Abstract
Matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry is a technique for high mass-resolution analysis of substances that is rapidly gaining popularity as an analytic tool. Extracting signal from the background noise, however, poses significant challenges. In this article, we model the noise part of a spectrum as an autoregressive, moving average (ARMA) time series with innovations given by a generalized gamma distribution with varying scale parameter but constant shape parameter and exponent. This enables us to classify peaks found in actual spectra as either noise or signal using a reasonable criterion that outperforms a standard threshold criterion.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 207-214 |
| Number of pages | 8 |
| Journal | Analytica Chimica Acta |
| Volume | 648 |
| Issue number | 2 |
| DOIs | |
| State | Published - Aug 26 2009 |
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Keywords
- Fourier transform ion cyclotron resonance
- Generalized gamma distribution
- Matrix-assisted laser desorption/ionization
ASJC Scopus subject areas
- Biochemistry
- Analytical Chemistry
- Spectroscopy
- Environmental Chemistry
Cite this
Analysis of MALDI FT-ICR mass spectrometry data : A time series approach. / Barkauskas, Donald A.; Kronewitter, Scott R.; Lebrilla, Carlito B; Rocke, David M.
In: Analytica Chimica Acta, Vol. 648, No. 2, 26.08.2009, p. 207-214.Research output: Contribution to journal › Article
}
TY - JOUR
T1 - Analysis of MALDI FT-ICR mass spectrometry data
T2 - A time series approach
AU - Barkauskas, Donald A.
AU - Kronewitter, Scott R.
AU - Lebrilla, Carlito B
AU - Rocke, David M
PY - 2009/8/26
Y1 - 2009/8/26
N2 - Matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry is a technique for high mass-resolution analysis of substances that is rapidly gaining popularity as an analytic tool. Extracting signal from the background noise, however, poses significant challenges. In this article, we model the noise part of a spectrum as an autoregressive, moving average (ARMA) time series with innovations given by a generalized gamma distribution with varying scale parameter but constant shape parameter and exponent. This enables us to classify peaks found in actual spectra as either noise or signal using a reasonable criterion that outperforms a standard threshold criterion.
AB - Matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry is a technique for high mass-resolution analysis of substances that is rapidly gaining popularity as an analytic tool. Extracting signal from the background noise, however, poses significant challenges. In this article, we model the noise part of a spectrum as an autoregressive, moving average (ARMA) time series with innovations given by a generalized gamma distribution with varying scale parameter but constant shape parameter and exponent. This enables us to classify peaks found in actual spectra as either noise or signal using a reasonable criterion that outperforms a standard threshold criterion.
KW - Fourier transform ion cyclotron resonance
KW - Generalized gamma distribution
KW - Matrix-assisted laser desorption/ionization
UR - http://www.scopus.com/inward/record.url?scp=67651087377&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=67651087377&partnerID=8YFLogxK
U2 - 10.1016/j.aca.2009.06.064
DO - 10.1016/j.aca.2009.06.064
M3 - Article
C2 - 19646586
AN - SCOPUS:67651087377
VL - 648
SP - 207
EP - 214
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
SN - 0003-2670
IS - 2
ER -