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Statistical modelling in chemistry - applications to nuclear magnetic resonance and polymerase chain reaction

Grage, Halfdan LU (2002)
Abstract
This thesis consists of two parts with the common theme of statistical modelling in chemistry. The first part is concerned with applications in nuclear magnetic resonance (NMR) spectroscopy, while the second part deals with applications in polymerase chain reaction (PCR).



The problems considered in the first part all have their origin in protein NMR spectroscopy, although they are treated mainly from a statistical perspective in the thesis. The interpretation of complex and crowded protein NMR spectra contaminated by noise is a challenging task where the method of maximum likelihood based on the Gaussian distribution has been used with good results. In Paper A it is investigated under what conditions on the processing of... (More)
This thesis consists of two parts with the common theme of statistical modelling in chemistry. The first part is concerned with applications in nuclear magnetic resonance (NMR) spectroscopy, while the second part deals with applications in polymerase chain reaction (PCR).



The problems considered in the first part all have their origin in protein NMR spectroscopy, although they are treated mainly from a statistical perspective in the thesis. The interpretation of complex and crowded protein NMR spectra contaminated by noise is a challenging task where the method of maximum likelihood based on the Gaussian distribution has been used with good results. In Paper A it is investigated under what conditions on the processing of the NMR signal the distributional assumptions usually made concerning the noise in the sampled signal may be appropriate. In Paper B some properties of the inverse Fisher information matrix pertaining to the model for a one-dimensional NMR signal are studied with respect to the influence of correlated noise and the problem of parameter resolution. In Paper C the combined effects of filtering and sampling are investigated in terms of their influence on the Cramér-Rao bounds for the estimated parameters of a one-dimensional NMR signal model. Finally, in Paper D a new algorithm, M-RELAX, for estimation of the parameters of several consecutive time series with amplitude decay is proposed. Such problems arise for instance in certain screening experiments in medical drug discovery.



