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Baseline correction using a deep-learning model combining ResNet and UNet.

Tiejun Chen¹, YoungJae Son¹, Aaron Park¹

¹Department of ICT Convergence System Engineering, Chonnam National University, Gwangju 61186, South Korea. tozero@jnu.ac.kr.

|August 24, 2022

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This study introduces an automated deep learning method for spectral baseline correction, improving accuracy and ease of use for infrared, Raman, and mass spectroscopy data analysis.

Area of Science:

Spectroscopy
Data Analysis
Machine Learning

Background:

Spectral data often suffers from baseline drifts caused by fluorescence, impacting analysis.
Existing baseline correction methods typically require manual parameter tuning for optimal results.

Purpose of the Study:

To develop an automated baseline correction method for spectral data.
To eliminate the need for manual parameter adjustments in baseline correction.

Main Methods:

A deep learning model combining ResNet and UNet architectures was developed.
The model was trained using simulated spectral data for baseline correction.

Main Results:

The proposed deep learning method demonstrated superior performance in qualitative and quantitative analyses.

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The method showed improved ease of application compared to existing techniques.

Effective baseline correction was achieved on both simulated and actual Raman spectra.

Conclusions:

The novel deep learning approach offers an automated and effective solution for spectral baseline correction.
This method is applicable to various spectral types, including Raman, with potential for wide adoption.