Multivariate statistical analysis of two-dimensional NMR data to differentiate grapevine cultivars and clones

Forveffle, L

doi:10.1016/0308-8146(95)00220-0

Cited by 46 publications

(27 citation statements)

References 14 publications

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“…In addition to GC/MS and LC/MS, another technique for the analysis of complex mixtures, nuclear magnetic resonance (NMR), has gained popularity in the food industry. NMR in combination with principal components analysis (PCA) has been applied to the metabolomic profiling of wine (Brescia et al 2002), coffee (Charlton et al 2002), orange juice (Vogels et al 1996), apple juice (Belton et al 1998;Del Campo et al 2005), beer (Duarte et al 2002), as well as for the differentiation of plant species and cultivars (Forveille et al 1996;Choi et al 2005). …”

Section: Introductionmentioning

confidence: 99%

Characterization of the biochemical variability of bovine milk using metabolomics

et al. 2009

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Bovine milk contains a complex mixture of metabolites, which were identified by liquid and gas chromatography mass spectrometry methods. With the aid of new software methods, spectral assignment was carried out, enabling the identification of 223 metabolites. Those included amino acids, lipids, carbohydrates, nucleotides, energy metabolites, vitamins, cofactors and short peptides. Metabolite concentrations were compared between 10 bovine milk varieties, differing in brand, fat content, expiry date, package type and farming method. Principal components analysis showed a clear separation of the different milk varieties. Whole milk could be distinguished from reduced fat and fat free milk by higher lipid metabolites like free fatty acids, cholesterol and 1,2-dipalmitoylglycerol. But also, the reduced fat varieties had lower levels of vitamin E. In comparing organic to conventional milk, 14 named metabolites were statistically different between the two farming methods. This shows the potential of identifying farming-method-specific biomarkers upon analysis and validation of a larger sample size. Finally, high biochemical variability was shown in conventional whole milk derived from different producers. The distinct biochemical profiles of milk varieties shows the utility of metabolic profiling for authentication of milk varieties, and for deriving potential markers that can serve as signatures for a particular milk.

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Section: Introductionmentioning

confidence: 99%

Characterization of the biochemical variability of bovine milk using metabolomics

et al. 2009

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“…Although these factors are essential to the wine quality, the grape variety is the most basic and important factor for making good quality wine. Grape-growers are aware that selection of grape cultivars that are the most suitable for each region and that have the highest potential quality is important in order to improve the final quality of wine (Forveille, Vercauteren, & Rutledge, 1996).…”

Section: Introductionmentioning

confidence: 99%

Characterization of wines from grape varieties through multivariate statistical analysis of 1H NMR spectroscopic data

Son

Hwang

Ahn

et al. 2009

Food Research International

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“…This study will therefore evaluate nuclear magnetic resonance (NMR) spectroscopy, which is a powerful tool for the simultaneous identification and quantification of compounds in complex mixtures (Le Gall and Colquhoun 2003). Requiring little sample preparation, NMR allowed the direct and reliable determination of analytes in different food matrices, including beer (Lachenmeier et al 2005), juices (Belton et al 1998), grapes (Forveille et al 1996), infant formulas (Lachenmeier et al 2009), or pine nuts (Köbler et al 2011). To the best of our knowledge, there have been only two papers published that give an insight into the potential of 1 H and 13 C NMR spectroscopy for quantitative analysis of lactose in conventional lactose-containing milk (Hu et al 2007;Klein et al 2010).…”

Section: Introductionmentioning

confidence: 97%

NMR spectroscopy as a screening tool to validate nutrition labeling of milk, lactose-free milk, and milk substitutes based on soy and grains

Monakhova

Kuballa

Leitz

et al. 2011

Dairy Science & Technol.

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Lactose-free milk and milk substitutes based on soy, oat, or rice are widely marketed to lactose-intolerant consumers. In this study, 400 MHz 1 H nuclear magnetic resonance (NMR) spectroscopy was used in the context of food surveillance to validate the "lactose-free" claims labeled on these beverages. Using soft independent modeling of class analogy (SIMCA) analysis, a qualitative classification according to the type of beverage (lactose-containing milk, lactose-free milk, oat, soy, and rice milk substitutes) was possible. Furthermore, quantitative data regarding nutrition labeling parameters were predicted from the same spectra using partial least squares (PLS) regression. The models obtained for carbohydrate, sugars, protein, fat, saturates, and energy (R 2 =0.89-0.97) were suitable for a screening analysis. Using nicotinamide as an internal standard, quantitative determination of lactose with a detection limit of 0.03 g.L -1 was also possible using direct integration of the signals (linear range, 0.05-50.0 g.L −1 , R>0.999). The relative standard deviations for the lactose-free milks were below 10%. NMR spectroscopy was judged to be suitable for the rapid routine analysis of milk and milk substitutes.

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Multivariate statistical analysis of two-dimensional NMR data to differentiate grapevine cultivars and clones

Cited by 46 publications

References 14 publications

Characterization of the biochemical variability of bovine milk using metabolomics

Characterization of the biochemical variability of bovine milk using metabolomics

Characterization of wines from grape varieties through multivariate statistical analysis of 1H NMR spectroscopic data

NMR spectroscopy as a screening tool to validate nutrition labeling of milk, lactose-free milk, and milk substitutes based on soy and grains

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