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Updated: Dec 24, 2025

Design and Application of a Fault Detection Method Based on Adaptive Filters and Rotational Speed Estimation for an Electro-Hydrostatic Actuator
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An Automatic Baseline Correction Method Based on the Penalized Least Squares Method.

Feng Zhang1, Xiaojun Tang1, Angxin Tong1

  • 1State Key Laboratory of Electrical Insulation & Power Equipment, Xi'an Jiaotong University, Xi'an 710049, China.

Sensors (Basel, Switzerland)
|April 9, 2020
PubMed
Summary

This study introduces an automatic baseline correction method for spectral analysis. The novel approach uses adaptive smoothness parameter penalized least squares (asPLS) to accurately estimate baselines, improving spectral data reliability.

Keywords:
automated baseline correctioninfrared spectrapenalized least squares

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Area of Science:

  • Analytical Chemistry
  • Spectroscopy
  • Chemometrics

Background:

  • Baseline drift in spectra significantly compromises quantitative and qualitative analysis.
  • Existing penalized least squares methods require manual parameter optimization, introducing potential inaccuracies.
  • A need exists for automated, robust baseline correction techniques in spectral data processing.

Discussion:

  • A novel automatic baseline correction method is proposed, utilizing adaptive smoothness parameter penalized least squares (asPLS).
  • The method involves signal extension with a Gaussian peak and adaptive selection of the smoothing parameter (λ) based on minimizing root-mean-square error (RMSE).
  • This approach automates the parameter optimization process inherent in penalized least squares methods.

Key Insights:

  • The proposed method effectively corrects baseline drift in various spectral types without user intervention.
  • Automatic selection of the optimal smoothing parameter (λ) enhances accuracy and reproducibility.
  • Validated on simulated and real infrared spectra, demonstrating robustness.

Outlook:

  • Potential for integration into various spectroscopic analysis software.
  • Further research could explore applicability to other spectroscopic techniques and complex spectral matrices.
  • This automated approach can significantly reduce analysis time and potential for human error.