Chemometrics: Multivariate View on Chemical Problems

Вармуза, Курт

doi:10.1002/0470845015.cca018

Cited by 6 publications

(6 citation statements)

References 95 publications

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“…Chemometrics (Adams, 1995;Massart et al, 1997;Beebe, Pell, & Seasholtz, 1998;Vandeginste et al, 1998;Varmuza, 1998) has been defined as a chemical discipline that uses mathematical and statistical methods to design or select optimal procedures and experiments, and to provide maximum chemical information by analyzing chemical data. The most prominent part of chemometrics is extraction of relevant, chemistry-related information by the application of multivariate data analysis methods.…”

Section: A Overviewmentioning

confidence: 99%

Exploration of anthropological specimens by GC‐MS and chemometrics

Вармуза

Makristathis

Schwarzmeier

et al. 2004

Mass Spectrometry Reviews

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Anthropological specimens combine a variety of unfavorable characteristics, rendering their evaluation an analytical challenge. Their remarkable status is primarily based on two characteristics: (i) these very rare samples of human origin are testimonies of human history and are, therefore, available only in minute amounts for analytical purposes, and (ii) the analysis of these samples is extremely limited by the decomposition of molecules, which are easily detected in living organisms, such as nucleic acids and proteins, but are subject to rapid post-mortem decay. In this article, we review the methods and results of archaeometry, emphasizing the role of MS combined with chemometrics. Focusing on experimental results for fatty acid profiles, specimens from mummies from different civilizations were compared. Considering in particular the Tyrolean Iceman, the application of chemometric methods to GC-MS data recovers essential information about the preservation and the storage conditions of mummies.

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Section: A Overviewmentioning

confidence: 99%

Exploration of anthropological specimens by GC‐MS and chemometrics

Вармуза

Makristathis

Schwarzmeier

et al. 2004

Mass Spectrometry Reviews

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“…In a typical PCA investigation, the number of principal components sufficient for the model should be detected. There are several standard guidelines available , for detection of the number of optimal PCs. In the present analysis, the scree test was used, which allows one to plot the eigenvalues graphically with respect to the number of PCs.…”

Section: Methodsmentioning

confidence: 99%

A General Treatment of Solubility 4. Description and Analysis of a PCA Model for Ostwald Solubility Coefficients

Tulp

Dobchev

Katritzky

et al. 2010

J. Chem. Inf. Model.

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Principal component analysis (PCA) of a large data matrix (153 solvents × 396 solutes) for Ostwald solubility coefficients (log L) resulted in a two-component model covering 98.6% of the variability. Analysis of the principal components exposed the structural characteristics of solutes and solvents that codify interactions which determine the behavior of a chemical in the surrounding media. The pattern revealed by PCA analysis distinguishes solutes according to the molecular size, functional groups, and electrostatic interactions, such as polarity and hydrogen-bonding donor and acceptor properties.

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“…Finally, scatter plots with principal component scores as coordinates offer an interactive cluster analysis of the molecular structures. 2 The following example with two small sets of structures demonstrates an application of PCA to S-matrices; another example is contained in Results.…”

Section: Methodsmentioning

confidence: 99%

“…The loadings of the principal components possessing the highest variances indicate those descriptors (substructures) which are most relevant for the investigated molecular structures. Finally, scatter plots with principal component scores as coordinates offer an interactive cluster analysis of the molecular structures . The following example with two small sets of structures demonstrates an application of PCA to S -matrices; another example is contained in Results.…”

Section: Methodsmentioning

confidence: 99%

“…Multivariate data analysis methods play an important role in computer chemistry, especially in modeling structure−activity/property relationships, in cluster analyses of chemical structures, in similarity (or dissimilarity) searches with chemical structures, and in describing the diversity of structural libraries. The application of multivariate data analysis methods , requires a representation of chemical structures by a set of variables (molecular descriptors, features). A number of different approaches for defining molecular descriptors is available. − Widely used is the characterization of molecular structures by binary descriptors that indicate whether predefined substructures are part of a molecular structure or not.…”

Section: Introductionmentioning

confidence: 99%

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Substructure Isomorphism Matrix

Вармуза

Scsibrany

2000

J. Chem. Inf. Comput. Sci.

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A substructure isomorphism matrix n x p contains binary elements describing which of the given p query structures (substructures) are part of the given n target structures (molecular structures). Such a matrix can be used to investigate the diversity of the target structures and allows the characterization and comparison of structural libraries. A quadratic substructure isomorphism matrix n x n is obtained if the same structures are used as molecular structures and as substructures; this matrix contains full information about the topological hierarchy of the n structures. A hierarchical arrangement of chemical structures is useful for the evaluation of results obtained from searches in structure databases.

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Chemometrics: Multivariate View on Chemical Problems

Cited by 6 publications

References 95 publications

Exploration of anthropological specimens by GC‐MS and chemometrics

Exploration of anthropological specimens by GC‐MS and chemometrics

A General Treatment of Solubility 4. Description and Analysis of a PCA Model for Ostwald Solubility Coefficients

Substructure Isomorphism Matrix

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