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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

MALDI-TOF spectrum corrected by the estimated baseline. An alternative baseline is indicated which follows the shape of the peak cluster around 12,000 m/z.
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fig0025: MALDI-TOF spectrum corrected by the estimated baseline. An alternative baseline is indicated which follows the shape of the peak cluster around 12,000 m/z.

Mentions: If the baseline estimation is used for baseline correction, we have to subtract the final baseline from the original spectrum as has been done in Fig. 5. We observe that the zero line has been well centred in the noise band. The baseline was chosen to be rigid enough to retain all the small peaks, e.g. around 7000 and 8000 m/z. By using more buckets and a smaller window width it is possible to place the baseline in the peak cluster around 12,000 m/z so that the peaks are better resolved as indicated with the curved line in Fig. 5. If more localised control of the baseline flexibility is needed one has to resolve use varying bin widths along the spectra.


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

Liland KH - MethodsX (2015)

MALDI-TOF spectrum corrected by the estimated baseline. An alternative baseline is indicated which follows the shape of the peak cluster around 12,000 m/z.
© Copyright Policy - CC BY
Related In: Results  -  Collection

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

fig0025: MALDI-TOF spectrum corrected by the estimated baseline. An alternative baseline is indicated which follows the shape of the peak cluster around 12,000 m/z.
Mentions: If the baseline estimation is used for baseline correction, we have to subtract the final baseline from the original spectrum as has been done in Fig. 5. We observe that the zero line has been well centred in the noise band. The baseline was chosen to be rigid enough to retain all the small peaks, e.g. around 7000 and 8000 m/z. By using more buckets and a smaller window width it is possible to place the baseline in the peak cluster around 12,000 m/z so that the peaks are better resolved as indicated with the curved line in Fig. 5. If more localised control of the baseline flexibility is needed one has to resolve use varying bin widths along the spectra.

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