Statistical modelling in chemistry  applications to nuclear magnetic resonance and polymerase chain reaction
(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 onedimensional 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 CramérRao bounds for the estimated parameters of a onedimensional NMR signal model. Finally, in Paper D a new algorithm, MRELAX, 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 foodchain are considered. The focus is on design of prePCR 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 foodchain. 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 prespecified 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 realtime PCR amplification and detection is presented in Paper C. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/record/465230
 author
 Grage, Halfdan ^{LU}
 opponent

 Professor Koski, Timo, matematiska institutionen, Linköpings universitet
 organization
 publishing date
 2002
 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, CramérRao 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
 20021210 10:15
 ISSN
 14040034
 ISBN
 9162854593
 language
 English
 LU publication?
 yes
 id
 fd5bc6099c514f13b68d0b03f7647cdf (old id 465230)
 date added to LUP
 20070927 15:09:06
 date last changed
 20160919 08:44:55
@phdthesis{fd5bc6099c514f13b68d0b03f7647cdf, 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 onedimensional 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 CramérRao bounds for the estimated parameters of a onedimensional NMR signal model. Finally, in Paper D a new algorithm, MRELAX, 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 foodchain are considered. The focus is on design of prePCR 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 foodchain. 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 prespecified 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 realtime PCR amplification and detection is presented in Paper C.}, author = {Grage, Halfdan}, isbn = {9162854593}, issn = {14040034}, keyword = {Statistik,operationsanalys,programmering,aktuariematematik,programming,actuarial mathematics,Statistics,operations research,experimental design.,logistic regression,detection probability,region of operability,sampling. Diagnostic PCR,CramérRao bounds,maximum likelihood estimation,NMR spectroscopy,Gaussian noise}, language = {eng}, pages = {190}, 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}, }