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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

Boxplot of decimal logarithm of MSE between real and estimated baseline for all the investigated methods with automatic criteria (a) and correlation of efficiency with computational time for all signals (b). R means reweighting, T means thresholding
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Fig4: Boxplot of decimal logarithm of MSE between real and estimated baseline for all the investigated methods with automatic criteria (a) and correlation of efficiency with computational time for all signals (b). R means reweighting, T means thresholding

Mentions: The comparison of mean squared error (MSE) between estimated and real (known in the case of artificial signals) baseline is depicted as the boxplot in Fig. 4a. The computational time is correlated with obtained error in Fig. 4b. The following conclusions can be made:Fig. 4


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

Komsta L - Chromatographia (2011)

Boxplot of decimal logarithm of MSE between real and estimated baseline for all the investigated methods with automatic criteria (a) and correlation of efficiency with computational time for all signals (b). R means reweighting, T means thresholding
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC3064906&req=5

Fig4: Boxplot of decimal logarithm of MSE between real and estimated baseline for all the investigated methods with automatic criteria (a) and correlation of efficiency with computational time for all signals (b). R means reweighting, T means thresholding
Mentions: The comparison of mean squared error (MSE) between estimated and real (known in the case of artificial signals) baseline is depicted as the boxplot in Fig. 4a. The computational time is correlated with obtained error in Fig. 4b. The following conclusions can be made:Fig. 4

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