Combining LIBS and Raman data for analysis of plastic type mixtures

presentation, poster

en

With increasing amount of plastic waste, the need for its proper identification arises. Techniques used for identification of plastic types today are mainly manual sorting and infrared spectroscopies. Infrared spectroscopies are reliable for identification of manufactured plastics in laboratory. For analysis of post-consumer plastic waste, the usage of infrared spectroscopies is problematic. Laser Induced Breakdown spectroscopy (LIBS) in combination with Raman spectroscopy can be used in situ, stand-off, and faster than infrared spectroscopies. This work focuses on classification of plastic-type mixtures, as this combination of matrices can often be encountered in post-consumer plastic waste. In our experiment, we focus on two types of samples – mixture of polycarbonate (PC) and polystyrene (PS) and mixture of polypropylene (PP) and polyethylene (PE). Every sample set (a mixture of named polymers) is measured on LIBS and Raman systems individually and acquired datasets are fused in data post-processing. The fused dataset is then analyzed using standard chemometric techniques such as Principal Component Analysis (PCA) or Soft independent modelling by class analogy (SIMCA). The classification is accurate and the distinction between individual samples is clear. In addition, the plastic type mixture data is predicted using Self-organizing maps (SOM). The predicted data has high correlation to measured data. The work shows straightforward analysis of selected plastic type mixtures with high accuracy, which has a potential for further usage.

With increasing amount of plastic waste, the need for its proper identification arises. Techniques used for identification of plastic types today are mainly manual sorting and infrared spectroscopies. Infrared spectroscopies are reliable for identification of manufactured plastics in laboratory. For analysis of post-consumer plastic waste, the usage of infrared spectroscopies is problematic. Laser Induced Breakdown spectroscopy (LIBS) in combination with Raman spectroscopy can be used in situ, stand-off, and faster than infrared spectroscopies. This work focuses on classification of plastic-type mixtures, as this combination of matrices can often be encountered in post-consumer plastic waste. In our experiment, we focus on two types of samples – mixture of polycarbonate (PC) and polystyrene (PS) and mixture of polypropylene (PP) and polyethylene (PE). Every sample set (a mixture of named polymers) is measured on LIBS and Raman systems individually and acquired datasets are fused in data post-processing. The fused dataset is then analyzed using standard chemometric techniques such as Principal Component Analysis (PCA) or Soft independent modelling by class analogy (SIMCA). The classification is accurate and the distinction between individual samples is clear. In addition, the plastic type mixture data is predicted using Self-organizing maps (SOM). The predicted data has high correlation to measured data. The work shows straightforward analysis of selected plastic type mixtures with high accuracy, which has a potential for further usage.

LIBS, Raman, Plastics, Chemometrics, PCA, SOM

30.11.2021