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Related Experiment Video

Updated: Aug 31, 2025

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

Published on: December 15, 2023

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

Summary

This summary is machine-generated.

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.

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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.
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.