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A solution to the 1D NMR alignment problem using an extended generalized fuzzy Hough transform and mode support
Stockholm University, Faculty of Science, Department of Analytical Chemistry.
Stockholm University, Faculty of Science, Department of Analytical Chemistry.
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2009 (English)In: Analytical and Bioanalytical Chemistry, ISSN 1618-2642, E-ISSN 1618-2650, Vol. 395, no 1, 213-223 p.Article in journal (Refereed) Published
Abstract [en]

This paper approaches the problem of intersample peak correspondence in the context of later applying statistical data analysis techniques to 1D 1H-nuclear magnetic resonance (NMR) data. Any data analysis methodology will fail to produce meaningful results if the analyzed data table is not synchronized, i.e., each analyzed variable frequency (Hz) does not originate from the same chemical source throughout the entire dataset. This is typically the case when dealing with NMR data from biological samples. In this paper, we present a new state of the art for solving this problem using the generalized fuzzy Hough transform (GFHT). This paper describes significant improvements since the method was introduced for NMR datasets of plasma in Csenki et al. (Anal Bioanal Chem 389:875-885, 15) and is now capable of synchronizing peaks from more complex datasets such as urine as well as plasma data. We present a novel way of globally modeling peak shifts using principal component analysis, a new algorithm for calculating the transform and an effective peak detection algorithm. The algorithm is applied to two real metabonomic 1H-NMR datasets and the properties of the method are compared to bucketing. We implicitly prove that GFHT establishes the objectively true correspondence. Desirable features of the GFHT are: (1) intersample peak correspondence even if peaks change order on the frequency axis and (2) the method is symmetric with respect to the samples.

Place, publisher, year, edition, pages
2009. Vol. 395, no 1, 213-223 p.
Keyword [en]
Metabolic profiling, NMR, Peak detection, Image processing, Hough transform, Synchronization, Alignment
National Category
Analytical Chemistry
Research subject
Analytical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-35113DOI: 10.1007/s00216-009-2940-4ISI: 000268866800024OAI: oai:DiVA.org:su-35113DiVA: diva2:286439
Available from: 2010-01-14 Created: 2010-01-14 Last updated: 2017-12-12Bibliographically approved
In thesis
1. Solving the correspondence problem in analytical chemistry: Automated methods for alignment and quantification of multiple signals
Open this publication in new window or tab >>Solving the correspondence problem in analytical chemistry: Automated methods for alignment and quantification of multiple signals
2012 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

When applying statistical data analysis techniques to analytical chemical data, all variables must have correspondence over the samples dimension in order for the analysis to generate meaningful results. Peak shifts in NMR and chromatography destroys that correspondence and creates data matrices that have to be aligned before analysis. In this thesis, new methods are introduced that allow for automated transformation from unaligned raw data to aligned data matrices where each column corresponds to a unique signal. These methods are based around linear multivariate models for the peak shifts and Hough transform for establishing the parameters of these linear models. Methods for quantification under difficult conditions, such as crowded spectral regions, noisy data and unknown peak identities are also introduced. These methods include automated peak selection and a robust method for background subtraction. This thesis focuses on the processing of the data; the experimental work is secondary and is not discussed in great detail.

All the developed methods are put together in a full procedure that takes us from raw data to a table of concentrations in a matter of minutes.

The procedure is applied to 1H-NMR data from biological samples, which is one of the toughest alignment tasks available in the field of analytical chemistry. It is shown that the procedure performs consistently on the same level as much more labor intensive manual techniques such as Chenomx NMRSuite spectral profiling.

Several kinds of datasets are evaluated using the procedure. Most of the data is from the field of Metabolomics, where the goal is to establish concentrations of as many small molecules as possible in biological samples.

Place, publisher, year, edition, pages
Stockholm: Department of Analytical Chemistry, Stockholm University, 2012. 74 p.
National Category
Chemical Sciences
Research subject
Analytical Chemistry
Identifiers
urn:nbn:se:su:diva-74556 (URN)978-91-7447-485-5 (ISBN)
Public defence
2012-05-25, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 16 B, Stockholm, 13:00 (English)
Opponent
Supervisors
Available from: 2012-05-03 Created: 2012-03-16 Last updated: 2012-05-02Bibliographically approved

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