Classification of treated wood using Fourier transform near infrared spectroscopy and multivariate data analysis

The aim of this work is to demonstrate that Fourier transform near infrared spectroscopy, with multivariate data analysis, can be used to distinguish between preservative treated wood and non-treated wood. This technique is quick, easy to use and has the potential to be non-destructive and portable. For this research, a set of calibration models for the elements boron, chromium, copper and cadmium and the organic preservative cypermethrin have been created using principal component analysis and soft independent modelling of class analogy. The actual metal contamination levels in the wood samples were determined by induction coupled plasma-atomic emission spectroscopy. It is shown that these models can be used to classify samples of wood on the basis of the presence of these chemical substances.