Femtosecond laser-induced breakdown spectroscopy studies for the identification of plastics

Abstract

We report the identification of five extensively used post-consumer plastics using the femtosecond laser-induced breakdown spectroscopy (fs-LIBS) technique. All the samples were obtained from a local recycling unit. An ultra-fast amplifier system delivering pulses of ∼50 fs duration was used for the investigation. Initially, a 2D scatter plot approach was employed in which a pair of selected spectral features was utilized to discriminate the samples. H-CN and C2-CN plots have shown promising discrimination. Apart from bivariate studies, principal component analysis (PCA) has been employed for the exploratory analysis where excellent segregation was achieved. Further, data were analysed using a supervised algorithm, artificial neural network (ANN). For efficient identification, an ANN model was constructed and evaluated by exploiting five different spectral windows/regions. The average identification rates obtained for all samples are in the range of 97 to 99% depending on the spectral window. The correct prediction rates with 100% accuracy were achieved when 10 prominent spectral features were employed. These findings demonstrate that the fs-LIBS combined with multivariate techniques can work as an efficient spectroscopic tool for the identification of post-consumer plastics.