Towards Universal Calibration for Infrared Spectroscopy with Probabilistic Interference Removal
10 affiliations
- 1Swiss Data Science Center
- 2EPFL and ETH Zürich
- 3Air Quality Research Center
- 4University of California
- 5Aerospec SA
- 6Center for the Study of Air Quality and Climate Change
- 7Foundation for Research and Technology Hellas
- 8Laboratory for Atmospheric Processes and their Impacts
- 9École Polytechnique Fédérale de Lausanne
- 10Laboratory for Environmental Spectrochemistry
Abstract
Structured interferences from substrates, matrices, and instrumental artifacts in mid-infrared (IR) spectra often overlap with analyte features and introduce errors in quantitative analysis. Existing correction methods rely on heuristic parameter selection and frequently perform suboptimally when applied across a diverse set of samples. We present a probabilistic framework, Variational-Inference-based Background Elimination in Spectroscopy (VIBES), for automated estimation of regularization hyperparameters from the data that enables removal of such interferences. Background contributions are modeled using covariance structures learned from analytical blank measurements, analyte signals as residual components from background removal under appropriate probabilistic assumptions that promote robustness and hyperparameters calibrated by maximizing a variational approximation to the model evidence. VIBES is demonstrated on two types of transmission-mode measurements with different types of background variations and analyte features, and shows capability to isolate the analyte absorption spectrum with high correlation (r > 0.95) between absorbance and mass loading across sharp and broad peak spectra. Interference-corrected spectra are furthermore shown to be consistent with absorption coefficients derived from complex refractive indices acquired from other measurement modalities. The possibility for quantitative extraction of analyte spectra in the presence of various interferences suggests a path toward development of universal calibrations for mid-IR spectroscopy.
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BibTeX
@misc{kamper2026towards,
title = {{Towards Universal Calibration for Infrared Spectroscopy with Probabilistic Interference Removal}},
author = {Francois Kamper and Ekaterina Krymova and Ann M. Dillner and Andrea Baccarini and Nikunj Dudani and Athanasios Nenes and Guillaume Obozinski and Satoshi Takahama},
howpublished = {ChemRxiv},
year = {2026},
month = sep,
doi = {10.26434/chemrxiv.15009188/v1},
url = {https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009188/v1},
note = {CC BY},
}
RIS
TY - UNPB
TI - Towards Universal Calibration for Infrared Spectroscopy with Probabilistic Interference Removal
AU - Francois Kamper
AU - Ekaterina Krymova
AU - Ann M. Dillner
AU - Andrea Baccarini
AU - Nikunj Dudani
AU - Athanasios Nenes
AU - Guillaume Obozinski
AU - Satoshi Takahama
PY - 2026
DA - 2026/09/21
PB - ChemRxiv
DO - 10.26434/chemrxiv.15009188/v1
UR - https://chemrxiv.org/doi/full/10.26434/chemrxiv.15009188/v1
AB - Structured interferences from substrates, matrices, and instrumental artifacts in mid-infrared (IR) spectra often overlap with analyte features and introduce errors in quantitative analysis. Existing correction methods rely on heuristic parameter selection and frequently perform suboptimally when applied across a diverse set of samples. We present a probabilistic framework, Variational-Inference-based Background Elimination in Spectroscopy (VIBES), for automated estimation of regularization hyperparameters from the data that enables removal of such interferences. Background contributions are modeled using covariance structures learned from analytical blank measurements, analyte signals as residual components from background removal under appropriate probabilistic assumptions that promote robustness and hyperparameters calibrated by maximizing a variational approximation to the model evidence. VIBES is demonstrated on two types of transmission-mode measurements with different types of background variations and analyte features, and shows capability to isolate the analyte absorption spectrum with high correlation (r > 0.95) between absorbance and mass loading across sharp and broad peak spectra. Interference-corrected spectra are furthermore shown to be consistent with absorption coefficients derived from complex refractive indices acquired from other measurement modalities. The possibility for quantitative extraction of analyte spectra in the presence of various interferences suggests a path toward development of universal calibrations for mid-IR spectroscopy.
ER -