This study presents a possible application of Fourier transform infrared (FTIR) spectrometry and multivariate data analysis, principal component analysis (PCA), and partial least squares-discriminant analysis (PLS-DA) for classifying asbestos and their nonasbestiform analogues. The objectives of the study are: 1) to classify six regulated asbestos types and 2) to classify between asbestos types and their nonasbestiform analogues. The respirable fraction of six regulated asbestos types and their nonasbestiform analogues were prepared in potassium bromide pellets and collected on polyvinyl chloride membrane filters for FTIR measurement. Both PCA and PLS-DA classified asbestos types and their nonasbestiform analogues on the score plots showed a very distinct clustering of samples between the serpentine (chrysotile) and amphibole groups. The PLS-DA model provided ∼95% correct prediction with a single asbestos type in the sample, although it did not provide all correct predictions for all the challenge samples due to their inherent complexity and the limited sample number. Further studies are necessary for a better prediction level in real samples and standardization of sampling and analysis procedures.
Keywords: Asbestos classification; FTIR; Partial least squares-discriminant analysis; Principal component analysis.
Published by Elsevier B.V.