In the second part of the thesis some problems encountered in connection with diagnostic PCR analysis and detection of pathogenic bacteria in the food-chain are considered. The focus is on design of pre-PCR strategies for future routine analysis to get a reliable and robust detection of pathogenic <i>Yersinia enterocolitica</i> and <i>Salmonella</i> in complex samples from the food-chain. In Paper A a logistic regression model for the reliability of PCR detection of <i>Yersinia enterocolitica</i> is presented, whereby it is possible to define a practical operating range, determined by the model and a pre-specified detection probability. The development, through a statistical approach using screening, factorial design experiments and confirmatory tests, of a new medium specifically optimised for PCR is described in Paper B. A combined linear and logistic regression model for real-time PCR amplification and detection is presented in Paper C. (Less)
Please use this url to cite or link to this publication:
author
supervisor
opponent
  • Professor Koski, Timo, matematiska institutionen, Linköpings universitet
organization
publishing date
type
Thesis
publication status
published
subject
keywords
Statistik, operationsanalys, programmering, aktuariematematik, programming, actuarial mathematics, Statistics, operations research, experimental design., logistic regression, detection probability, region of operability, sampling. Diagnostic PCR, Cramér-Rao bounds, maximum likelihood estimation, NMR spectroscopy, Gaussian noise
pages
190 pages
publisher
Centre for Mathematical Sciences, Lund University
defense location
Centre for Mathematical Sciences, Sölvegatan 18, sal MH:C
defense date
2002-12-10 10:15:00
ISBN
91-628-5459-3
language
English
LU publication?
yes
additional info
Article: This thesis consists of two parts. The first part concerns applications to nuclear magnetic resonance spectroscopy and is based on the following four papers: Article: A. Halfdan Grage and Mikael Akke: A statistical analysis of NMR spectrometer noise.B. Halfdan Grage, Jan Holst, and Tobias Rydén: Some properties of the Cramér-Rao bounds for damped exponentials in noise.C. Halfdan Grage, Jan Holst, and Tobias Rydén: The effect of sampling rate in the estimation of parameters of exponentially damped sinusoids.D. Halfdan Grage, Jan Holst, and Tobias Rydén: M-RELAX, an algorithm for multiple time series with amplitude decay. Article: The second part concerns applications to polymerase chain reaction and is based on the following three papers: Article: A. Rickard Knutsson, Ylva Blixt, Halfdan Grage, Elisabeth Borch, Peter Rådström: Evaluation of selective enrichment PCR procedures for Yersinia enterocolitica. International Journal of Food Microbiology 73 (2002) 35-46.B. Rickard Knutsson, Massimo Fontanesi, Halfdan Grage, Peter Rådström: Development of a PCR-compatible enrichment medium for Yersinia enterocolitica: amplification precision and dynamic detection range during cultivation. International Journal of Food Microbiology 72 (2002) 185-201.C. Rickard Knutsson, Charlotta Löfström, Halfdan Grage, Jeffrey Hoorfar, and Peter Rådström: Modeling of 5' Nuclease Real-Time Responses for Optimization of a High-Throughput Enrichment PCR Procedure for Salmonella enterica. Journal of Clinical Microbiology 40 (2002) 52-60.
id
fd5bc609-9c51-4f13-b68d-0b03f7647cdf (old id 465230)
date added to LUP
2016-04-01 16:09:24
date last changed
2018-11-21 20:39:10
@phdthesis{fd5bc609-9c51-4f13-b68d-0b03f7647cdf,
  abstract     = {This thesis consists of two parts with the common theme of statistical modelling in chemistry. The first part is concerned with applications in nuclear magnetic resonance (NMR) spectroscopy, while the second part deals with applications in polymerase chain reaction (PCR).<br/><br>
<br/><br>
The problems considered in the first part all have their origin in protein NMR spectroscopy, although they are treated mainly from a statistical perspective in the thesis. The interpretation of complex and crowded protein NMR spectra contaminated by noise is a challenging task where the method of maximum likelihood based on the Gaussian distribution has been used with good results. In Paper A it is investigated under what conditions on the processing of the NMR signal the distributional assumptions usually made concerning the noise in the sampled signal may be appropriate. In Paper B some properties of the inverse Fisher information matrix pertaining to the model for a one-dimensional NMR signal are studied with respect to the influence of correlated noise and the problem of parameter resolution. In Paper C the combined effects of filtering and sampling are investigated in terms of their influence on the Cramér-Rao bounds for the estimated parameters of a one-dimensional NMR signal model. Finally, in Paper D a new algorithm, M-RELAX, for estimation of the parameters of several consecutive time series with amplitude decay is proposed. Such problems arise for instance in certain screening experiments in medical drug discovery.<br/><br>
<br/><br>
In the second part of the thesis some problems encountered in connection with diagnostic PCR analysis and detection of pathogenic bacteria in the food-chain are considered. The focus is on design of pre-PCR strategies for future routine analysis to get a reliable and robust detection of pathogenic &lt;i&gt;Yersinia enterocolitica&lt;/i&gt; and &lt;i&gt;Salmonella&lt;/i&gt; in complex samples from the food-chain. In Paper A a logistic regression model for the reliability of PCR detection of &lt;i&gt;Yersinia enterocolitica&lt;/i&gt; is presented, whereby it is possible to define a practical operating range, determined by the model and a pre-specified detection probability. The development, through a statistical approach using screening, factorial design experiments and confirmatory tests, of a new medium specifically optimised for PCR is described in Paper B. A combined linear and logistic regression model for real-time PCR amplification and detection is presented in Paper C.},
  author       = {Grage, Halfdan},
  isbn         = {91-628-5459-3},
  language     = {eng},
  publisher    = {Centre for Mathematical Sciences, Lund University},
  school       = {Lund University},
  title        = {Statistical modelling in chemistry - applications to nuclear magnetic resonance and polymerase chain reaction},
  year         = {2002},
}