Untargeted selected ion flow tube mass spectrometry headspace analysis: High-throughput differentiation of virgin and recycled polyethylene pellets.

Abstract

Recycled plastics are increasingly used for packaging of fast-moving consumer goods (FMCG). Compared with packaging made from virgin polymers, there is greater risk of taints entering products due to prior use of the polymers and incomplete cleaning. Increased quality assurance testing of polymer feedstock is required for recycled packaging. Selected ion flow tube mass spectrometry (SIFT-MS) analysis coupled with multivariate statistical data processing can provide high-throughput untargeted screening of recycled polymers at low cost per sample. SIFT-MS is a direct-injection MS technique that provides high-throughput automated headspace analysis of polymer samples when coupled with a syringe-injection autosampler (12 incubated samples per hour). Full-scan SIFT-MS data were processed using multivariate statistical analysis (specifically, the soft independent modeling by class analogy (SIMCA) algorithm). SIFT-MS full-scan data were acquired for ten replicates each of ten recycled and four virgin high-density polyethylene (HDPE) pellet products from multiple manufacturers. The samples varied approximately 20-fold in terms of total volatile residue, while showing very high repeatability across replicates. SIFT-MS scan data were dominated by aliphatic and monoterpene hydrocarbon residues, and - to a lesser extent - alcohols. Application of the SIMCA algorithm to the data resulted in successful classification by both individual samples and manufacturers. Automated, untargeted SIFT-MS analysis coupled with multivariate statistical data analysis has the potential to provide rapid, effective screening of recycled polymer products, which would provide increased quality assurance of recycled polymers used for FMCG.