Limits...
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

Subsampling of smoothed MALDI-TOF spectrum, reducing resolution from 4000 to 150 m/z values.
© Copyright Policy - CC BY
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC4487348&req=5

fig0010: Subsampling of smoothed MALDI-TOF spectrum, reducing resolution from 4000 to 150 m/z values.

Mentions: Instead of estimating the baselines directly on the spectra, a simple binning is performed first; see Fig. 2 and Section S2 in the R code. The number of bins is chosen by the user, and the minimum value in each bin is used as a local representative of the spectrum. The subsampling serves two goals. Firstly, it increases the efficiency of the algorithm with regard to the number of local windows it will work in, thus reducing the number of iterations needed to suppress the baseline. Secondly, it simplifies the shape of the spectrum while retaining the basic shape of the baseline.


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

Liland KH - MethodsX (2015)

Subsampling of smoothed MALDI-TOF spectrum, reducing resolution from 4000 to 150 m/z values.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

fig0010: Subsampling of smoothed MALDI-TOF spectrum, reducing resolution from 4000 to 150 m/z values.
Mentions: Instead of estimating the baselines directly on the spectra, a simple binning is performed first; see Fig. 2 and Section S2 in the R code. The number of bins is chosen by the user, and the minimum value in each bin is used as a local representative of the spectrum. The subsampling serves two goals. Firstly, it increases the efficiency of the algorithm with regard to the number of local windows it will work in, thus reducing the number of iterations needed to suppress the baseline. Secondly, it simplifies the shape of the spectrum while retaining the basic shape of the baseline.

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