Fourier transform infrared spectra clustering for biochar: a principal component analysis approach

Pashuk, Vikentiy (2024) Fourier transform infrared spectra clustering for biochar: a principal component analysis approach. Memorial University of Newfoundland. (Unpublished)

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Abstract

Biochar, recognized for its porous structure and functional groups, holds promise as a tool for mitigating greenhouse gas transmissions, particularly CO₂. This study acts as a precursor for future exploration of the efficacy of Principal Component Analysis (PCA) on Fourier Transform Infrared spectra for sample categorization for CO₂ adsorption. Utilizing RStudio, spectra from feedstock and biochar auger wood and snow crab samples were subjected to PCA. Results indicate that, in smaller sample systems, overall spectral intensity outweighs chemical differences in peak structure, while larger systems exhibit increased significance of peak structure due to comparable intensities. Future research should investigate the in uence of experimental conditions, such as temperature and exposure time, on spectral intensity for conclusive PCA clustering. Although PCA effectively distinguishes spectral features in diverse samples, its applicability to larger systems with colinear features requires further exploration.

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