Compound Identification
Using Partial and Semipartial
Correlations for Gas Chromatography–Mass Spectrometry Data
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Abstract
Compound identification is a key component of data analysis
in
the applications of gas chromatography–mass spectrometry (GC-MS).
Currently, the most widely used compound identification is mass spectrum
matching, in which the dot product and its composite version are employed
as spectral similarity measures. Several forms of transformations
for fragment ion intensities have also been proposed to increase the
accuracy of compound identification. In this study, we introduced
partial and semipartial correlations as mass spectral similarity measures
and applied them to identify compounds along with different transformations
of peak intensity. The mixture versions of the proposed method were
also developed to further improve the accuracy of compound identification.
To demonstrate the performance of the proposed spectral similarity
measures, the National Institute of Standards and Technology (NIST)
mass spectral library and replicate spectral library were used as
the reference library and the query spectra, respectively. Identification
results showed that the mixture partial and semipartial correlations
always outperform both the dot product and its composite measure.
The mixture similarity with semipartial correlation has the highest
accuracy of 84.6% in compound identification with a transformation
of (0.53,1.3) for fragment ion intensity and <i>m</i>/<i>z</i> value, respectively