Analysis of ATR-FTIR Absorption-Reflection Data from 13 Polymeric Fabric Materials Using Chemometrics

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.

DOI: 10.4236/ajac.2015.64029                

Abstract

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.

Keywords

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

Conflicts of Interest

The authors declare no conflicts of interest.

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