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  • Articles  (11,921)
  • General Chemistry  (6,850)
  • Animals  (2,869)
  • Analytical Chemistry and Spectroscopy  (2,202)
  • 1995-1999  (11,921)
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  • Articles  (11,921)
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  • 11
    Electronic Resource
    Electronic Resource
    A combined theory for PCA and PLS (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 91-123 
    Details
    ISSN: 0886-9383
    Keywords: H-principle ; PCA ; PLS regression ; latent variable models ; quadratic models ; sensitivity analysis ; outlier tests ; prediction variances ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: We present here an algorithmic approach to modelling data that includes principal component analysis (PCA) and partial least squares (PLS). In fact, the numerical algorithm presented can carry out PCA or PLS. The algorithm for linear analysis and extensions to non-linear analysis applies to both PCA and PLS. The algorithm allows for combination of PCA and PLS types of models and therefore extends modelling to new types of models that involve combination of regression models and ‘selection of variation’ models, which is the idea of PCA-type models. The fact that the algorithm carries out both PCA and PLS shows that PCA and PLS are based on the same theory. This theory is based on the H-principle of mathematical modelling. The algorithm allows tests for outliers, sensitivity analysis and tests of submodels. These aspects of the algorithm are treated in detail. We compute various measures of sizes, e.g. of components, of the covariance matrix, of its inverse, etc. that show how much the algorithm has selected at each step. The analysis is illustrated by data from practice.
    Additional Material: 11 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090203
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  • 12
    Electronic Resource
    Electronic Resource
    Pattern recognition analysis of near-infrared spectra by robust distance method (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 169-178 
    Details
    ISSN: 0886-9383
    Keywords: minimum volume ellipsoid (MVE) estimators ; robust distance method ; pattern recognition ; Hotelling's T2 statistics ; near-infrared spectra ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A method for pattern recognition analysis of near-infrared spectra has been developed using robust distances determined by minimum volume ellipsoid (MVE) estimators of multivariate location and scatter. Classical methods such as the Mahalanobis distance method often fail in the presence of a moderate number of outliers in a training data set, while robust distance methods can tolerate a considerably larger proportion of outliers in a training data set Outliers can be detected by their relatively large robust distances and can be excluded from a training set without a priori knowledge of the nature of the data set. In this paper the properties of a robust distance method are examined using near-infrared spectra of sulfamethoxazole and mixtures with its major degradation products, sulfanilic acid and sulfanilamide. The robust distance method successfully detected unacceptable samples (71.4%-89.3% (α = 0.05) or 78.6%-92.9% (α = 0.10)) even when they were inadvertently included in the training data set.
    Additional Material: 3 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090304
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  • 13
    Electronic Resource
    Electronic Resource
    Data analyses or problem formulations? (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 226-228 
    Details
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090309
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  • 14
    Electronic Resource
    Electronic Resource
    Expression of the reference value for proficiency tests (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 197-209 
    Details
    ISSN: 0886-9383
    Keywords: proficiency test ; true value ; homogeneity robust statistic ; standard ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Profficiency tests raise two types of problems: (i) the homogeneity of a sample must be carefully checked before using it as a reference material; (ii) it is necessary to define a conventional reference value (RV) in order to rank participants. Since these topics are poorly addressed in the literature, a reference material was specially prepared in order to propose a procedure for verifying homogeneity and to establish whether the algorithms classically used to compute the conventional true value have an influence on the conclusion of the test.The homogeneity of the sample was not perfect and univariate and multivariate techniques were used to demonstrate some analytes can be suspected as heterogeneous. However, this was considered adequate for introducing the sample in a large proficiency test organized between four laboratory associations which regularly perform such tests. Altogether more than 3000 measurements were collected and eight algorithms were applied to compute the RV on 14 analytes. Thus is was possible to demonstrate that some of these algorithms are better adapted than others. It is also obvious that some analytes are better suited to proficiency testing.It can be concluded from this work that some effort towards standardization would be profitable to check homogeneity or to compute the RV, especially since the economic weight of proficiency testing is becoming even more important with the development of certification accreditation.
