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1. |
Non‐linear mapping for structure‐activity and structure‐property modelling |
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Journal of Chemometrics,
Volume 7,
Issue 4,
1993,
Page 227-242
D. Domine,
J. Devillers,
M. Chastrette,
W. Karcher,
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摘要:
AbstractFrom a review of the theoretical aspects of non‐linear mapping and the different algorithms available in the literature, the methodological and practical problems linked to the use of this multivariate method in structure‐activity and structure‐property relationship studies are underlined. Useful tools for the graphical display of the outputs and the interpretation of the obtained clusters are presented. Statistical parameters estimating the quality of the graphical representation of each individual are also introduced. An example of application on a data matrix of 37 acaricides described by four physicochemical descriptors (π,F, R, MR) is pre
ISSN:0886-9383
DOI:10.1002/cem.1180070402
出版商:John Wiley&Sons, Ltd.
年代:1993
数据来源: WILEY
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2. |
Application of orthogonal expansion to mapping and modelling |
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Journal of Chemometrics,
Volume 7,
Issue 4,
1993,
Page 243-253
Zhaonian Cheng,
Eryi Zhu,
Nianyi Chen,
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摘要:
AbstractIn this paper we discuss the orthogonal expansion of data matrices and its application to mapping and modelling. Two new methods, modified optimal discriminant plane (MODP) for mapping and order Gram‐Schmidt orthogonalization (OGSO) for modelling, are proposed and examples are give
ISSN:0886-9383
DOI:10.1002/cem.1180070403
出版商:John Wiley&Sons, Ltd.
年代:1993
数据来源: WILEY
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3. |
Graphics for linearity and selectivity and prediction diagnostics for multicomponent spectra |
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Journal of Chemometrics,
Volume 7,
Issue 4,
1993,
Page 255-265
Philip J. Brown,
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摘要:
AbstractModern scanning infrared reflectance/absorption spectroscopes measure absorptions or reflectance at a sequence of around 1000 wavelengths. Training data may consist of 10–100 carefully designed sample mixtures whose true compositions are either known by formulation or accurately determined by wet chemistry. In future, one wishes to predict the true composition of a newly presented sample from its spectrum. Varying compositions of a mixture of three sugars in water are used for illustration of several different graphical techniques; the spectral measurements here are near‐infrared (NIR) absorbances, but there is nothing exclusively infrared about the methodology. Graphs display the adequacy of a linear explanation of absorbance variability at each wavelength by wavelength linearity plots. These highlight regions of the spectrum where non‐linearities and interaction effects are substantial. By selecting out these substantially non‐linear regions, one can concentrate on linear formulae for prediction with resultant robust linear modelling. Such selections are further aided by plots which identify the component sugar for which each wavelength is most selective. Such plots offer rather natural pre‐screening as an alternative or supplement to the wavelength selection method of Brown.We also display prediction diagnostics (R, Rx) which on a sample‐by‐sample basis may diagnose a particularly unusual presented spectrum. These diagnostics are shown to have predictive import for a valida
ISSN:0886-9383
DOI:10.1002/cem.1180070404
出版商:John Wiley&Sons, Ltd.
年代:1993
数据来源: WILEY
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4. |
Use of the Kalman filter for multivariate calibration in a real system and its comparison with CLS and pure component calibration methods |
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Journal of Chemometrics,
Volume 7,
Issue 4,
1993,
Page 267-275
L. V. Péarez‐arribas,
F. Navarro‐Villoslada,
M. E. León‐Gonzalez,
L. M. Polo‐Díez,
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摘要:
AbstractThe usefulness of the Kalman filter as an algorithm for calibration in a real system is shown. Results are compared with classical least squares and pure component calibration. The prediction of four priority pollutant chlorophenols in binary, ternary and quaternary mixtures was also carried out by Kalman filtering. The condition number, standard deviation and prediction error have been employed to choose the most suitable wavelength range. Comparison of the standard error of prediction in the validation set shows significant differences between the evaluated chlorophenols, the best results being obtained with Kalman multivariate calibration.
ISSN:0886-9383
DOI:10.1002/cem.1180070405
出版商:John Wiley&Sons, Ltd.
年代:1993
数据来源: WILEY
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5. |
Determination of a multivariate detection limit and local chemical rank by designing a non‐parametric test from the zero‐component regions |
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Journal of Chemometrics,
Volume 7,
Issue 4,
1993,
Page 277-289
Yi‐Zeng Liang,
Olav M. Kvalheim,
Agnar Höskuldsson,
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摘要:
AbstractIn this paper we redefine the term detection limit to embrace the inherent multivariate nature of samples, instrumental measurements and chemometrics resolution procedures. The so‐called zero‐component regions, i.e. parts with no chemical components eluting, are used as repeated analytical blanks to estimate a statistical multivariate detection limit for determining the number of chemical species in local regions of a single two‐way chromatogram or a collection of synchronized one‐way chromatograms. For two‐way chromatography the detection limit is determined from the distribution of the first eigenvalues obtained from all possible combinations of spectra in the zero‐component regions. The number of spectra in each calculation should correspond to the number included in the later examination of the local retention time regions. For one‐way chromatography on a collection of samples with similar chemical components at varying concentrations the same procedure is used, with the samples taking the role of the spectra in two‐way chromatography. The detection limit can be chosen at various confidence levels depending on whether false positive or negative detection of minor components is most critical. The results obtained from the zero‐eigenvalue distribution are more robust than those obtained by a previousl
ISSN:0886-9383
DOI:10.1002/cem.1180070406
出版商:John Wiley&Sons, Ltd.
年代:1993
数据来源: WILEY
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6. |
A test of significance for partial least squares regression |
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Journal of Chemometrics,
Volume 7,
Issue 4,
1993,
Page 291-304
Ian N. Wakeling,
Jeff J. Morris,
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摘要:
AbstractPartial least squares (PLS) regression is a commonly used statistical technique for performing multivariate calibration, especially in situations where there are more variables than samples. Choosing the number of factors to include in a model is a decision that all users of PLS must make, but is complicated by the large number of empirical tests available. In most instances predictive ability is the most desired property of a PLS model and so interest has centred on making this choice based on an internal validation process. A popular approach is the calculation of a cross‐validatedr2to gauge how much variance in the dependent variable can be explained from leave‐one‐out predictions. Using Monte Carlo simulations for different sizes of data set, the influence of chance effects on the cross‐validation process is investigated. The results are presented as tables of critical values which are compared against the values of cross‐validatedr2obtained from the user's own data set. This gives a formal test for predictive ability of a PLS model with a given number of d
ISSN:0886-9383
DOI:10.1002/cem.1180070407
出版商:John Wiley&Sons, Ltd.
年代:1993
数据来源: WILEY
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7. |
Masthead |
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Journal of Chemometrics,
Volume 7,
Issue 4,
1993,
Page -
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PDF (89KB)
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ISSN:0886-9383
DOI:10.1002/cem.1180070401
出版商:John Wiley&Sons, Ltd.
年代:1993
数据来源: WILEY
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