TY - JOUR
T1 - 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
AU - Herbert-Pucheta, José Enrique
AU - Lozada-Ramírez, José Daniel
AU - Ortega-Regules, Ana E.
AU - Hernández, Luis Ricardo
AU - de Parrodi, Cecilia Anaya
N1 - Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.
PY - 2021/7/2
Y1 - 2021/7/2
N2 - The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad1H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.
AB - The quality of foods has led researchers to use various analytical methods to determine the amounts of principal food constituents; some of them are the NMR techniques with a multivariate statistical analysis (NMR-MSA). The present work introduces a set of NMR-MSA novelties. First, the use of a double pulsed-field-gradient echo (DPFGE) experiment with a refocusing band-selective uniform response pure-phase selective pulse for the selective excitation of a 5–10-ppm range of wine samples reveals novel broad1H resonances. Second, an NMR-MSA foodomics approach to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains proposed for large-scale alcohol reductions. Third a comparative study between a nonsupervised Principal Component Analysis (PCA), supervised standard partial (PLS-DA), and sparse (sPLS-DA) least squares discriminant analysis, as well as orthogonal projections to a latent structures discriminant analysis (OPLS-DA), for obtaining holistic fingerprints. The MSA discriminated between different Cabernet Sauvignon fermentation schemes and juice varieties (apple, apricot, and orange) or juice authentications (puree, nectar, concentrated, and commercial juice fruit drinks). The new pulse sequence DPFGE demonstrated an enhanced sensitivity in the aromatic zone of wine samples, allowing a better application of different unsupervised and supervised multivariate statistical analysis approaches.
KW - Cabernet Sauvignon
KW - Candida zemplinina
KW - H-NMR
KW - Juices
KW - Multivariate statistical analysis
KW - NMR pulse sequence
KW - Saccharomyces Bayanus ex uvarum
KW - Wine
UR - http://www.scopus.com/inward/record.url?scp=85110648976&partnerID=8YFLogxK
U2 - 10.3390/molecules26144146
DO - 10.3390/molecules26144146
M3 - Artículo
C2 - 34299421
AN - SCOPUS:85110648976
SN - 1420-3049
VL - 26
JO - Molecules
JF - Molecules
IS - 14
M1 - 4146
ER -