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  • Analytical Chemistry and Spectroscopy  (25,033)
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  • 11
    Electronic Resource
    Electronic Resource
    The Unscrambler®, Version 5.5. Multivariate analysis software for MS-DOS. Available from CAMO A/S, Olav Tryggvasons Gate 24, N-7011 Trondheim, Norway (Telephone: (+47) 7351 4966, Fax: (+47) 7351 4257) or from CAMO USA, 435 Blake Street, Menlo Park, CA 94025, U.S.A. (Telephone/Fax: (+1 415) 324-4286, Internet: teledyn@netcom.com). Single-copy price NOK 26 900 commercial, NOK 18 800 non-commercial, NOK 11 900 academic (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 527-529 
    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.1180090612
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  • 12
    Electronic Resource
    Electronic Resource
    Comment (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 229-229 
    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.1180090310
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  • 13
    Electronic Resource
    Electronic Resource
    Stone and jonathan versus mager debate (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 230-231 
    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.1180090311
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  • 14
    Electronic Resource
    Electronic Resource
    A rigorous QSAR analysis? (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 232-236 
    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.1180090312
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  • 15
    Electronic Resource
    Electronic Resource
    Generalized rank annihilation method. III: Practical implementation (1994)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 8 (1994), S. 273-285 
    Details
    ISSN: 0886-9383
    Keywords: GRAM ; Least-squares problem ; Eigenvalue problem ; NIPALS ; Performance index ; Condition number ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: In this paper we discuss the practical implementation of the generalized rank annihilation method (GRAM). The practical implementation comes down to developing a computer program where two critical steps can be distinguished: the construction of the factor space and the oblique rotation of the factors. The construction of the factor space is a least-squares (LS) problem solved by singular value decomposition (SVD), whereas the rotation of the factors is brought about by solving an eigenvalue problem. In the past several formulations for GRAM have been published. The differences essentially come down to solving either a standard eigenvalue problem or a generalized eigenvalue problem. The first objective of this paper is to discuss the numerical stability of the algorithms resulting from these formulations. It is found that the generalized eigenvalue problem is only to be preferred if the construction of the factor space is not performed with maximum precision. This is demonstrated for the case where the dominant factors are calculated by the non-linear iterative partial least-squares (NIPALS) algorithm. Several performance measures are proposed to investigate the numerical accuracy of the computed solution. The previously derived bias and variance are proposed to estimate the number of physically significant digits in the computed solution. The second objective of this paper is to discuss the relevance of theoretical considerations for application of GRAM in the presence of model errors.
    Additional Material: 1 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180080406
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  • 16
    Electronic Resource
    Electronic Resource
    Univariate regression models with errors in both axes (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 343-362 
    Details
    ISSN: 0886-9383
    Keywords: straight line calibration ; errors in both axes ; uncertainties ; linear regression ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Calibration is a fundamental step in the calculation of the unknown concentration of analyte in most analytical methods. It is known that for certain methodologies, if only the errors in the independent variable are taken into account, there may be considerable errors in the estimation of the value of the regression coefficients, the derived statistical parameters and in some cases the sought for response and concentration values. This paper reviews the calibration methods including some references to procedures for the detection of outliers and robust regression when there are errors in both axes. The advantages and limitations of the different approaches are discussed and a comparative study is made of the approaches of several techniques for which computer programmes have been developed based on the algorithms put forward by the different authors. Finally, some trends of future development in this field are envisaged.
    Additional Material: 2 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090503
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  • 17
    Electronic Resource
    Electronic Resource
    A simple iteration algorithm for PLS regression (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 363-372 
    Details
    ISSN: 0886-9383
    Keywords: PLS regression ; orthogonal expansion ; optimization ; Lagrange multipliers ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A simple iteration algorithm that is faster and less memory-intensive than the NIPALS iteration algorithm for PLS regression is presented. The iteration algorithm is obtained by treating the orthogonal expansion or decomposition of a matrix X as an extremum problem subject to normalization and orthogonality constraint conditions and then solving the problem by use of the method of Lagrange multipliers. The main idea in this method is to find the transformation vector r. The latent variable t is expressed exactly as the linear combination of X-variables with the vector r so that the final regression coefficients can be conveniently provided. In the algorithm the recursion of the orthogonal projection is needed, which is derived by use of a matrix inverse formula. Algorithms are established from the equation for calculating the vector r that are suitable for dealing with three cases of large data sets. The first case is when the number of objects is very large, the number of variables is relatively small and the number of Y-variables is equal to or greater than the number of X-variables. The second case is when the number of objects is very large, the number of variables is relatively small and the number of X-variables is greater than the number of Y-variables. The last case is when the number of variables, either X- or Y-variables, or both, is very large and the number of objects is small.
    Additional Material: 4 Tab.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090504
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  • 18
    Electronic Resource
    Electronic Resource
    The data analysis handbook, I. E. Frank and R. Todeschini, Elsevier, Amsterdam, 1994, ISBN-444-81659-3,386 pp., Dfl325, US$185 (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 431-432 
    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.1180090509
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  • 19
    Electronic Resource
    Electronic Resource
    Robust non-linear regression analysis (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 451-457 
    Details
    ISSN: 0886-9383
    Keywords: non-linear regression ; optimization ; robust methods ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Several robust regression methods, including a new proposal, are described and their properties discussed. Resistance to various types of outliers and non-normality is demonstrated. The techniques are applied to non-linear regression models from chemical kinetics and calibration. Optimization of the types of objective functions encountered when applying robust regression is considered.
    Additional Material: 5 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090603
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  • 20
    Electronic Resource
    Electronic Resource
    A PLS kernel algorithm for data sets with many variables and few objects. Part II: Cross-validation, missing data and examples (1995)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 9 (1995), S. 459-470 
    Details
    ISSN: 0886-9383
    Keywords: PLS ; kernel algorithm ; multivariate calibration ; EM algorithm ; cross-validation ; missing data ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: This is Part II of a series concerning the PLS kernel algorithm for data sets with many variables and few objects. Here the issues of cross-validation and missing data are investigated. Both partial and full crossvalidation are evaluated in terms of predictive residuals and speed and are illustrated on real examples. Two related approaches to the solution of the missing data problem are presented. One is a full EM algorithm and the second a reduced EM algorithm which applies when the number of missing values is small. The two examples are multivariate calibration data sets. The first set consists of UV-visible data measured on mixtures of four metal ions. The second example consists of FT-IR measurements on mixtures consisting of four different organic substances.
    Additional Material: 5 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180090604
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