Comparison of principal component regression (PCR) and partial least square regression (PLSR) modeling methods for quantifying polyethylene (PE) in recycled polypropylene (rPP) with near-infrared spectrometry (NIR)

Abstract

Recycled polypropylene (rPP) often contains a small amount of polyethylene (PE). Since polypropylene (PP) and PE are incompatible, the presence of PE compromises the performance of rPP materials and needs to be closely monitored. In our previous work, Raman and near-infrared (NIR) spectrometries were evaluated to monitor PE content in rPP with partial least square regression (PLSR) modeling. The NIR spectrometry exhibited a wider application range, but the accuracy of the prediction models might be further improved. In the current work, a different modeling method, principal component regression (PCR) was employed to analyze PE content in rPP with NIR spectrometry. Spectrum pretreatment methods, including multivariate scatter correction (MSC), standard normal variate transformation (SNV), smoothing, and first derivative, were investigated to improve the NIR spectrum quality. Forward and backward interval methods were used to optimize spectral range selection. The outcomes were compared with our previous PLSR modeling results. The highest accuracy in independent validation was achieved by a PCR model with an R² of 0.9991 and a root-mean-square error of prediction (RMSEP) of 0.1596 PE%. On the other hand, a PLSR model achieved the lowest RMSEP of 0.9712 PE% for a non-colored post-consumer rPP sample. The PCR models might be sensitive to interference and more suitable for post-industrial materials, which have a simpler chemical composition. The PLSR models might have better stability and be more suitable for complicated post-consumer samples. Both the PCR and PLSR models were successfully applied to a gray commercial rPP sample.

Keywords:

• Recycled polypropylene
• polyethylene
• NIR
• PCR
• PLSR

Acknowledgments

The authors would also like to thank KW Plastics for providing the recycled PP resins for this research.

Author contributions

P.W. conceived, designed, and performed the experiments; analyzed and interpreted the data. K.Z. conceived, designed, and performed the experiments. X.W. conceived, designed, and performed the experiments. Y.P. conceived and designed the experiments; analyzed and interpreted the data; and contributed reagents and materials. H.P. conceived, designed, and performed the experiments. Y.W. conceived and designed the experiments; analyzed and interpreted the data; and contributed reagents and materials. S.L. conceived and designed the experiments; analyzed and interpreted the data; and contributed reagents and materials. All authors contributed to the writing and review of the manuscript.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was performed under the financial assistance award 70NANB20H147 from the National Institute of Standards and Technology, U.S. Department of Commerce.