Pumure, I., Renton, J.J. and Smart, R.B. (2011) The Interstitial Location of Selenium and Arsenic in Rocks Associated with Coal Mining Using Ultrasound Extractions and Principal Component Analysis (PCA). Journal of Hazardous Materials, 198, 151-158.
http://dx.doi.org/10.1016/j.jhazmat.2011.10.032

has been cited by the following article:

• 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.