Document Type : Original Article
Department of Physics, Faculty of Mathematics and Natural Sciences Universitas Syiah Kuala, Banda Aceh, Indonesia
Graduate School of Mathematics and Applied Sciences, Universitas Syiah Kuala, Banda Aceh, Indonesia
Growing plastic waste emission as a planetary threat urges the development of a rapid and efficient recycling process, especially during the classification process. Herein, we aimed to solve the problem by employing laser-induced breakdown spectroscopy (LIBS) in combination with principal component analysis (PCA) as means for automated plastic waste classification. Samples used in the study were plastic wastes derived from beverage, food, and stationery products of different brands. The Nd:YAG laser was shot to the sample surface without a pre-treatment and under an open-air system (laser energy= 54 mJ; time delay= 1-2 μs). The spectral profile of each plastic waste revealed the presence of metal components such as those indicated by Ca II 396.85 nm, Al I 395.92 nm, Mg I 383.83 nm, and Fe I 404.85 emission lines. Peak intensities of organic material-related emission lines (C I 247.86 nm, O II 777.32 nm, O I 844.48 nm, H I 666.22 nm, N II 818.83 nm, and N II 821.62 nm) were revealed fluctuating, suggesting that a mere LIBS spectral analysis could not discriminate the plastic waste. PCA analysis revealed that C2 molecular band 490—520 nm had the most discriminative properties against polyethylene terephthalate (PET) and polypropylene (PP). The molecular band was generated differently between PET and PP because of their contrast thermal behavior. In conclusion, molecular LIBS-PCA could be used to distinguish PET and PP in a simple and rapid way.