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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| Format: | Article |
| Language: | English |
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Penerbit UMP
2021
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| Online Access: | http://umpir.ump.edu.my/id/eprint/34001/1/The%20classification%20of%20FTIR%20plastic%20bag%20spectra.pdf |
| _version_ | 1796995069574643712 |
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| author | Almanifi, Omair Rashed Abdulwareth Ng, Jee Kwan Anwar P. P., Abdul Majeed |
| author_facet | Almanifi, Omair Rashed Abdulwareth Ng, Jee Kwan Anwar P. P., Abdul Majeed |
| author_sort | Almanifi, Omair Rashed Abdulwareth |
| collection | UMP |
| description | 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. |
| first_indexed | 2024-03-06T12:56:53Z |
| format | Article |
| id | UMPir34001 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T12:56:53Z |
| publishDate | 2021 |
| publisher | Penerbit UMP |
| record_format | dspace |
| spelling | UMPir340012022-05-09T08:00:47Z http://umpir.ump.edu.my/id/eprint/34001/ The classification of FTIR plastic bag spectra via label spreading and stacking Almanifi, Omair Rashed Abdulwareth Ng, Jee Kwan Anwar P. P., Abdul Majeed TJ Mechanical engineering and machinery TS Manufactures 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. Penerbit UMP 2021 Article PeerReviewed pdf en cc_by_nc_4 http://umpir.ump.edu.my/id/eprint/34001/1/The%20classification%20of%20FTIR%20plastic%20bag%20spectra.pdf Almanifi, Omair Rashed Abdulwareth and Ng, Jee Kwan and Anwar P. P., Abdul Majeed (2021) The classification of FTIR plastic bag spectra via label spreading and stacking. Mekatronika - Journal of Intelligent Manufacturing & Mechatronics, 3 (2). pp. 70-76. ISSN 2637-0883. (Published) https://doi.org/10.15282/mekatronika.v3i2.7390 https://doi.org/10.15282/mekatronika.v3i2.7390 |
| spellingShingle | TJ Mechanical engineering and machinery TS Manufactures Almanifi, Omair Rashed Abdulwareth Ng, Jee Kwan Anwar P. P., Abdul Majeed The classification of FTIR plastic bag spectra via label spreading and stacking |
| title | The classification of FTIR plastic bag spectra via label spreading and stacking |
| title_full | The classification of FTIR plastic bag spectra via label spreading and stacking |
| title_fullStr | The classification of FTIR plastic bag spectra via label spreading and stacking |
| title_full_unstemmed | The classification of FTIR plastic bag spectra via label spreading and stacking |
| title_short | The classification of FTIR plastic bag spectra via label spreading and stacking |
| title_sort | classification of ftir plastic bag spectra via label spreading and stacking |
| topic | TJ Mechanical engineering and machinery TS Manufactures |
| url | http://umpir.ump.edu.my/id/eprint/34001/1/The%20classification%20of%20FTIR%20plastic%20bag%20spectra.pdf |
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