Sparse Infrared Spectroscopy for Detection of Volatile Organic Compounds
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866912450304016384 |
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| author | Welner, Mira Hazbun, Andre Beechem, Thomas |
| author_facet | Welner, Mira Hazbun, Andre Beechem, Thomas |
| contents | To reduce the complexity of infrared spectroscopy hardware while maintaining performance, a data informed, task-specific, spectral collection approach termed Sparse Infrared Spectroscopy (SIRS) is developed. Using a numerically based virtual experiment based on a quantitatively accurate infrared database, non-negative matrix factorization is used to identify the spectral pass bands of a minimal number of filters necessary to identify volatile organic compounds (VOC) within either an inert background or mixture of gases. The data-driven approach is found capable of identifying contaminants at the 1-10 part per million level (PPM) with $\mathrm{\sim~20-50}$ spectral samples as opposed to the more than 1,000 typical of a traditional infrared spectrum. Reasonably robust to both noise and the characteristics of the base compound in a mixture, the task-specific spectral sampling points to simplified hardware design that maintains performance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_20678 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sparse Infrared Spectroscopy for Detection of Volatile Organic Compounds Welner, Mira Hazbun, Andre Beechem, Thomas Chemical Physics Data Analysis, Statistics and Probability To reduce the complexity of infrared spectroscopy hardware while maintaining performance, a data informed, task-specific, spectral collection approach termed Sparse Infrared Spectroscopy (SIRS) is developed. Using a numerically based virtual experiment based on a quantitatively accurate infrared database, non-negative matrix factorization is used to identify the spectral pass bands of a minimal number of filters necessary to identify volatile organic compounds (VOC) within either an inert background or mixture of gases. The data-driven approach is found capable of identifying contaminants at the 1-10 part per million level (PPM) with $\mathrm{\sim~20-50}$ spectral samples as opposed to the more than 1,000 typical of a traditional infrared spectrum. Reasonably robust to both noise and the characteristics of the base compound in a mixture, the task-specific spectral sampling points to simplified hardware design that maintains performance. |
| title | Sparse Infrared Spectroscopy for Detection of Volatile Organic Compounds |
| topic | Chemical Physics Data Analysis, Statistics and Probability |
| url | https://arxiv.org/abs/2506.20678 |