Research on Baseline Extraction Method Based on NMR Spectrogram

This paper proposes a baseline extraction method based on a nuclear magnetic resonance atlas, which uses the DBSCAN algorithm to cluster the data extracted by the nuclear magnetic resonance spectrometer and determines the baseline of the atlas according to the clustering results. In this paper, the...

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Vydané v:Journal of physics. Conference series Ročník 2303; číslo 1; s. 12080 - 12085
Hlavní autori: Xiong, ShiYong, Zhu, Fuli, Xu, Daoxu
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Bristol IOP Publishing 01.07.2022
Predmet:
Algorithms
Clustering
NMR
Nuclear magnetic resonance
Outliers (statistics)
Parameters
Physics
ISSN:1742-6588, 1742-6596
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Abstract This paper proposes a baseline extraction method based on a nuclear magnetic resonance atlas, which uses the DBSCAN algorithm to cluster the data extracted by the nuclear magnetic resonance spectrometer and determines the baseline of the atlas according to the clustering results. In this paper, the core parameters of the DBSCAN algorithm are improved, and the Eps parameter set is selected by combining k-dist and equal interval division. The optimal number of outliers is obtained based on this set, and the baseline value range is set. Finally, the clustering quality is tested by the silhouette coefficient. The final experiment shows that the accuracy and stability of the baseline set based on the improved DBSCAN clustering algorithm are better.
AbstractList This paper proposes a baseline extraction method based on a nuclear magnetic resonance atlas, which uses the DBSCAN algorithm to cluster the data extracted by the nuclear magnetic resonance spectrometer and determines the baseline of the atlas according to the clustering results. In this paper, the core parameters of the DBSCAN algorithm are improved, and the Eps parameter set is selected by combining k-dist and equal interval division. The optimal number of outliers is obtained based on this set, and the baseline value range is set. Finally, the clustering quality is tested by the silhouette coefficient. The final experiment shows that the accuracy and stability of the baseline set based on the improved DBSCAN clustering algorithm are better.
Author Xiong, ShiYong
Xu, Daoxu
Zhu, Fuli
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Cites_doi 10.1016/j.jmr.2006.07.013
10.1039/b922045c
ContentType Journal Article
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SubjectTerms Algorithms
Clustering
NMR
Nuclear magnetic resonance
Outliers (statistics)
Parameters
Physics
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