ArticleCommunications chemistry2022
Deep learning-enabled Inference of 3D molecular absorption distribution of biological cells from IR spectra.
Article in Communications chemistry, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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Who cites it
6 citing papers in PubMed.
- Vibrational spectroscopy methodology for profiling of mixed carbon metabolism in carotenogenic and oleaginous Mucor circinelloides.Microbial cell factories · 2026Article
- A Universal Approach to Mie Scatter Correction in FTIR Analysis of Microsized Samples.ACS omega · 2025Article
- Application of vibrational spectroscopies as process analytical techniques for monitoring fermentation and the conversion of lignocellulosic biomass by oleaginous filamentous fungi.Microbial cell factories · 2025Article
- Calibration for Quantitative Chemical Analysis in IR Microscopic Imaging.Analytical chemistry · 2025Article
- Inverse reconstruction of model cells: Extracting structural and molecular insights through infrared spectroscopic cytology.PloS one · 2025Article
- Sparse wavelengths data in mid-infrared spectroscopy: Modelling approaches and channel sampling.Journal of biophotonics · 2023Article
Corrections and comments
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Authors and funding
12 authors.
Funding
Abstract
Infrared spectroscopy delivers abundant information about the chemical composition, as well as the structural and optical properties of intact samples in a non-destructive manner. We present a deep convolutional neural network which exploits all of this information and solves full-wave inverse scattering problems and thereby obtains the 3D optical, structural and chemical properties from infrared spectroscopic measurements of intact micro-samples. The proposed model encodes scatter-distorted infrared spectra and infers the distribution of the complex refractive index function of concentrically spherical samples, such as many biological cells. The approach delivers simultaneously the molecular absorption, sample morphology and effective refractive index in both the cell wall and interior from a single measured spectrum. The model is trained on simulated scatter-distorted spectra, where absorption in the distinct layers is simulated and the scatter-distorted spectra are estimated by analytic solutions of Maxwell's equations for samples of different sizes. This allows for essentially real-time deep learning-enabled infrared diffraction micro-tomography, for a large subset of biological cells.
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Registered trials
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