Multivariate Measurements and Chemical Imaging of Organic Compounds
Doctoral thesis, 2008

Chemical imaging is an emerging analytical technique, based on the interplay between, most often, spectroscopic measurements and advanced evaluation methodology. With this we can study the distribution of chemical compounds in different compartments, such as individual cells and pharmaceutical tablets. Also, chemical imaging allows for studies of time dependent processes, such as uptake of drugs or dissolution. Surface enhanced Raman spectroscopy (SERS) was used to acquire data in complex matrices (lymphocytes), the spectra obtained varied significantly in background and intensity. Near-infrared spectroscopy (NIR) and atomic force microscopy (AFM) were used to characterise the texture of pharmaceutical tablets. Also, the dissolution of a tablet was monitored over time. The focus of this thesis is the data analysis, more specifically the multivariate handling of large data sets, comparisons of images, structure identification and textures. Three application areas have been more thoroughly investigated; normalisation of spectral data to obtain quantitative results during difficult statistical sampling conditions; extraction of relevant spectral and chemical information from complex matrices and measurements in noisy or high-background environments; detection of small changes in spatial features. A normalisation method for highly variable SERS measurements, based on an internal standard self assembled monolayer was developed and studied for use with multivariate regression. It was shown that the highly variable background in SERS imaging could be reduced with wavelet filtering and modelling of the background to extract chemically interesting spectra as outliers. Together with the concept of an internal standard applied as a self assembled monolayer, this points in the direction of a solution for quantitative SERS. The use of wavelet transformation in combination with principal components analysis, were shown to be successful in characterisation of spatial features without prior segmentation. Continuous wavelet transform (CWT) gave more consistent results in texture analysis than discrete wavelet transform (DWT), and dual-tree complex wavelet transform (DT-CWT).

Image analysis



Chemical Imaging




Wavelet Transform

Multivariate analysis

Opponent: Prof. Olav Kvalheim, Department of Chemistry, Bergen University, Norway


Johan Engelbrektsson

Chalmers, Chemical and Biological Engineering, Analytical Chemistry

Internal standard in surface-enhanced Raman spectroscopy

Analytical Chemistry,; Vol. 76(2004)p. 7391-7395

Journal article

Self-assembled monolayer coating for normalization of surface enhanced Raman spectra

Nano Letters,; Vol. 4(2004)p. 309-312

Journal article

Surface-enhanced Raman scattering imaging of single living lymphocytes with multivariate evaluation

Spectrochimica Acta - Part A: Molecular and Biomolecular Spectroscopy,; Vol. 61(2005)p. 755-760

Journal article

Multivariate methodology for surface enhanced Raman chemical imaging of lymphocytes

Chemometrics and Intelligent Laboratory Systems,; Vol. 81(2006)p. 13-20

Journal article

An evaluation of 2D-wavelet filters for estimation of differences in textures of pharmaceutical tablets

Chemometrics and Intelligent Laboratory Systems,; Vol. 84(2006)p. 3-8

Journal article

Subject Categories

Analytical Chemistry



Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 2730


Opponent: Prof. Olav Kvalheim, Department of Chemistry, Bergen University, Norway

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