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4S Peak Filling - baseline estimation by iterative mean suppression.

Liland KH - MethodsX (2015)

Bottom Line: A novel baseline estimation procedure building on previously published works is presented. •The core of the estimation is an iterative spectrum suppression consisting of a moving window minimum replacement (adapted from Friedrichs [1]).•Four, easily understandable, parameters control placement of the baseline relative to the noise band around the signal (adapted from Eilers [2]) and the flexibility in different situations.•The method is especially suited for non-linear baselines with local variations and for resolving peak clusters in qualitative analyses.

View Article: PubMed Central - PubMed

Affiliation: Norwegian University of Life Sciences, 1430 Ås, Norway ; Nofima - Norwegian Institute of Food, Fisheries and Aquaculture Research, 1432 Ås, Norway.

ABSTRACT
A novel baseline estimation procedure building on previously published works is presented. •The core of the estimation is an iterative spectrum suppression consisting of a moving window minimum replacement (adapted from Friedrichs [1]).•Four, easily understandable, parameters control placement of the baseline relative to the noise band around the signal (adapted from Eilers [2]) and the flexibility in different situations.•The method is especially suited for non-linear baselines with local variations and for resolving peak clusters in qualitative analyses.

No MeSH data available.


Related in: MedlinePlus

Baseline stretched by smoothing splines after smoothing, subsampling and suppression of MALDI-TOF spectrum.
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fig0020: Baseline stretched by smoothing splines after smoothing, subsampling and suppression of MALDI-TOF spectrum.

Mentions: The final stage of the algorithm consists of interpolating the estimated baseline back to full spectrum length; see Fig. 4 and Section S4 in the R code. Because of the choice of minimum values, we can place the estimated baseline at the centre points of the buckets and find the remaining values using simple, linear interpolation for speed and robustness or a set of properly constrained smoothing splines for better smoothness.


4S Peak Filling - baseline estimation by iterative mean suppression.

Liland KH - MethodsX (2015)

Baseline stretched by smoothing splines after smoothing, subsampling and suppression of MALDI-TOF spectrum.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4487348&req=5

fig0020: Baseline stretched by smoothing splines after smoothing, subsampling and suppression of MALDI-TOF spectrum.
Mentions: The final stage of the algorithm consists of interpolating the estimated baseline back to full spectrum length; see Fig. 4 and Section S4 in the R code. Because of the choice of minimum values, we can place the estimated baseline at the centre points of the buckets and find the remaining values using simple, linear interpolation for speed and robustness or a set of properly constrained smoothing splines for better smoothness.

Bottom Line: A novel baseline estimation procedure building on previously published works is presented. •The core of the estimation is an iterative spectrum suppression consisting of a moving window minimum replacement (adapted from Friedrichs [1]).•Four, easily understandable, parameters control placement of the baseline relative to the noise band around the signal (adapted from Eilers [2]) and the flexibility in different situations.•The method is especially suited for non-linear baselines with local variations and for resolving peak clusters in qualitative analyses.

View Article: PubMed Central - PubMed

Affiliation: Norwegian University of Life Sciences, 1430 Ås, Norway ; Nofima - Norwegian Institute of Food, Fisheries and Aquaculture Research, 1432 Ås, Norway.

ABSTRACT
A novel baseline estimation procedure building on previously published works is presented. •The core of the estimation is an iterative spectrum suppression consisting of a moving window minimum replacement (adapted from Friedrichs [1]).•Four, easily understandable, parameters control placement of the baseline relative to the noise band around the signal (adapted from Eilers [2]) and the flexibility in different situations.•The method is especially suited for non-linear baselines with local variations and for resolving peak clusters in qualitative analyses.

No MeSH data available.


Related in: MedlinePlus