Post-Consumer Plastic Identification Using Raman Spectroscopy

Abstract

Raman spectroscopy is evaluated as a spectroscopic method for identification of common household plastics for recycling purposes. The methods of K-nearest neighbor (KNN), cyclic subspace regression (CSR), and library searching are compared for computerized plastic classification. Plastics studied consist of polyethylene terephthalate, high-density polyethylene, polyvinyl chloride, low-density polyethylene, polypropylene, and polystyrene. With principal component analysis (PCA), visual distinction between the different plastics becomes possible. Correct class membership to all six plastic types is provided by KNN. To date, all development and uses of CSR have been based on building models for each prediction property analogous to the form of partial least-squares known as PLS1. Cyclic subspace regression is modified in this paper to also allow modeling of multiple properties, as does PLS2. The new form of CSR was able to correctly classify all six plastic types when seven-factor models were used. This paper reports that key observations made in comparing PCR to PLS1 are verified for the interrelationships of PCR and PLS2 models. Most notable is that even though PLS2 uses spectral responses and plastic identifications to form factors, PLS2 eigenvector weights are not much different from PCR eigenvector weights where PCR only uses spectral responses to form eigenvector weights. Library searching showed less significant results than KNN and CSR. Regardless of the identification approach, polyethylene samples could be identified as either being high density or low density with the use of Raman spectroscopy.