The Classification of FTIR Plastic Bag Spectra via Label Spreading and Stacking

Authors

  • Omair Rashed Abdulwareth Almanifi Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Malaysia
  • Jee Kwan Ng IDIR Solutions, Jalan Kpk 1/2, Kawasan Perindustrian Kundang, 48020 Rawang, Selangor
  • Anwar P. P. Abdul Majeed Innovative Manufacturing, Mechatronics and Sports Laboratory, Faculty of Manufacturing and Mechatronic Engineering Technology, Universiti Malaysia Pahang, Malaysia

DOI:

https://doi.org/10.15282/mekatronika.v3i2.7390

Keywords:

FTIR, Spectroscopy, Machine learning, Semisupervised learning, Plastic

Abstract

Whereas plastics are a group of the most useful materials, widely used in all walks of life, the plastic waste that is produced daily poses a great threat towards wildlife and the planet as a whole. The use of biodegradable plastics is an important step in combating the plastic crisis. FTIR spectroscopy is a non-destructive method used for identifying different types of materials, however interpreting spectra produced by such spectrometers is both susceptible to human error, and time-consuming, not to mention that the industry suffers from a great of specialists, in the field of spectroscopy. Utilising machine learning as a method of filling the mentioned issue is suggested by this paper. Four pipelines were investigated, consisting of two machine learning algorithms, a stacked model that stacks the KNN, SVM and RF algorithms together, and Label spreading, as well as two different dimensionality reduction methods namely; SVD and UMAP. The pipelines studied seemed to show great predictivity at 100% classification accuracy acquired by the SVD-Stacked pipeline when data was sampled using an Agilent Cary 660 FTIR Spectrometer, and 99.18% by the same model when IDIR BP10 spectrometer was employed for sampling instead. The semi-supervised learning model (Label Spreading) seemed to achieve close enough accuracy at 99.82% in the case of the former dataset, and 97.54% for the latter, at a labelling rate of only 10% of the full datasets.

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Published

2021-07-31

How to Cite

[1]
O. R. A. Almanifi, J. K. Ng, and A. P. P. Abdul Majeed, “The Classification of FTIR Plastic Bag Spectra via Label Spreading and Stacking”, MEKATRONIKA, vol. 3, no. 2, pp. 70–76, Jul. 2021.

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Original Article

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