Fuelprop: Fuel property prediction from ATR-FTIR spectroscopic data
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866916772785946624 |
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| author | Almomtan, Mohammed Ibrahim, Emad Al Farooq, Aamir |
| author_facet | Almomtan, Mohammed Ibrahim, Emad Al Farooq, Aamir |
| contents | Synthetic fuels are crucial for decarbonizing the transportation sector. A significant challenge lies in the rapid and efficient characterization of these fuels. Chemometric methods using ATR-FTIR data offer a potential alternative to conventional techniques. This study expands the applicability and performance of chemometric models by providing an extensive ATR-FTIR spectral dataset and exploring various data enhancement strategies. Data enhancement was achieved by semi-supervised data generation, consistency enforcement through unsupervised data augmentation, and data imputation using synthetic spectra blending and pseudo-labeling. Models were trained on surrogate fuels and rigorously tested on real fuels, representing out-of-distribution testing conditions. We believe that this work will enhance the adoption of chemometric models for fuel characterization. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_01601 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Fuelprop: Fuel property prediction from ATR-FTIR spectroscopic data Almomtan, Mohammed Ibrahim, Emad Al Farooq, Aamir Chemical Physics Synthetic fuels are crucial for decarbonizing the transportation sector. A significant challenge lies in the rapid and efficient characterization of these fuels. Chemometric methods using ATR-FTIR data offer a potential alternative to conventional techniques. This study expands the applicability and performance of chemometric models by providing an extensive ATR-FTIR spectral dataset and exploring various data enhancement strategies. Data enhancement was achieved by semi-supervised data generation, consistency enforcement through unsupervised data augmentation, and data imputation using synthetic spectra blending and pseudo-labeling. Models were trained on surrogate fuels and rigorously tested on real fuels, representing out-of-distribution testing conditions. We believe that this work will enhance the adoption of chemometric models for fuel characterization. |
| title | Fuelprop: Fuel property prediction from ATR-FTIR spectroscopic data |
| topic | Chemical Physics |
| url | https://arxiv.org/abs/2506.01601 |