The classification of FTIR plastic bag spectra via label spreading and stacking
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 spectros...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Penerbit UMP
2021
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| Subjects: | |
| Online Access: | http://umpir.ump.edu.my/id/eprint/34001/1/The%20classification%20of%20FTIR%20plastic%20bag%20spectra.pdf http://umpir.ump.edu.my/id/eprint/34001/ https://doi.org/10.15282/mekatronika.v3i2.7390 https://doi.org/10.15282/mekatronika.v3i2.7390 |
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| Institution: | Universiti Malaysia Pahang |
| Language: | English |
| Summary: | 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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