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A Signature‐Based Method to Distinguish Time‐Of‐Flight Secondary‐Ion Mass Spectra from Biological Samples
Author(s) -
Quong Judy N.,
Quong Andrew A.,
Wu Kuang Jen,
Kercher James R.
Publication year - 2005
Publication title -
chemistry and biodiversity
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.427
H-Index - 70
eISSN - 1612-1880
pISSN - 1612-1872
DOI - 10.1002/cbdv.200590121
Subject(s) - overfitting , chemistry , mass spectrum , mass spectrometry , analytical chemistry (journal) , singular value decomposition , biological system , ion , data set , spectral line , mass , pattern recognition (psychology) , chromatography , artificial intelligence , computer science , physics , organic chemistry , astronomy , artificial neural network , biology
Time‐Of‐Flight Mass Spectrometry (TOF‐SIMS) was used to determine elemental and biomolecular ions from isolated protein samples. We identified a set of 23 mass‐to‐charge ratio ( m/z ) peaks that represent signatures for distinguishing biological samples. The 23 peaks were identified by Singular Value Decomposition (SVD) and Canonical Analysis (CA) to find the underlying structure in the complex mass‐spectra data sets. From this modified data, SVD was used to identify sets of m/z peaks, and we used these patterns from the TOF‐SIMS data to predict the biological source from which individual mass spectra were generated. The signatures were validated using an additional data set different from the initial training set used to identify the signatures. We present a simple method to identify multiple variables required for sample classification based on mass spectra that avoids overfit. This is important in a variety of studies using mass spectrometry, including the ability to identify proteins in complex mixtures and for the identification of new biomarkers.