In the PLAST2bCLEANED project, Raman spectroscopy is researched to improve the sorting of the polymers High Impact Polystyrene (HIPS) and Acrylonitrile Butadiene Styrene (ABS) within the recycling process of Waste of Electric and Electronic Equipment (WEEE).
WEEE plastics usually contain certain additives like pigments or brominated flame retardants (BFR), which sometimes make difficult the correct identification of polymers with spectroscopic techniques. Certainly, the Raman spectrum of some WEEE samples presents a fluorescence or scattering effect that masks the Raman signal of the polymer. To overcome this technical challenge, in the PLAST2bCLEANED project Chemometrics is studied in combination with Raman spectroscopy for WEEE polymers identification.
Chemometrics is a tool that uses mathematical and statistical methods to analyse and extract relevant information from a data matrix. It is based on Multivariate Analysis (MVA), which assesses the information within many variables in the measured data to find the relationship between samples and variations in a data set.
Applied to Raman spectroscopy, MVA is used to find main patterns and correlations in spectral data in order to establish a measurable relationship between the registered spectra and the variable or characteristic object of the study. Principal Component Analysis (PCA) together with several pre-treatment algorithms are initially applied to spectral data with the objective of separating valuable information contained in the data structure from the signal due to other sources and spectral noise.
Once the spectra have been pre-processed, multivariate models are built and tested to classify analysed samples into target categories by using pattern recognition. All these techniques are being evaluated within the PLAST2bCLEANED project to analyse Raman spectral data and to define a classification model for sorting HIPS and ABS from WEEE streams.