Classification of mass spectra: A comparison of yes/no classification methods for the recognition of simple structural properties
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Mass spectral databases for LC/MS- and GC/MS-based metabolomics: State of the field and future prospects
2016, TrAC - Trends in Analytical ChemistryCitation Excerpt :AMDIS has been designed by NIST to deconvolute and reconstruct “pure component” spectra from complex GC/MS total ion current chromatograms. Deconvoluted mass spectra are used for compound identification via NIST database matching, while substructure interpretation is also available (complementary to the MS Search) using the classification of Varmuza et al. [73,74]. Although AMDIS is freely available, the code is not.
Utilizing Artificial Neural Networks in MATLAB to Achieve Parts-Per-Billion Mass Measurement Accuracy with a Fourier Transform Ion Cyclotron Resonance Mass Spectrometer
2009, Journal of the American Society for Mass SpectrometrySupport vector machines and its applications in chemistry
2009, Chemometrics and Intelligent Laboratory SystemsFeature selection by genetic algorithms for mass spectral classifiers
2001, Analytica Chimica ActaCitation Excerpt :In early work with mass spectral classifiers, mostly the peak intensities have been used as features. It has been shown [18,24] that an appropriate transformation of the peak data into a set of new spectral features improves the relationships between features and chemical structure information. In this work, a set of 400 spectral features has been used as summarised in Table 2; details have been described elsewhere [18,20].
Capability of feed-forward neural networks for a chemical evaluation of sediments with diffuse reflectance spectroscopy
2000, Chemometrics and Intelligent Laboratory Systems