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

Abstract

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 broad 1H 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.

Quality assessment and chemical diversity of Australian propolis from Apis mellifera bees

“…Recently 1 H NMR has been receiving considerable attention for metabolic fingerprinting analysis to test food quality, origin, manufacture, or authenticity due to its high reproducibility, unbiased structural information production and possibility of detecting fraudulent compounds in a sample 71 . This non-destructive technique has been utilised to evaluate and identify possible adulterations in coffee 72 , 73 , honey 74 , 75 , milk and dairy products 76 – 78 , liquor 79 – 82 , oil 83 , 84 and juice 85 , 86 .…”

“…The 1 H NMR spectral data of both high-grade and low-grade propolis samples were further processed and analysed by chemometric methodologies using untargeted multivariate statistical principal component analysis (PCA) and partial least squares—discriminant analysis (PLS-DA) to understand chemical similarity or differences according to the geographical regions the samples were collected. The PCA explains in an unsupervised way the variance of each dataset when increasing the number of principal components without referring to any class label while the PLS-DA extracts the information that can predict all possible class memberships from linear combinations of original NMR bins with the use of multivariate regression techniques and assess all class discriminations 71 . The PCA score plots of high-grade propolis did not show any separate grouping among six datasets with a 95% confidence level (Fig.…”

Foodomics: Analytical Opportunities and Challenges

“…Despite the restricted sensitivity of NMR-based methods, the combination of highly reproducible, noninvasive, rapid, and simple-use proton nuclear magnetic resonance ( 1 H-NMR) with multivariate statistical analysis in Foodomics applications has emerged over the last decades for the implementation of models to trace the food quality. 92 Along this line, a combination of NMR spectroscopy and chemometrics was proposed by Cavallini et al to characterize beer. 93 The authors compared two multivariate approaches: the full spectra analysis and the analysis of the chemical features extracted by multivariate curve resolution.…”

“…The resolved information is comparable with the full spectrum information, but interpretability is greatly improved. Another NMR-based Foodomics approach was proposed by Herbert-Pucheta et al to discriminate between wine samples produced from the same Cabernet Sauvignon variety fermented with different yeast strains . A double pulsed field-gradient-echo (DPFGE) NMR methodology was applied in this work as a selective refocusing method of the aromatic frequency (5.5–10 ppm) of the wine samples fermented, which allowed one to discriminate between yeast strains used for the controls and for large-scale alcohol reductions after supervised standard and sparse PLS-DA multivariate statistical treatments.…”

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].…”