We used both correlation and covariance principal component analysis (PCA) to classify Attenuated total reflectance �Infrared spectroscopy (ATR-FTIR) absorption-reflectance data from 13 different polymeric fabric materials. Correlation PCA enabled the classification of the fabric materials according to their organic functional groups. On the other hand, covariance PCA was used to classify the fabric materials according to their origins.  Surface chemical compositions of the different fabric materials were also monitored as a function of exposure to environmental conditions and accelerated degradation through the application of ultrasound energy and elevated temperatures. We therefore recommended the application of both covariance PCA and correlation PCA on datasets, wherever applicable, to enable broader and wider classification analytical information.

Comparison of covariance-PCA and correlation-PCA: A study on the chemical composition of thirteen different polymeric fabric materials

245th National Meeting (2013)

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