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)

References

• Geyer, R., J. R. Jambeck, and K. L. Law. 2017. Production, use, and fate of all plastics ever made. Sci. Adv. 3:e1700782. doi:10.1126/sciadv.1700782
   PubMed Web of Science ®Google Scholar
• Maddah, H. A. 2016. Polypropylene as a promising plastic: A review. Am. J. Polym. Sci. 6:1–11.
   Google Scholar
• Bora, R. R., R. Wang, and F. You. 2020. Waste polypropylene plastic recycling toward climate change mitigation and circular economy: Energy, environmental, and technoeconomic perspectives. ACS Sustain. Chem. Eng. 8:16350–16363. doi:10.1021/acssuschemeng.0c06311
   Web of Science ®Google Scholar
• Luijsterburg, B. 2015. Mechanical Recycling of Plastic Packaging Waste. Chemical Engineering and Chemistry. Eindhoven, Netherlands: Technische Universiteit Eindhoven.
   Google Scholar
• Karian, H. 2003. Handbook of Polypropylene and Polypropylene Composites, Revised and Expanded. New York, NY: Marcel Dekker, Inc.
   Google Scholar
• Larsen, Å. G., K. Olafsen, and B. Alcock. 2021. Determining the PE fraction in recycled PP. Polym. Test 96:107058. doi:10.1016/j.polymertesting.2021.107058
   Web of Science ®Google Scholar
• Jose, S., A. S. Aprem, B. Francis, M. C. Chandy, P. Werner, V. Alstaedt, and S. Thomas. 2004. Phase morphology, crystallisation behaviour and mechanical properties of isotactic polypropylene/high density polyethylene blends. Eur. Polym. J. 40:2105–2115. doi:10.1016/j.eurpolymj.2004.02.026
   Web of Science ®Google Scholar
• Camacho, W., and S. Karlsson. 2001. NIR, DSC, and FTIR as quantitative methods for compositional analysis of blends of polymers obtained from recycled mixed plastic waste. Polym. Eng. Sci. 41:1626–1635. doi:10.1002/pen.10860
   Web of Science ®Google Scholar
• Workman, J. 1993. A brief review of the near infrared measurement technique. NIR News 4:8–16. doi: 10.1255/nirn.218
   Google Scholar
• Wang, P., K. Zhan, X. Wang, Y. Peng, and S. Liu. 2023. Comparison of NIR and Raman spectrometries as quantitative methods to monitor polyethylene content in recycled polypropylene. Polym. Test 119:107938. doi:10.1016/j.polymertesting.2023.107938
   Web of Science ®Google Scholar
• Geladi, P., and B. R. Kowalski. 1986. Partial least-squares regression: a tutorial. Anal. Chim. Acta 185:1–17. doi:10.1016/0003-2670(86)80028-9
   Web of Science ®Google Scholar
• Siegel, A. F., and M. R. Wagner. 2022. Practical Business Statistics, 8th ed. eds. A. F. Siegel and M. R. Wagner, pp. 313–370. New York, NY: McGraw-Hill Education.
   Google Scholar
• Fearn, T., C. Riccioli, A. Garrido-Varo, and J. E. Guerrero-Ginel. 2009. On the geometry of SNV and MSC. Chemometr. Intellig. Lab. Syst. 96:22–26. doi:10.1016/j.chemolab.2008.11.006
   Web of Science ®Google Scholar
• Chu, X., H. Yuan, and W. Lu. 2004. Progress and application of spectral data pretreatment and wavelength selection methods in NIR analytical technique. Prog. Chem. 16:528–542.
   Web of Science ®Google Scholar
• Barnes, R. J., M. S. Dhanoa, and S. J. Lister. 1989. Standard normal variate transformation and de-trending of near-infrared diffuse reflectance spectra. Appl. Spectrosc. 43:772–777. doi:10.1366/0003702894202201
   Web of Science ®Google Scholar
• Nørgaard, L., A. Saudland, J. Wagner, J. P. Nielsen, L. Munck, and S. B. Engelsen. 2000. Interval partial least-squares regression (iPLS): a comparative chemometric study with an example from near-infrared spectroscopy. Appl. Spectrosc. 54:413–419. doi:10.1366/0003702001949500
   Web of Science ®Google Scholar
• Leardi, R., and L. Nørgaard. 2004. Sequential application of backward interval partial least squares and genetic algorithms for the selection of relevant spectral regions. J. Chemometr. J. Chemometr. Soc. 18:486–497. doi:10.1002/cem.893
   Web of Science ®Google Scholar
• Billard, L., and J. Le-Rademacher. 2012. Principal component analysis for interval data. WIREs Comput. Stats. 4:535–540. doi:10.1002/wics.1231
   Google Scholar
• Kucheryavskiy, S. 2020. Mdatools–R package for chemometrics. Chemometr. Intellig. Lab. Syst. 198:103937. doi:10.1016/j.chemolab.2020.103937
   Web of Science ®Google Scholar
• Guo, Q., L. Nie, L. Li, and H. Zang. 2017. Estimation of the critical quality attributes for hydroxypropyl methylcellulose with near-infrared spectroscopy and chemometrics. Spectrochim. Acta. A Mol. Biomol. Spectrosc. 177:158–163. doi:10.1016/j.saa.2017.01.012
   PubMed Web of Science ®Google Scholar
• Li, G., W. Lao, T. Qin, and L. Huang. 2015. Rapid determination of biomass and polypropylene in three types of wood plastic composites (WPCs) using FTIR spectroscopy and partial least squares regression (PLSR). Holzforschung. 69:399–404. doi:10.1515/hf-2014-0157
   Web of Science ®Google Scholar
• Ran, Z., L. Sun, Y. Liu, X. Pan, J. Li, and Y. Liu. 2020. Forward and backward interval partial least squares method for quantitative analysis of frying oil quality. Infrared Phys. Technol. 105:103207. doi:10.1016/j.infrared.2020.103207
   Web of Science ®Google Scholar