Strukturaufklärung organischer Verbindungen durch computerunterstützten Vergleich spektraler Daten

Erni, F.; Clerc, J. T.

doi:10.1002/hlca.19720550220

Cited by 49 publications

(15 citation statements)

References 35 publications

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“…Features of this group emphasize isolated peaks even if possessing only low intensities (Erni and Clerc 1972):…”

Section: A Intensities At a Single Mass Normalized To Local Ion Currentmentioning

confidence: 99%

The Classification Tree Combined with SIR and Its Applications to Classification of Mass Spectra

He¹,

Fang²,

Xu³

2021

Journal of Data Science

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A new approach combining classification tree (CT) with sliced inverse regression (SIR) is proposed and applied to the classification of mass spectra in this paper. The classification tree has been widely used to generate classifiers from the mass spectral data because of its powerful ability in automatic variable selection and automatic interaction detection. However, it is often weak on presenting the linear and global relationships among variables. When the variables enter a model with the form of linear combination, the classification tree can not detect the form and leads to a low accuracy. SIR is an effective method to find useful linear combinations of predictor variables to regress the response variable. So merging CT and SIR harmoniously can inherit both advantages of them. Experiments in the paper show that the proposed approach can improve classification accuracy of decision tree and get better result than other classical classification methods.

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“…Features of this group emphasize isolated peaks even if possessing only low intensities (Erni and Clerc 1972):…”

Section: A Intensities At a Single Mass Normalized To Local Ion Currentmentioning

confidence: 99%

The Classification Tree Combined with SIR and Its Applications to Classification of Mass Spectra

He¹,

Fang²,

Xu³

2021

Journal of Data Science

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“…Features of this group emphasize isolated peaks even if possessing only low intensities [26]. The local ion current is the sum of peak intensities in a mass interval AEDm around a considered mass m. Features have been calculated for masses 25±38 and 40±150 with Dm = 3, resulting in 125 features.…”

Section: Intensities At Single Masses Normalized To Local Ion Currentmentioning

confidence: 99%

Evaluation of mass spectra from organic compounds assumed to be present in cometary grains. Exploratory data analysis

Werther¹,

Demuth²,

Krueger³

et al. 2002

Journal of Chemometrics

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From about 60 organic compounds, mass spectra have been measured by 252 Cf ionization mass spectrometry (PDMS). The substances and the data are relevant for future experiments near a comet by the space research project COSIMA (a time-of-flight mass spectrometer for secondary ions) on board of the ESA mission ROSETTA. The spectra have been transformed into a set of appropriate spectral features (x-variables); the corresponding chemical structures have been characterized by binary substructure descriptors (y-variables). PLS-2 mappings exhibit good correlations between the x-and the y-components. PLS spectrum±substructure mappings supported the detection of structural classes that are well reflected by spectral data. PCA with spectral data was less efficient for this purpose. The found structural classes are candidates for the development of spectral classifiers for the COSIMA project. Interpretation of the loading plots indicated spectral properties which are characteristic for substance classes.

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“…Spectral Features. Mathematical transformation of mass spectra before applying formal interpretation methods dates back to early applications of multivariate methods for spectra interpretation, library search, and pattern recognition . Recently, the importance of an appropriate mathematical representation of mass spectra has been demonstrated again. ,− A spectral feature x j (a spectral invariant) is a number that can be automatically computed from a spectrum.…”

Section: Mass Spectra Classificationmentioning

confidence: 99%

Mass Spectral Classifiers for Supporting Systematic Structure Elucidation

and

Werther

1996

J. Chem. Inf. Comput. Sci.

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A set of mass spectral classifiers has been developed to recognize presence or absence of 70 substructures or more general structural properties in a molecule. Classification is based on numerical transformation of low resolution mass spectral data, automatic selection of appropriate features, multivariate discriminant methods, and estimation of the reliability of the classification answer. Examples demonstrate applications in structure elucidation together with automatic isomer generation as well as combination with results obtained by the CHEMICS system.

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Strukturaufklärung organischer Verbindungen durch computerunterstützten Vergleich spektraler Daten

Cited by 49 publications

References 35 publications

The Classification Tree Combined with SIR and Its Applications to Classification of Mass Spectra

The Classification Tree Combined with SIR and Its Applications to Classification of Mass Spectra

Evaluation of mass spectra from organic compounds assumed to be present in cometary grains. Exploratory data analysis

Mass Spectral Classifiers for Supporting Systematic Structure Elucidation

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