Author(s)

Innocent Pumure¹, Shannon Ford¹, Jessica Shannon¹, Christopher Kohen¹, Amanda Mulcahy¹, Kelvin Frank¹, Sheri Sisco¹, Nhamo Chaukura²

¹College of Heath, Science and Technology, School of Environmental, Physical and Applied Sciences, University of Central Missouri, Warrensburg, MO, USA.
²Department of Polymer Science & Engineering, Harare Institute of Technology, Belvedere, Harare, Zimbabwe.

We used both correlation and covariance-principal component analysis (PCA) to classify the same absorption-reflectance data collected from 13 different polymeric fabric materials that was obtained using Attenuated Total Reflectance-Fourier Transform Infrared spectroscopy (ATR-FTIR). The application of the two techniques, though similar, yielded results that represent different chemical properties of the polymeric substances. On one hand, correlation-PCA enabled the classification of the fabric materials according to the organic functional groups of their repeating monomer units. On the other hand, covariance-PCA was used to classify the fabric materials primarily according to their origins; natural (animal or plant) or synthetic. Hence besides major chemical functional groups of the repeat units, it appears covariance-PCA is also sensitive to other characteristic chemical (inorganic and/or organic) or biochemical material inclusions that are found in different samples. We therefore recommend the application of both covariance-PCA and correlation-PCA on datasets, whenever applicable, to enable a broader classification of spectroscopic information through data mining and exploration.

Data Mining, Principal Component Analysis, ATR-FTIR, Polymeric Materials, Chemometrics

Pumure, I. , Ford, S. , Shannon, J. , Kohen, C. , Mulcahy, A. , Frank, K. , Sisco, S. and Chaukura, N. (2015) Analysis of ATR-FTIR Absorption-Reflection Data from 13 Polymeric Fabric Materials Using Chemometrics. American Journal of Analytical Chemistry, 6, 305-312. doi: 10.4236/ajac.2015.64029.