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Qualitative and Quantitative Multivariate Analysis of Optical Density Spectra of Plastic Waste
Abstract
5 types of plastics were classified based on NIR spectra of optical density using multivariate cluster analysis in principal component space. Accuracy above 0.96 is obtained by ranking spectral variables by decreasing variance of measured optical density. Changes in polycarbonate VIS-NIR spectra during thermal degradation were modeled using partial least squares method with searching combination moving windows of optimal width. A quantitative calibration of the aging term up to 11.5 years was obtained with a root mean square error of the estimate around 19 days and a residual predictive deviation of more than 45.
Keywords
absorption spectrum,
principal component analysis,
spectral variables selection,
k nearest neighbors,
hierarchical cluster analysis,
partial least squares,
polymers
Supporting agencies: The work was carried out with the financial support of the Belarusian Republican Foundation for Basic Research (grant no. F22-031)
About the Authors
P. A. Kulikovskaya
B. I. Stepanov Institute of Physics of the National Academy
of Sciences of Belarus
Belarus
Belarus
Minsk
M. A. Khodasevich
B. I. Stepanov Institute of Physics of the National Academy
of Sciences of Belarus
Belarus
Belarus
Minsk
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Review
For citations:
Kulikovskaya P.A., Khodasevich M.A. Qualitative and Quantitative Multivariate Analysis of Optical Density Spectra of Plastic Waste. Zhurnal Prikladnoii Spektroskopii. 2024;91(5):714-722. (In Russ.)
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