ArticleJournal of chemical information and modeling2025
Inverse Modeling for Artifact Removal in Photonic Data: A Computational Physics and Transfer Learning-Based Approach.
Article in Journal of chemical information and modeling, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Towards the digital analytical sciences in chemistry and biochemistry: from FAIR data ecosystems to artificial intelligence.Analytical and bioanalytical chemistry · 2026Review
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Etaloning artifacts introduce notable distortions in spectroscopic data, complicating downstream analysis and interpretation. We present an inverse modeling framework that integrates computational physics with deep learning to address this challenge. Our approach employs a two-phase transfer learning strategy: pretraining on over 30,000 simulated spectra generated using the transfer matrix method and fine-tuning on real experimental data. This extensive simulated data set enhances the model's ability to generalize across different sensor designs, significantly improving robustness and accuracy. Rigorous cross-validation across multiple devices demonstrates that the transfer learning approach reduces etaloning-induced distortions by up to 70%, ensuring substantial spectral accuracy and interpretability improvements. This study sets a new standard for achieving reliable spectral data by combining correction procedures with physics simulations.
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Registered trials
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