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Zhang F, Tang X, Tong A, et al. An Automatic Baseline Correction Method Based on the Penalized Least Squares Method. Sensors (Basel). 2020;20(7)doi: 10.3390/s20072015.
Zhang, F., Tang, X., Tong, A., Wang, B., & Wang, J. (2020). An Automatic Baseline Correction Method Based on the Penalized Least Squares Method. Sensors (Basel, Switzerland), 20(7), . https://doi.org/10.3390/s20072015
Zhang, Feng, et al. "An Automatic Baseline Correction Method Based on the Penalized Least Squares Method." Sensors (Basel, Switzerland) vol. 20,7 (2020). doi: https://doi.org/10.3390/s20072015
Zhang F, Tang X, Tong A, Wang B, Wang J. An Automatic Baseline Correction Method Based on the Penalized Least Squares Method. Sensors (Basel). 2020 Apr 03;20(7). doi: 10.3390/s20072015. PMID: 32260258; PMCID: PMC7181009.
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Sensors (Basel). 2020 Apr 03;20(7). doi: 10.3390/s20072015.

An Automatic Baseline Correction Method Based on the Penalized Least Squares Method.

Sensors (Basel, Switzerland)

Feng Zhang, Xiaojun Tang, Angxin Tong, Bin Wang, Jingwei Wang

Affiliations

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

PMID: 32260258 PMCID: PMC7181009 DOI: 10.3390/s20072015

Abstract

Baseline drift spectra are used for quantitative and qualitative analysis, which can easily lead to inaccurate or even wrong results. Although there are several baseline correction methods based on penalized least squares, they all have one or more parameters that must be optimized by users. For this purpose, an automatic baseline correction method based on penalized least squares is proposed in this paper. The algorithm first linearly expands the ends of the spectrum signal, and a Gaussian peak is added to the expanded range. Then, the whole spectrum is corrected by the adaptive smoothness parameter penalized least squares (asPLS) method, that is, by turning the smoothing parameter λ of asPLS to obtain a different root-mean-square error (RMSE) in the extended range, the optimal λ is selected with minimal RMSE. Finally, the baseline of the original signal is well estimated by asPLS with the optimal λ. The paper concludes with the experimental results on the simulated spectra and measured infrared spectra, demonstrating that the proposed method can automatically deal with different types of baseline drift.

Keywords: automated baseline correction; infrared spectra; penalized least squares

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