Multivariate Analysis of Spectral Outputs of Biologically Relevant Analytes

K, Sudeep (2021) Multivariate Analysis of Spectral Outputs of Biologically Relevant Analytes. Masters thesis, Indian Institute of Science Education and Research Kolkata.

Text (MS Dissertation of Sudeep K (16MS095)) 16MS095_Thesis_file.pdf - Submitted Version Restricted to Repository staff only Download (5MB)

Abstract

Multivariate analysis is an exemplary tool to push further the boundaries of chemistry, biochemistry, medicine, biology, and chemical engineering. The advancements in spectroscopic instrumentation and multimodal approach to analytical chemistry have created a large amount of data. It is difficult to comprehend the information buried in those multidimensional data without any mathematical or statistical tools. Herein, we have used dimensionality reduction techniques, principal component analysis (PCA), and linear discriminant analysis (LDA), along with clustering method, hierarchical clustering analysis (HCA) to gather information out of spectral outputs of a nucleotide sensor. It is a tetraphenylethylenepyridinium ion-derived ligand that can non-selectively bind with more than six nucleotides and detect them with the help of the methods mentioned above. I have also developed an open-source, non-profitable website application for other fellow members who want to perform multivariate analysis without any monetary mortgage and programming hurdles.

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