ArticleSensors (Basel, Switzerland)2020
An Automatic Baseline Correction Method Based on the Penalized Least Squares Method.
Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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Who cites it
8 citing papers in PubMed.
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- Automated Machine-Learning-Driven Analysis of Microplastics by TGA-FTIR for Enhanced Identification and Quantification.Analytical chemistry · 2025Article
- Classification of LAMOST spectra of B-type and hot subdwarf stars using kernel support vector machine.Scientific reports · 2024Article
- Integrated LSPR Biosensing Signal Processing Strategy and Visualization Implementation.Micromachines · 2024Article
- Double-sided asymmetric method for automated fetal heart rate baseline calculation.Physical and engineering sciences in medicine · 2023Article
- Raman spectroscopy-based prediction of ofloxacin concentration in solution using a novel loss function and an improved GA-CNN model.BMC bioinformatics · 2023Article
- Intracellular calcium dynamics of lymphatic endothelial and muscle cells co-cultured in a Lymphangion-Chip under pulsatile flow.The Analyst · 2022Article
- Experimental Investigation on the Bioprotective Role of Trehalose on Glutamine Solutions by Infrared Spectroscopy.Materials (Basel, Switzerland) · 2022Article
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Authors and funding
5 authors.
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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.
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