Classification of asbestos and their nonasbestiform analogues using FTIR and multivariate data analysis

Area of Science:

• Mineralogy
• Analytical Chemistry
• Spectroscopy

Background:

• Accurate identification of asbestos is crucial for environmental and occupational health.
• Distinguishing asbestos from non-asbestos analogues requires reliable analytical techniques.

Purpose of the Study:

• To apply Fourier transform infrared (FTIR) spectrometry and multivariate data analysis for classifying asbestos.
• To differentiate between six regulated asbestos types and their nonasbestiform analogues.

Main Methods:

• Fourier transform infrared (FTIR) spectrometry was used to analyze asbestos samples.
• Multivariate data analysis, including principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA), was employed.
• Samples were prepared as potassium bromide pellets and collected on polyvinyl chloride membrane filters.

Main Results:

• Both PCA and PLS-DA demonstrated distinct clustering between serpentine (chrysotile) and amphibole asbestos groups.
• The PLS-DA model achieved approximately 95% correct prediction for single asbestos types.
• Some challenge samples showed lower prediction accuracy due to complexity and limited sample size.

Conclusions:

• FTIR spectrometry coupled with PCA and PLS-DA offers a viable method for asbestos classification.
• Further research is needed to improve prediction accuracy for real-world samples.
• Standardization of sampling and analysis procedures is recommended for broader application.