Rapid classification of virgin and recycled EPS containers by Fourier transform infrared spectroscopy and chemometrics

ABSTRACT

A rapid and sensitive method for classification of virgin and recycled expanded polystyrene (EPS) food containers was developed using Fourier transform infrared spectroscopy (FTIR) and chemometrics. This method includes preparing a transparent film by dissolution, examining by FTIR and developing classification models. The degradation of EPS containers occurring during the recycling process was reflected by the carbonyl region of the infrared spectrum which was used as variables for multivariate data analysis. PCA was used to reduce the data dimension and view the sample similarities. Soft independent modelling of class analogy (SIMCA), partial least squares-discrimination analysis (PLS-DA) and linear discrimination analysis (LDA) were applied to construct three classification models. The best discrimination results were obtained by an LDA model, with all samples correctly classified. PLS-DA and SIMCA could not classify the recycled EPS samples with low levels of adulteration. When applying this method to commercially available EPS containers, about 45% of samples were shown to contain recycled polystyrene resins. It is concluded that the carbonyl region of the infrared spectra coupled with chemometrics could be a powerful tool for the classification of virgin and recycled EPS food containers.

KEYWORDS:

• Recycled polystyrene
• classification
• FTIR
• chemometrics

Acknowledgments

This study was financially supported by the Natural Science Foundation of Guangdong Province, China (2015A030313329), Research Funding Program of General Administration of Quality Supervision, Inspection and Quarantine, China (2017IK133), and Zhuhai Premier-Discipline Enhancement Scheme.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the Natural Science Foundation of Guangdong Province, China [2015A030313329]; Research Funding Program of General Administration of Quality Supervision, Inspection and Quarantine, China [2017IK133].