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Variance Reduction in Analytical Chemistry: New Numerical Methods in Chemometrics and Molecular Simulation
Stockholm University, Faculty of Science, Department of Analytical Chemistry.
2004 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis is based on five papers addressing variance reduction in different ways. The papers have in common that they all present new numerical methods.

Paper I investigates quantitative structure-retention relationships from an image processing perspective, using an artificial neural network to preprocess three-dimensional structural descriptions of the studied steroid molecules.

Paper II presents a new method for computing free energies. Free energy is the quantity that determines chemical equilibria and partition coefficients. The proposed method may be used for estimating, e.g., chromatographic retention without performing experiments.

Two papers (III and IV) deal with correcting deviations from bilinearity by so-called peak alignment. Bilinearity is a theoretical assumption about the distribution of instrumental data that is often violated by measured data. Deviations from bilinearity lead to increased variance, both in the data and in inferences from the data, unless invariance to the deviations is built into the model, e.g., by the use of the method proposed in paper III and extended in paper IV.

Paper V addresses a generic problem in classification; namely, how to measure the goodness of different data representations, so that the best classifier may be constructed.

Variance reduction is one of the pillars on which analytical chemistry rests. This thesis considers two aspects on variance reduction: before and after experiments are performed. Before experimenting, theoretical predictions of experimental outcomes may be used to direct which experiments to perform, and how to perform them (papers I and II). After experiments are performed, the variance of inferences from the measured data are affected by the method of data analysis (papers III-V).

Place, publisher, year, edition, pages
Stockholm: Institutionen för analytisk kemi , 2004. , 59 p.
Keyword [en]
chemometrics, pulse-coupled neural networks, peak alignment, class separability, molecular dynamics, Monte Carlo, expanded ensembles, free energy
National Category
Analytical Chemistry
Identifiers
URN: urn:nbn:se:su:diva-283ISBN: 91-7265-968-8 (print)OAI: oai:DiVA.org:su-283DiVA: diva2:191920
Public defence
2004-12-03, Magnélisalen, Kemiska övningslaboratoriet, Svante Arrhenius väg 12 A, Stockholm, 10:00
Opponent
Supervisors
Available from: 2004-11-11 Created: 2004-11-11Bibliographically approved
List of papers
1. Pre-processing of three-way data by pulse-coupled neural networks—an imaging approach
Open this publication in new window or tab >>Pre-processing of three-way data by pulse-coupled neural networks—an imaging approach
2001 In: Chemometrics and Intelligent Laboratory Systems, ISSN 0169-7439, Vol. 57, no 1, 25-36 p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:su:diva-23393 (URN)
Note
Part of urn:nbn:se:su:diva-283Available from: 2004-11-11 Created: 2004-11-11Bibliographically approved
2. Determination of solvation free energies by adaptive expanded ensemble molecular dynamics
Open this publication in new window or tab >>Determination of solvation free energies by adaptive expanded ensemble molecular dynamics
2004 In: Journal of Chemical Physics, ISSN 0021-9606, Vol. 120, no 8, 3770-3776 p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:su:diva-23394 (URN)
Note
Part of urn:nbn:se:su:diva-283Available from: 2004-11-11 Created: 2004-11-11Bibliographically approved
3. Peak alignment using reduced set mapping
Open this publication in new window or tab >>Peak alignment using reduced set mapping
2003 In: Journal of Chemometrics, ISSN 0886-9383, Vol. 17, no 11, 573-582 p.Article in journal (Refereed) Published
Identifiers
urn:nbn:se:su:diva-23395 (URN)
Note
Part of urn:nbn:se:su:diva-283Available from: 2004-11-11 Created: 2004-11-11Bibliographically approved
4. Extensions to peak alignment using reduced set mapping and classification of LC-UV data from peptide mapping
Open this publication in new window or tab >>Extensions to peak alignment using reduced set mapping and classification of LC-UV data from peptide mapping
2005 (English)In: Journal of Chemometrics, ISSN 0886-9383, E-ISSN 1099-128X, Vol. 18, no 10, 465-473 p.Article in journal (Refereed) Published
Abstract [en]

Peak alignment using reduced set mapping (PARS) is extended with a new baseline approximation

and a new dendrogram alignment scheme, which is designed to avoid the issue of selecting a target

chromatogram for the alignment. Two data sets with LC/UV data are studied and it is shown that

peak alignment with PARS increases the class separation substantially in the principal component

score space. The results indicate that it is possible to use PARS for calibration transfer of multivariate

models of chromatographic data.

Place, publisher, year, edition, pages
Wiley InterScience, 2005
Keyword
peak shift; peak alignment; peptide mapping; classification; PARS; chromatography
National Category
Analytical Chemistry
Identifiers
urn:nbn:se:su:diva-23396 (URN)10.1002/cem.892 (DOI)
Note
Part of urn:nbn:se:su:diva-283Available from: 2004-11-11 Created: 2004-11-11 Last updated: 2010-10-20Bibliographically approved
5. A measure of class separation
Open this publication in new window or tab >>A measure of class separation
In: Journal of ChemometricsArticle in journal (Refereed) Submitted
Identifiers
urn:nbn:se:su:diva-23397 (URN)
Note
Part of urn:nbn:se:su:diva-283Available from: 2004-11-11 Created: 2004-11-11Bibliographically approved

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