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Comparison of Several Methods of Chromatographic Baseline Removal with a New Approach Based on Quantile Regression.

Komsta L - Chromatographia (2011)

Bottom Line: It is compared with current methods based on polynomial fitting, spline fitting, LOESS, and Whittaker smoother, each with thresholding and reweighting approach.For curve flexibility selection in existing algorithms, a new method based on skewness of the residuals is successfully applied.The newly introduced methods could be preferred to visible better performance and short computational time.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicinal Chemistry, Medical University of Lublin, Jaczewskiego 4, 20-090 Lublin, Poland.

ABSTRACT
The article is intended to introduce and discuss a new quantile regression method for baseline detrending of chromatographic signals. It is compared with current methods based on polynomial fitting, spline fitting, LOESS, and Whittaker smoother, each with thresholding and reweighting approach. For curve flexibility selection in existing algorithms, a new method based on skewness of the residuals is successfully applied. The computational efficiency of all approaches is also discussed. The newly introduced methods could be preferred to visible better performance and short computational time. The other algorithms behave in comparable way, and polynomial regression can be here preferred due to short computational time.

No MeSH data available.


Related in: MedlinePlus

Examples of artificial signals used in the study
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Fig2: Examples of artificial signals used in the study

Mentions: 10000 very complex simulated signals of length 2000 were generated (random examples are shown in Fig. 2). Each signal consisted of:Fig. 2


Comparison of Several Methods of Chromatographic Baseline Removal with a New Approach Based on Quantile Regression.

Komsta L - Chromatographia (2011)

Examples of artificial signals used in the study
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC3064906&req=5

Fig2: Examples of artificial signals used in the study
Mentions: 10000 very complex simulated signals of length 2000 were generated (random examples are shown in Fig. 2). Each signal consisted of:Fig. 2

Bottom Line: It is compared with current methods based on polynomial fitting, spline fitting, LOESS, and Whittaker smoother, each with thresholding and reweighting approach.For curve flexibility selection in existing algorithms, a new method based on skewness of the residuals is successfully applied.The newly introduced methods could be preferred to visible better performance and short computational time.

View Article: PubMed Central - PubMed

Affiliation: Department of Medicinal Chemistry, Medical University of Lublin, Jaczewskiego 4, 20-090 Lublin, Poland.

ABSTRACT
The article is intended to introduce and discuss a new quantile regression method for baseline detrending of chromatographic signals. It is compared with current methods based on polynomial fitting, spline fitting, LOESS, and Whittaker smoother, each with thresholding and reweighting approach. For curve flexibility selection in existing algorithms, a new method based on skewness of the residuals is successfully applied. The computational efficiency of all approaches is also discussed. The newly introduced methods could be preferred to visible better performance and short computational time. The other algorithms behave in comparable way, and polynomial regression can be here preferred due to short computational time.

No MeSH data available.


Related in: MedlinePlus