Determination of metabolite profiles in tropical wines by <sup>1</sup>H NMR spectroscopy and chemometrics

Neto, Humberto Gomes da Silva; Silva, João B. P. da; Pereira, G. E.; Hallwass, Fernando

doi:10.1002/mrc.2520

Cited by 12 publications

(5 citation statements)

References 8 publications

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“…In a study of wines produced in the Bordeaux region in France, Pereira et al [51] showed that the NMR metabolic profile of wines was affected within a given wine cultivar by soil type (gravely, sandy, or clay), but vintage and cultivar effects were more dominant. Da Silva Neto et al [52] Some other agronomical factors that have been studied include the effect of grape maturity and health on wine quality. Chang et al [53] studied the effect of harvest time on the quality and metabolite composition of wines obtained from grapevine cv.…”

mentioning

confidence: 99%

NMR-based metabolomics in wine quality control and authentication

Amargianitaki

Spyros

2017

Chem. Biol. Technol. Agric.

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A comprehensive summary of research work related to applications of NMR spectroscopy in combination with multivariate statistical analysis techniques for the analysis, quality control, and authentication of wine is presented. NMR spectroscopy is used to obtain the non-volatile metabolic profile and/or phenolic profile of wines, with the help of 2D NMR spectroscopy. Metabolomics is then used as an analytical tool to investigate the variability of the metabolic profile of wines due to a series of different factors involved during wine production, including terroir, pedoclimatic conditions, vintage, vineyard practices, wine-making, barrel maturation, and aging. The discriminating power of the NMR-obtained wine metabolome is utilized as a wholistic analytical approach in efforts to authenticate wine with respect to important economic attributes, such as cultivar, vintage, and geographical origin.

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confidence: 99%

NMR-based metabolomics in wine quality control and authentication

Amargianitaki

Spyros

2017

Chem. Biol. Technol. Agric.

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“…Simple 1 H NMR methods provide a wealth of information about liquid samples, such as biomass hydrolysates, including both carbohydrate and hydrolysate by-product compositions. Mixture analysis of NMR spectra is well developed, with many software applications making analysis straightforward and reliable (da Silva Neto et al, 2009;Powers, 2009;Spraul et al, 2009;Da Silva et al, 2013). Traditionally, sugar analysis in biomass hydrolysates has been performed using integration of the anomeric proton region between 4.4 and 5.4 ppm against a reference standard (Kiemle et al, 2003;Mittal et al, 2009), but peak overlap occurs for several sugars and the large water peak at 4.8 ppm often makes integration of these peaks impossible.…”

Section: Nmr For Biomass Sugar and Fatty Acid Compositionmentioning

confidence: 99%

High Throughput Screening Technologies in Biomass Characterization

et al. 2018

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Biomass analysis is a slow and tedious process and not solely due to the long generation time for most plant species. Screening large numbers of plant variants for various geno-, pheno-, and chemo-types, whether naturally occurring or engineered in the lab, has multiple challenges. Plant cell walls are complex, heterogeneous networks that are difficult to deconstruct and analyze. Macroheterogeneity from tissue types, age, and environmental factors makes representative sampling a challenge and natural variability generates a significant range in data. Using high throughput (HTP) methodologies allows for large sample sets and replicates to be examined, narrowing in on more precise data for various analyses. This review provides a comprehensive survey of high throughput screening as applied to biomass characterization, from compositional analysis of cell walls by NIR, NMR, mass spectrometry, and wet chemistry to functional screening of changes in recalcitrance via HTP thermochemical pretreatment coupled to enzyme hydrolysis and microscale fermentation. The advancements and development of most high-throughput methods have been achieved through utilization of state-of-the art equipment and robotics, rapid detection methods, as well as reduction in sample size and preparation procedures. The computational analysis of the large amount of data generated using high throughput analytical techniques has recently become more sophisticated, faster and economically viable, enabling a more comprehensive understanding of biomass genomics, structure, composition, and properties. Therefore, methodology for analyzing large datasets generated by the various analytical techniques is also covered.

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“…Simple 1 H NMR screening methods are able to detect the sugar components in liquid hydrolysates, making this an attractive method for high throughput screening. There is a wealth of information published about mixture analysis with NMR in applications including drug discovery (Powers, 2009) and quality control of wine (da Silva Neto et al, 2009) and fruit juices (Spraul et al, 2009). In biomass hydrolysates, sugar concentrations have been determined with 1 H NMR by relying on the anomeric proton region between 4.4 and 5.4 ppm where the signals from the individual sugars are integrated against a known reference compound that has been added to the sample (Kiemle et al, 2004; Mittal et al, 2009).…”

Section: Introductionmentioning

confidence: 99%

Rapid determination of sugar content in biomass hydrolysates using nuclear magnetic resonance spectroscopy

Gjersing

Happs

Sykes

et al. 2012

Biotech & Bioengineering

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Large populations of potential cellulosic biomass feedstocks are currently being screened for fuel and chemical applications. The monomeric sugar content, released through hydrolysis, is of particular importance and is currently measured with time-consuming HPLC methods. A method for sugar detection is presented here that employs (1)H NMR spectra regressed against primary HPLC sugar concentration data to build partial least squares (PLS) models. The PLS2 model is able to predict concentrations of both major sugar components, like glucose and xylose, and minor sugars, such as arabinose and mannose, in biomass hydrolysates. The model was built with 65 samples from a variety of different biomass species and covers a wide range of sugar concentrations. Model predictions were validated with a set of 15 samples which were all within error of both HPLC and NMR integration measurements. The data collection time for these NMR measurements is less than 20 min, offering a significant improvement to the 1 h acquisition time that is required for HPLC.

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Determination of metabolite profiles in tropical wines by ¹H NMR spectroscopy and chemometrics

Cited by 12 publications

References 8 publications

NMR-based metabolomics in wine quality control and authentication

NMR-based metabolomics in wine quality control and authentication

High Throughput Screening Technologies in Biomass Characterization

Rapid determination of sugar content in biomass hydrolysates using nuclear magnetic resonance spectroscopy

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Determination of metabolite profiles in tropical wines by 1H NMR spectroscopy and chemometrics

Cited by 12 publications

References 8 publications

NMR-based metabolomics in wine quality control and authentication

NMR-based metabolomics in wine quality control and authentication

High Throughput Screening Technologies in Biomass Characterization

Rapid determination of sugar content in biomass hydrolysates using nuclear magnetic resonance spectroscopy

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Determination of metabolite profiles in tropical wines by ¹H NMR spectroscopy and chemometrics