Scattering correction for infrared spectra of biological cells using computational infrared microspectroscopy and deep learning
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2026
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866917520072507392 |
|---|---|
| author | Rodrigo, Sergio G. Rasskazov, Ilia L. Martin-Moreno, Luis Schnell, Martin |
| author_facet | Rodrigo, Sergio G. Rasskazov, Ilia L. Martin-Moreno, Luis Schnell, Martin |
| contents | IR microspectroscopy of single biological cells is challenged by strong light scattering, which produces baseline effects and peak distortions in the IR spectra and hinders the direct extraction of chemical information. Current methods for scattering correction typically rely on Mie theory and are accurate only under the assumption that the cell can be approximated by a sphere. Here, we present a framework for the scattering correction of IR absorbance spectra that is based on 3D ellipsoid models and provides efficient scattering correction for both suspended (spherical) and adhered (flattened) cells. Our approach combines deep learning approaches with computational IR microspectroscopy based on the finite-difference time-domain (FDTD) method. The FDTD method generates a synthetic library of realistic training spectra, while the deep learning model enables fast spectral inversion. We demonstrate scattering correction in silico using numerical cell phantoms of cervical cancer cells (HeLa) and show that the true absorption spectra can be inferred from IR absorbance spectra. We further show that the 3D cell dimensions can be recovered from the IR absorbance spectra, highlighting that the inherent light scattering could be exploited to realize the full analytical potential of IR spectroscopy. We anticipate that deep learning-based scattering corrections can be readily extended to increasingly complex sample geometries owing to the flexibility of the FDTD method to model arbitrary geometries. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_22503 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Scattering correction for infrared spectra of biological cells using computational infrared microspectroscopy and deep learning Rodrigo, Sergio G. Rasskazov, Ilia L. Martin-Moreno, Luis Schnell, Martin Optics IR microspectroscopy of single biological cells is challenged by strong light scattering, which produces baseline effects and peak distortions in the IR spectra and hinders the direct extraction of chemical information. Current methods for scattering correction typically rely on Mie theory and are accurate only under the assumption that the cell can be approximated by a sphere. Here, we present a framework for the scattering correction of IR absorbance spectra that is based on 3D ellipsoid models and provides efficient scattering correction for both suspended (spherical) and adhered (flattened) cells. Our approach combines deep learning approaches with computational IR microspectroscopy based on the finite-difference time-domain (FDTD) method. The FDTD method generates a synthetic library of realistic training spectra, while the deep learning model enables fast spectral inversion. We demonstrate scattering correction in silico using numerical cell phantoms of cervical cancer cells (HeLa) and show that the true absorption spectra can be inferred from IR absorbance spectra. We further show that the 3D cell dimensions can be recovered from the IR absorbance spectra, highlighting that the inherent light scattering could be exploited to realize the full analytical potential of IR spectroscopy. We anticipate that deep learning-based scattering corrections can be readily extended to increasingly complex sample geometries owing to the flexibility of the FDTD method to model arbitrary geometries. |
| title | Scattering correction for infrared spectra of biological cells using computational infrared microspectroscopy and deep learning |
| topic | Optics |
| url | https://arxiv.org/abs/2605.22503 |