Metabolic profiling based on two-dimensional J-resolved 1H NMR data and parallel factor analysis

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Metabolic profiling based on two-dimensional J-resolved 1H NMR data and parallel factor analysis. / Yilmaz, Ali; Nyberg, Nils T; Jaroszewski, Jerzy W.

In: Analytical Chemistry, Vol. 83, No. 21, 27.09.2011, p. 8278-8285.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Yilmaz, A, Nyberg, NT & Jaroszewski, JW 2011, 'Metabolic profiling based on two-dimensional J-resolved 1H NMR data and parallel factor analysis', Analytical Chemistry, vol. 83, no. 21, pp. 8278-8285. https://doi.org/10.1021/ac202089g

APA

Yilmaz, A., Nyberg, N. T., & Jaroszewski, J. W. (2011). Metabolic profiling based on two-dimensional J-resolved 1H NMR data and parallel factor analysis. Analytical Chemistry, 83(21), 8278-8285. https://doi.org/10.1021/ac202089g

Vancouver

Yilmaz A, Nyberg NT, Jaroszewski JW. Metabolic profiling based on two-dimensional J-resolved 1H NMR data and parallel factor analysis. Analytical Chemistry. 2011 Sep 27;83(21):8278-8285. https://doi.org/10.1021/ac202089g

Author

Yilmaz, Ali ; Nyberg, Nils T ; Jaroszewski, Jerzy W. / Metabolic profiling based on two-dimensional J-resolved 1H NMR data and parallel factor analysis. In: Analytical Chemistry. 2011 ; Vol. 83, No. 21. pp. 8278-8285.

Bibtex

@article{950309d885ad4c259ad51931145bf8d9,
title = "Metabolic profiling based on two-dimensional J-resolved 1H NMR data and parallel factor analysis",
abstract = "Metabolic profiling of natural products is used to map correlated concentration variances of known and unknown secondary metabolites in extracts. NMR-spectroscopy is in this respect regarded as convenient and reproducible technique with the ability to detect a wide range of small organic compounds. Two-dimensional J-resolved NMR-spectra are used in this context to resolve overlapping signals by separating the effect of J-coupling from the effect of chemical shifts. Often one-dimensional projections of these data are used as input for standard multivariate statistical methods and only the intensity variances along the chemical shift axis are taken into account. Here, we describe the use of parallel factor analysis (PARAFAC) as a tool to preprocess a set of two-dimensional J-resolved spectra with the aim of keeping the J-coupling information intact. PARAFAC is a mathematical decomposition method that fits three-way experimental data to a model whose parameters in this case reflect concentrations and individual components spectrum along the chemical shift axis and corresponding profiles along the J-coupling axis. A set of saffron samples, directly extracted with methanol-d4, were used as a model system to evaluate the feasibility and merits of the method. To successfully use PARAFAC the two-dimensional spectra (n = 96) had to be aligned and processed in narrow windows (0.04 ppm wide) along the chemical shift axis. Selection of windows and number of components for each PARAFAC-model was done automatically by evaluating amount of explained variance and core consistency values. Score plots showing the distribution of objects in relation to each other, and loading plots in the form of two-dimensional pseudo-spectra with the same appearance as the original J-resolved spectra but with positive and negative contributions are presented. Loadings are interpreted not only in terms of signals with different chemical shifts, but also the associated J-coupling profiles.",
keywords = "Former Faculty of Pharmaceutical Sciences",
author = "Ali Yilmaz and Nyberg, {Nils T} and Jaroszewski, {Jerzy W.}",
note = "Keywords: NMR, PARAFAC, 2-dimensional, J-resolved, saffron",
year = "2011",
month = "9",
day = "27",
doi = "10.1021/ac202089g",
language = "English",
volume = "83",
pages = "8278--8285",
journal = "Analytical Chemistry",
issn = "0003-2700",
publisher = "American Chemical Society",
number = "21",

}

RIS

TY - JOUR

T1 - Metabolic profiling based on two-dimensional J-resolved 1H NMR data and parallel factor analysis

AU - Yilmaz, Ali

AU - Nyberg, Nils T

AU - Jaroszewski, Jerzy W.

