TITLE:
Analysis of ATR-FTIR Absorption-Reflection Data from 13 Polymeric Fabric Materials Using Chemometrics
AUTHORS:
Innocent Pumure, Shannon Ford, Jessica Shannon, Christopher Kohen, Amanda Mulcahy, Kelvin Frank, Sheri Sisco, Nhamo Chaukura
KEYWORDS:
Data Mining, Principal Component Analysis, ATR-FTIR, Polymeric Materials, Chemometrics
JOURNAL NAME:
American Journal of Analytical Chemistry,
Vol.6 No.4,
March
12,
2015
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.