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Processing and analysis of NMR data: Impurity determination and metabolic profiling
Stockholm University, Faculty of Science, Department of Analytical Chemistry.
2005 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis describes the use of nuclear magnetic resonance (NMR) spectrometry as an analytical tool. The theory of NMR spectroscopy in general and quantitative NMR spectrometry (qNMR) in particular is described and the instrumental properties and parameter setups for qNMR measurements are discussed. Examples of qNMR are presented by impurity determination of pharmaceutical compounds and analysis of urine samples from rats fed with either water or a drug (metabolic profiling). The instrumental parameter setup of qNMR and traditional data pre-treatments are examined. Spectral smoothing by convolution with a triangular function, which is an unusual application in this context, was shown to be successful regarding the sensitivity and robustness of the method in paper II. In addition, papers III and IV comprise the field of peak alignment, especially designed for 1H-NMR spectra of urine samples. This is an important preprocessing tool when multivariate analysis is to be applied. A novel peak alignment method was developed and compared to the traditional bucketing approach and a conceptually different alignment method.

Univariate, multivariate, linear and nonlinear data analyses were applied to qNMR data. In papers I–II, calibration models were created to examine the potential of qNMR for these applications. The data analysis in papers III–VI was mainly explorative. The potential of data fusion and data correlation was examined in order to increase the possibilities of analysing the highly complex samples from metabolic profiling (papers V–VI). Data from LC/MS analysis of the same samples were used with the 1H-NMR data in different ways. Correlation analyses between the 1H-NMR data and the drug metabolites identified from the LC/MS data were also performed. In this process, data fusion proved to be a valuable tool.

Place, publisher, year, edition, pages
Stockholm: Institutionen för analytisk kemi , 2005. , 95 p.
Keyword [en]
quantitative nuclear magnetic resonance spectrometry, qNMR, multivariate analysis, peak alignment, data fusion
National Category
Analytical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-712ISBN: 91-7155-139-5 (print)OAI: oai:DiVA.org:su-712DiVA: diva2:197688
Public defence
2005-12-01, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 12 A, Stockholm, 13:00
Opponent
Supervisors
Available from: 2005-10-28 Created: 2005-10-28Bibliographically approved
List of papers
1. NMR and Bayesian regularized neural network regression for impurity determination of 4-aminophenol
Open this publication in new window or tab >>NMR and Bayesian regularized neural network regression for impurity determination of 4-aminophenol
2002 In: Journal of Pharmaceutical and Biomedical Analysis, ISSN 0731-7085, Vol. 29, no 3, 495-505 p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:su:diva-24510 (URN)
Note
Part of urn:nbn:se:su:diva-712Available from: 2005-10-28 Created: 2005-10-28Bibliographically approved
2. Quantification of aldehyde impurities in poloxamer by 1H NMR spectrometry
Open this publication in new window or tab >>Quantification of aldehyde impurities in poloxamer by 1H NMR spectrometry
2005 In: Analytica Chimica Acta, ISSN 0003-2670, Vol. 552, no 1-2, 160-165 p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:su:diva-24511 (URN)
Note
Part of urn:nbn:se:su:diva-712Available from: 2005-10-28 Created: 2005-10-28Bibliographically approved
3. Peak alignment of NMR signals by means of a genetic algorithm
Open this publication in new window or tab >>Peak alignment of NMR signals by means of a genetic algorithm
2003 In: Analytica Chimica Acta, ISSN 0003-2670, Vol. 487, no 2, 189-199 p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:su:diva-24512 (URN)
Note
Part of urn:nbn:se:su:diva-712Available from: 2005-10-28 Created: 2005-10-28Bibliographically approved
4. A comparison of methods for alignment of NMR peaks in the context of cluster analysis
Open this publication in new window or tab >>A comparison of methods for alignment of NMR peaks in the context of cluster analysis
Show others...
2005 In: Journal of Pharmaceutical and Biomedical Analysis, ISSN 0731-7085, Vol. 38, no 5, 824-832 p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:su:diva-24513 (URN)
Note
Part of urn:nbn:se:su:diva-712Available from: 2005-10-28 Created: 2005-10-28Bibliographically approved
5. Evaluation of different techniques for fusion of LC/MS and 1HNMR data
Open this publication in new window or tab >>Evaluation of different techniques for fusion of LC/MS and 1HNMR data
2007 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 85, no 1, 102-109 p.Article in journal (Refereed) Published
Abstract [en]

In the analyses of highly complex samples (for example, metabolic fingerprinting), the data might not suffice for classification when using only a single analytical technique. Hence, the use of two complementary techniques, e.g., LUMS and H-1-NMR, might be advantageous. Another possible advantage from using two different techniques is the ability to verify the results (for instance, by verifying a time trend of a metabolic pattern). In this work, both LC/MS and H-1-NMR data from analysis of rat urine have been used to obtain metabolic fingerprints. A comparison of three different methods for data fusion of the two data sets was performed and the possibilities and difficulties associated with data fusion were discussed. When comparing concatenated data, full hierarchical modeling, and batch modeling, the first two approaches were found to be the most successful. Different types of block scaling and variable scaling were evaluated and the optimal scaling for each case was found by cross validation. Validations of the final models were performed by means of an external test set.(2)

Keyword
H-1-NMR; LC/MS; data fusion; data concatenation; hierarchical modeling; batch modeling
Identifiers
urn:nbn:se:su:diva-24514 (URN)000243628800011 ()
Note
Part of urn:nbn:se:su:diva-712Available from: 2005-10-28 Created: 2005-10-28 Last updated: 2017-12-13Bibliographically approved
6. Enhanced multivariate analysis by correlation scaling and fusion of LC/MS and 1H-NMR data
Open this publication in new window or tab >>Enhanced multivariate analysis by correlation scaling and fusion of LC/MS and 1H-NMR data
2007 (English)In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, E-ISSN 1873-3239, Vol. 85, no 2, 179-185 p.Article in journal (Refereed) Published
Abstract [en]

A method to enhance the multivariate data interpretation of, for instance, metabolic profiles is presented. This was done by correlation scaling of 1H NMR data by the time pattern of drug metabolite peaks identified by LC/MS, followed by parallel factor analysis (PARAFAC). The variables responsible for the discrimination between the dosed and control rats in this model were then eliminated in both data sets. Next, an additional PARAFAC analysis was performed with both LC/MS and 1H NMR data, fused by outer product analysis (OPA), to obtain sufficient class separation. The loadings from this second PARAFAC analysis showed new peaks discriminating between the classes. The time trajectories of these peaks did not agree with the drug metabolites and were detected as possible candidates for markers. These data analyses were also compared with the PARAFAC analysis of raw data, which showed very much the same loading peaks as for the correlation-scaled data, although the intensities differed. Elimination of the variables correlated with the drug metabolites was therefore necessary to be able to select the peaks which were not drug metabolites and which discriminated between the classes.1

Place, publisher, year, edition, pages
Elsevier B.V, 2007
Keyword
Data correlation; Correlation scaling; Data fusion; Outer product analysis; PARAFAC analysis; PLS
National Category
Analytical Chemistry
Identifiers
urn:nbn:se:su:diva-24515 (URN)doi:10.1016/j.chemolab.2006.06.012 (DOI)000245073200004 ()
Note
Part of urn:nbn:se:su:diva-712Available from: 2005-10-28 Created: 2005-10-28 Last updated: 2017-12-13Bibliographically approved

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