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Data mining in the spectro-microscopic analysis of complex material

University of British Columbia

Vibrational spectroscopy has received significant interest in last decades as a robust, rapid, and cost-effective alternative to the traditional wet-chemical methods employed by various industries. The spectra of complex materials may contain some components with a low concentration, whose information is buried within a major peak of another component. These small hidden peaks contain critical information in some analysis. This thesis aims to develop novel data mining methods to improve the quality of data, select its essential features, and finally build prediction models. The pulp industry offers one example in which spectroscopy offers attractive advantages as an on-line method for optimizing manufacturing. While spectroscopic techniques are inherently sensitive to many of properties of interest to the pulp industry, they are potentially sensitive to provide features uncorrelated with physical properties of pulp; which could hinder the development of robust prediction models. To overcome this challenge, we introduced Template Oriented Genetic Algorithm (TOGA). TOGA is aimed to establish significant features to assign predictors according to a template determined to minimize prediction variance in a calibration space. It was found that TOGA significantly improved the prediction accuracy of certain pulp properties compared to those without undergoing these data processing techniques. Near Infrared (NIR) is the most well-known spectroscopy technique which has been successfully applied to pulp industry. However, broad overtone NIR absorption band makes discerning of signature features a difficult process. We showed that a combination of DWT and Orthogonal Signal Correction improved accuracy of prediction models built based on pulp NIR spectra.In the second part of this thesis, a combined technique of interferometric scattering microscopy (iSCAT) and Raman spectroscopy was used to study the dynamics of gold-nanoparticle cellular uptake in cancer and normal cells model. Images derived from the study of these complex samples are heterogeneous which poses a challenge on true quantification and identification of the structure and components of a cell. To address this challenge, we used DWT to remove the out-of-focus and uncorrelated features from the original iSCAT images. This would make a true 3D volume of a cell and a precise track of AuNP internalizing a cell.

Text

2017-07-07

Attribution-NonCommercial-NoDerivatives 4.0 International

http://hdl.handle.net/2429/62176

Chemistry

University of British Columbia

UBCV

http://creativecommons.org/licenses/by-nc-nd/4.0/