    Additional Material: 3 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090306
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  • 15
    Electronic Resource
    Electronic Resource
    Interactive variable selection (IVS) for PLS. Part II: Chemical applications (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 331-342 
    Details
    ISSN: 0886-9383
    Keywords: partial least squares (PLS) ; variable selection ; IVS-PLS ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: With the aim of developing PLS models with improved predictive properties, an interactive variable selection (IVS) approach for PLS regression was introduced in Part I of this series. IVS-PLS is based on a dimension-wise selective removal of single elements in the PLS weight vector w. IVS uses cross-validation (CV) as a guiding tool. The present paper illustrates the use of IVS-PLS on both simulated data and real examples from chemistry. In the first example, spectrophotometric data were simulated according to an experimental design. The objective was to see how IVS-PLS was influenced by different levels of noise in X and Y and by the number of predictor variables (K). The results of the modelling are shown as response surfaces. In addition, four real examples were modelled by the IVS-PLS technique. The real data sets were chosen to reflect different types of data from chemistry. For each example a comparison of ‘prediction error sum of squares’ (PRESS) between IVS-PLS and classical PLS is madeFor most of the examples containing many predictor variables IVS-PLS shows an improvement in predictive properties over classical PLS. Also, improvements for IVS-PLS2 (modelling of more than one y-variable) models were found. For data sets with a moderate number of variables the influence of the IVS method becomes less pronounced.
    Additional Material: 3 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090502
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  • 16
    Electronic Resource
    Electronic Resource
    An extended strategy for exploratory multivariate image analysis including noise considerations (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 389-409 
    Details
    ISSN: 0886-9383
    Keywords: multivariate image analysis ; principal component analysis ; exploratory data analysis ; projection in multivariate space ; graphical visualization ; noise ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Multivariate image analysis (MIA) is a powerful tool for many image segmentation and classification problems, but the interpretation and understanding of the original and resulting multidimensional (multivariate) data are not always easy. A strategy for MIA has been proposed which describes its usage on multivariate images for segmentation tasks. MIA starts with principal component analysis (PCA) and then continues with interactive analysis of the output from PCA. In this paper a number of extensions to MIA are proposed. The extensions are the suggestion to incorporate preprocessing of the multivariate image in MIA, the suggestion to use synthetic multivariate image models which create a clear-cut situation, and new visualization tools which improve the interactivity and understanding of the results. Extended MIA is applied on synthetic multivariate image data simulating a possible application with large noise, positron emission tomography (PET). As a result of the interactive analysis, suggestions for preprocessing emerge. The developed methodology for handling the noise is then applied on real PET image data with good results.
    Additional Material: 20 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090506
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  • 17
    Electronic Resource
    Electronic Resource
    A correlation principal component regression analysis of NIR data (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 21-29 
    Details
    ISSN: 0886-9383
    Keywords: Near-infrared absorbance ; Partial least squares ; Principal component regression ; Root-mean-square error of prediction ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: The use of principal component regression (PCR) as a multivariate calibration method has been discussed by a number of authors. In most situations principal components are included in the regression model in sequence based on the variances of the components, and the principal components with small variances are rarely used in regression. As pointed out by some authors, a low variance for a component does not necessarily imply that the corresponding component is unimportant, especially when prediction is of primary interest. In this paper we investigate a different version of PCR, correlation principal component regression (CPCR). In CPCR the importance of principal components in terms of predicting the response variable is used as a basis for the inclusion of principal components in the regression model. Two typical examples arising from calibrating near-infrared (NIR) instruments are discussed for the comparison of the two different versions of PCR along with partial least squares (PLS), a commonly used regression approach in NIR analysis. In both examples the three methods show similar optimal prediction ability, but CPCR performs better than standard PCR and PLS in terms of the number of components needed to achieve the optimal prediction ability. Similar results are also seen in other NIR examples.
    Additional Material: 2 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090104
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  • 18
    Electronic Resource
    Electronic Resource
    Announcement (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. i 
    Details
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090108
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  • 19
    Electronic Resource
    Electronic Resource
    International chemometrics research meeting (ICRM) (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 137-138 
    Details
    ISSN: 0886-9383
    Keywords: Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090205
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  • 20
    Electronic Resource
    Electronic Resource
    Simple encoding of infrared spectra for pattern recognition. 1: Statistical examination of the effectiveness and information content by factor analysis (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 143-168 
    Details
    ISSN: 0886-9383
    Keywords: pattern recognition ; infrared spectra ; factor analysis ; maximum likelihood method ; entropy of information ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: The spectral region from 700 to 3600 cm-1 is subdivided into several wave number intervals. The peaks in each interval are summarized by means of three encoding algorithms. Using a factor model of kcommon factors, the total extractable variacnce (com) of a given set of intervals is calculated and correlated with the redundancy of information in all these intervals. The value of com is verified by analysis of the factor loadings aik (factor pattern). Finally, the information content of some chosen sets of intervals coded by the three selected feature algorithms will be correlated to the probability of information flow through a serial-parallel network. The encoding using only wave numbers was found to be the most effective.
    Additional Material: 9 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090303
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