N1 - Keywords: NMR, PARAFAC, 2-dimensional, J-resolved, saffron

PY - 2011/9/27

Y1 - 2011/9/27

N2 - Metabolic profiling of natural products is used to map correlated concentration variances of known and unknown secondary metabolites in extracts. NMR-spectroscopy is in this respect regarded as convenient and reproducible technique with the ability to detect a wide range of small organic compounds. Two-dimensional J-resolved NMR-spectra are used in this context to resolve overlapping signals by separating the effect of J-coupling from the effect of chemical shifts. Often one-dimensional projections of these data are used as input for standard multivariate statistical methods and only the intensity variances along the chemical shift axis are taken into account. Here, we describe the use of parallel factor analysis (PARAFAC) as a tool to preprocess a set of two-dimensional J-resolved spectra with the aim of keeping the J-coupling information intact. PARAFAC is a mathematical decomposition method that fits three-way experimental data to a model whose parameters in this case reflect concentrations and individual components spectrum along the chemical shift axis and corresponding profiles along the J-coupling axis. A set of saffron samples, directly extracted with methanol-d4, were used as a model system to evaluate the feasibility and merits of the method. To successfully use PARAFAC the two-dimensional spectra (n = 96) had to be aligned and processed in narrow windows (0.04 ppm wide) along the chemical shift axis. Selection of windows and number of components for each PARAFAC-model was done automatically by evaluating amount of explained variance and core consistency values. Score plots showing the distribution of objects in relation to each other, and loading plots in the form of two-dimensional pseudo-spectra with the same appearance as the original J-resolved spectra but with positive and negative contributions are presented. Loadings are interpreted not only in terms of signals with different chemical shifts, but also the associated J-coupling profiles.

AB - Metabolic profiling of natural products is used to map correlated concentration variances of known and unknown secondary metabolites in extracts. NMR-spectroscopy is in this respect regarded as convenient and reproducible technique with the ability to detect a wide range of small organic compounds. Two-dimensional J-resolved NMR-spectra are used in this context to resolve overlapping signals by separating the effect of J-coupling from the effect of chemical shifts. Often one-dimensional projections of these data are used as input for standard multivariate statistical methods and only the intensity variances along the chemical shift axis are taken into account. Here, we describe the use of parallel factor analysis (PARAFAC) as a tool to preprocess a set of two-dimensional J-resolved spectra with the aim of keeping the J-coupling information intact. PARAFAC is a mathematical decomposition method that fits three-way experimental data to a model whose parameters in this case reflect concentrations and individual components spectrum along the chemical shift axis and corresponding profiles along the J-coupling axis. A set of saffron samples, directly extracted with methanol-d4, were used as a model system to evaluate the feasibility and merits of the method. To successfully use PARAFAC the two-dimensional spectra (n = 96) had to be aligned and processed in narrow windows (0.04 ppm wide) along the chemical shift axis. Selection of windows and number of components for each PARAFAC-model was done automatically by evaluating amount of explained variance and core consistency values. Score plots showing the distribution of objects in relation to each other, and loading plots in the form of two-dimensional pseudo-spectra with the same appearance as the original J-resolved spectra but with positive and negative contributions are presented. Loadings are interpreted not only in terms of signals with different chemical shifts, but also the associated J-coupling profiles.

KW - Former Faculty of Pharmaceutical Sciences

U2 - 10.1021/ac202089g

DO - 10.1021/ac202089g

M3 - Journal article

C2 - 21950244

VL - 83

SP - 8278

EP - 8285

JO - Analytical Chemistry

JF - Analytical Chemistry

SN - 0003-2700

IS - 21

ER -

ID: 34528193