PMID- 16860329
OWN - NLM
STAT- MEDLINE
DCOM- 20061122
LR  - 20090115
IS  - 0021-9673 (Print)
IS  - 0021-9673 (Linking)
VI  - 1129
IP  - 1
DP  - 2006 Sep 29
TI  - Classification of high-speed gas chromatography-mass spectrometry data by 
      principal component analysis coupled with piecewise alignment and feature 
      selection.
PG  - 111-8
AB  - A useful methodology is introduced for the analysis of data obtained via gas 
      chromatography with mass spectrometry (GC-MS) utilizing a complete mass spectrum 
      at each retention time interval in which a mass spectrum was collected. Principal 
      component analysis (PCA) with preprocessing by both piecewise retention time 
      alignment and analysis of variance (ANOVA) feature selection is applied to all 
      mass channels collected. The methodology involves concatenating all concurrently 
      measured individual m/z chromatograms from m/z 20 to 120 for each GC-MS 
      separation into a row vector. All of the sample row vectors are incorporated into 
      a matrix where each row is a sample vector. This matrix is piecewise aligned and 
      reduced by ANOVA feature selection. Application of the preprocessing steps 
      (retention time alignment and feature selection) to all mass channels collected 
      during the chromatographic separation allows considerably more selective chemical 
      information to be incorporated in the PCA classification, and is the primary 
      novelty of the report. This methodology is objective and requires no knowledge of 
      the specific analytes of interest, as in selective ion monitoring (SIM), and does 
      not restrict the mass spectral data used, as in both SIM and total ion current 
      (TIC) methods. Significantly, the methodology allows for the classification of 
      data with low resolution in the chromatographic dimension because of the added 
      selectivity from the complete mass spectral dimension. This allows for the 
      successful classification of data over significantly decreased chromatographic 
      separation times, since high-speed separations can be employed. The methodology 
      is demonstrated through the analysis of a set of four differing gasoline samples 
      that serve as model complex samples. For comparison, the gasoline samples are 
      analyzed by GC-MS over both 10-min and 10-s separation times. The successfully 
      classified 10-min GC-MS TIC data served as the benchmark analysis to compare to 
      the 10-s data. When only alignment and feature selection was applied to the 10-s 
      gasoline separations using GC-MS TIC data, PCA failed. PCA was successful for 
      10-s gasoline separations when the methodology was applied with all the m/z 
      information. With ANOVA feature selection, chromatographic regions with Fisher 
      ratios greater than 1500 were retained in a new matrix and subjected to PCA 
      yielding successful classification for the 10-s separations.
FAU - Watson, Nathanial E
AU  - Watson NE
AD  - Department of Chemistry, University of Washington, Seattle, WA 98195, USA.
FAU - Vanwingerden, Matthew M
AU  - Vanwingerden MM
FAU - Pierce, Karisa M
AU  - Pierce KM
FAU - Wright, Bob W
AU  - Wright BW
FAU - Synovec, Robert E
AU  - Synovec RE
LA  - eng
PT  - Journal Article
PT  - Research Support, U.S. Gov't, Non-P.H.S.
DEP - 20060724
PL  - Netherlands
TA  - J Chromatogr A
JT  - Journal of chromatography. A
JID - 9318488
RN  - 0 (Gasoline)
SB  - IM
MH  - Analysis of Variance
MH  - Gas Chromatography-Mass Spectrometry/*methods
MH  - Gasoline/analysis
MH  - Principal Component Analysis/*methods
EDAT- 2006/07/25 09:00
MHDA- 2006/12/09 09:00
CRDT- 2006/07/25 09:00
PHST- 2006/03/03 00:00 [received]
PHST- 2006/06/22 00:00 [revised]
PHST- 2006/06/26 00:00 [accepted]
PHST- 2006/07/25 09:00 [pubmed]
PHST- 2006/12/09 09:00 [medline]
PHST- 2006/07/25 09:00 [entrez]
AID - S0021-9673(06)01288-X [pii]
AID - 10.1016/j.chroma.2006.06.087 [doi]
PST - ppublish
SO  - J Chromatogr A. 2006 Sep 29;1129(1):111-8. doi: 10.1016/j.chroma.2006.06.087. 
      Epub 2006 Jul 24.