in combination with Multivariate Statistical Analysis (MSA) as a foodomics tool for retrieving discriminant features related to geographical origins, grape varieties, and further quality controls. Said omics methods have gained such attention that Intergovernmental Organizations and Control Agencies are currently recommending their massive use amongst countries as quality compliances for tracking standard and degradation parameters, fermentation products, polyphenols, amino acids, geographical origins, appellations d’origine contrôlée and type of monovarietal strains in wines. This study presents, for the first time, a 1H-NMR/MSA profiling of industrial Mexican wines, finding excellent statistical features to discriminate between oenological regions and grape varieties with supervised Orthogonal Projections to Latent Structures Discriminant Analysis (OPLS-DA). In a comparative way, it is applied with the 1H-NMR/OPLS-DA workflow for the first time in ancestral and artisanal Mexican mezcals with promising results to discriminate between regions, agave species and manufacturing processes. The central aim of this comparative study is to extrapolate the know-how of wine-omics into the non-professionalized mezcal industry for establishing the NMR acquisition, preprocessing and statistical analysis basis to implement novel, non-invasive and highly reproducible regional, agave species and manufacturing-quality controls.

Nuclear Magnetic Resonance Metabolomics with Double Pulsed-Field-Gradient Echo and Automatized Solvent Suppression Spectroscopy for Multivariate Data Matrix Applied in Novel Wine and Juice Discriminant Analysis

“…The Sparse Partial Least Squares–Discriminant Analysis (sPLS-DA) is a special case of PLS-DA for data selection and classification in a one-step procedure, whereas the algorithm is used to effectively reduce an important number of NMR bins, within the original high-dimensional data, for producing robust and easy-to-interpret discriminant models [ 51 ]. The Orthogonal Projections to Latent Structures-Discriminant Analysis (OPLS-DA) permits obtaining optimal information from the dataset by identifying a more refined multivariate subspace for maximum group separations by applying Monte-Carlo Cross Validations with a set of partitions per number of permutations [ 32 ]. Due to the capacities for distinguishing between subtle variations in NMR datasets that are relevant for keen identifications of spectral features to drive group separations further, OPLS-DA tends to produce less complex discriminant models, with more accurate dimension reductions and more reliable than PLS-DA models [ 52 ].…”

“…Despite the restricted Limits of Quantification (LOQ) and Detection (LOD), 1 H-NMR has been a useful method for the geographical origin authentication of several food matrices, with clear advantages of being nondestructive, fast, reproducible, and reliable, compared to chromatography coupled with MS techniques [ 19 , 20 , 29 , 30 , 31 ]. The combination of high-reproducible, noninvasive, rapid, and simple-use proton Nuclear Magnetic Resonance Spectroscopy ( 1 H-NMR) with Multivariate Statistical Analysis (MSA) for foodstuff metabolomics has emerged over the last decades for the implementation of models to trace the food quality, origin, manufacture, or authenticity [ 32 ].…”

“…To study nonprofessionalized traditional spirits such as Mexican mezcal, we have recently implemented NMR foodomics to profile the metabolite composition of both ancestral and artisanal products [ 32 ]. This work has been possible to discriminate the geographical origin of mezcals from the Oaxaca, Puebla, and San Luis Potosí Mexican regions, from the spectral fingerprint generated by NMR data matrices that reflect the presence of eleven discriminant metabolites in different concentrations.…”

Foodomics: Analytical Opportunities and Challenges

“…To a lesser extent than HR-MS technology, 1 H NMR-based metabolomics has emerged recently as a valuable technology to trace the origin, manufacture, or authenticity of food products. 111 Despite its relatively low sensitivity, this technology is highly reproducible, rapid, and no preliminary sample separation is required, and it has been implemented to screen the metabolic profile of the blue mussel (Mytilus edulis) and the Manila clam (Ruditapes phlippinarum), 112 to discriminate the geographical origins of agave species and grape varieties, 113 to determine the metabolomic profiling of acerola clones at different ripening stages, 114 to evaluate the rheological characteristics of sponge cake after in vitro digestion, 115 or to investigate milk fermentation during yoghurt production when different heat treatments of milk and starter cultures are employed. 116 In all of these studies, advanced statistical tools played a critical role for compound identification and the discovery of significant metabolites.…”

Current trends in ŒNO-NMR based metabolomics

“…Such holistic methods related to food integrity control can produce broad or narrow analytical signatures, depending on the capacity of the technique to respectively produce low-or high-resolution fingerprints. Typically, broad analytical fingerprints are obtained with portable instrumentation such as UV-VIS, Raman or NIR approaches, whilst narrow fingerprints for elucidating complex chemical diversities are obtained by means of coupled chromatography with mass spectrometry, and more recently with nuclear magnetic resonance spectroscopy [4,5]. In alcoholic beverages, broad and narrow analytical fingerprints have been used to respectively trace brand and generic authentication, like for instance, to differentiate rums manufactured from sugar cane juice from those made from sugar cane molasses [6], and for the assessment of both quality and authenticity of Scotch Whisky, based on non-targeted fingerprinting approach using gas chromatography coupled to tandem high-resolution mass spectrometry followed by multidimensional chemometric processing [7].…”