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arXiv · 2506.01601

Fuelprop: Fuel property prediction from ATR-FTIR spectroscopic data

Mohammed Almomtan·Emad Al Ibrahim·Aamir Farooq
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Abstract

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.

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Mohammed Almomtan, Emad Al Ibrahim, Aamir Farooq. 2025-06-02. Fuelprop: Fuel property prediction from ATR-FTIR spectroscopic data. https://arxiv.org/abs/2506.01601

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