Library

X
feed icon rss

Your email was sent successfully. Check your inbox.

An error occurred while sending the email. Please try again.

Proceed reservation?

Export
  • 1
    Electronic Resource
    Electronic Resource
    Sequential assignment of 2D-NMR spectra of proteins using genetic algorithms (1993)
    s.l. : American Chemical Society
    Journal of chemical information and modeling 33 (1993), S. 245-251 
    Details
    ISSN: 1520-5142
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1021/ci00012a010
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 2
    Electronic Resource
    Electronic Resource
    Curve fitting using natural computation (1994)
    s.l. : American Chemical Society
    Analytical chemistry 66 (1994), S. 23-31 
    Details
    ISSN: 1520-6882
    Source: ACS Legacy Archives
    Topics: Chemistry and Pharmacology
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1021/ac00073a006
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 3
    Electronic Resource
    Electronic Resource
    Computer aided exploitation of mathematical models of processes. A general description of the approach (1992)
    Springer
    Microchimica acta 108 (1992), S. 173-184 
    Details
    ISSN: 1436-5073
    Keywords: model parameter estimation ; information profiles
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract An approach is described which helps the experimenter in: (a) selecting the most appropriate method of determination of physicochemical or technical parameters, (b) optimization of analytical procedures or of determination of physicochemical parameters, (c) indication of the possible improvements of the experimental technique applied, (d) diagnosis of the model and the data handling scheme applied in the determination of parameters, and other. The examples presented are based on simulated experiments. They concern selection of the best method for determination of an equilibrium constant and for determination of two analytes from a single instrumental (photometric, potentiometric) titration curve.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF01242427
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 4
    Electronic Resource
    Electronic Resource
    Standard errors in the eigenvalues of a cross-product matrix: Theory and applications (1993)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 7 (1993), S. 495-526 
    Details
    ISSN: 0886-9383
    Keywords: Standard errors ; Eigenvalues ; PCA ; MLR ; GRAM ; Rank estimation ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: New expressions are derived for the standard errors in the eigenvalues of a cross-product matrix by the method of error propagation. Cross-product matrices frequently arise in multivariate data analysis, especially in principal component analysis (PCA). The derived standard errors account for the variability in the data as a result of measurement noise and are therefore essentially different from the standard errors developed in multivariate statistics. Those standard errors were derived in order to account for the finite number of observations on a fixed number of variables, the so-called sampling error. They can be used for making inferences about the population eigenvalues. Making inferences about the population eigenvalues is often not the purposes of PCA in physical sciences. This is particularly true if the measurements are performed on an analytical instrument that produces two-dimensional arrays for one chemical sample: the rows and columns of such a data matrix cannot be identified with observations on variables at all. However, PCA can still be used as a general data reduction technique, but now the effect of measurement noise on the standard errors in the eigenvalues has to be considered. The consequences for significance testing of the eigenvalues as well as the usefulness for error estimates for scores and loadings of PCA, multiple linear regression (MLR) and the generalized rank annihilation method (GRAM) are discussed. The adequacy of the derived expressions is tested by Monte Carlo simulations.
    Additional Material: 8 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180070605
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 5
    Electronic Resource
    Electronic Resource
    Generalized rank annihilation method. II: Bias and variance in the estimated eigenvalues (1994)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 8 (1994), S. 181-203 
    Details
    ISSN: 0886-9383
    Keywords: RAFA ; GRAM ; Eigenvalues ; Bias ; Variance ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Rank annihilation factor analysis (RAFA) is a method for multicomponent calibration using two data matrices simultaneously, one for the unknown and one for the calibration sample. In its most general form, the generalized rank annihilation method (GRAM), an eigenvalue problem has to be solved. In this second paper expressions are derived for predicting the bias and variance in the eigenvalues of GRAM. These expressions are built on the analogies between a reformulation of the eigenvalue problem and the prediction equations of univariate and multivariate calibration. The error analysis will also be performed for Lorber's formulation of RAFA. It will be demonstrated that, depending on the size of the eigenvalue, large differences in performance must be expected. A bias correction technique is proposed that effectively eliminates the bias if the error in the bias estimate is not too large. The derived expressions are evaluated by Monte Carlo simulations. It is shown that the predictions are satisfactory up to the limit of detection. The results are not sensitive to an incorrect choice of the dimension of the factor space.
    Additional Material: 3 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180080303
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 6
    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
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 7
    Electronic Resource
    Electronic Resource
    Generalized rank annihilation method. I: Derivation of eigenvalue problems (1994)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 8 (1994), S. 147-154 
    Details
    ISSN: 0886-9383
    Keywords: RAFA ; GRAM ; Eigenvalue problem ; Complex solution ; Degenerate solution ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Rank annihilation factor analysis (RAFA) is a method for multicomponent calibration using two data matrices simultaneously, one for the unknown and one for the calibration sample. In its most general form, the generalized rank annihilation method (GRAM), an eigenvalue problem has to be solved. In this first paper different formulations of GRAM are compared and a slightly different eigenvalue problem will be derived. The eigenvectors of this specific eigenvalue problem constitute the transformation matrix that rotates the abstract factors from principal component analysis (PCA) into their physical counterparts. This reformulation of GRAM facilitates a comparison with other PCA-based methods for curve resolution and calibration. Furthermore, we will discuss two characteristics common to all formulations of GRAM, i.e. the distinct possibility of a complex and degenerate solution. It will be shown that a complex solution-contrary to degeneracy-should not arise for components present in both samples for model data.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180080206
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 8
    Electronic Resource
    Electronic Resource
    The construction of simultaneous optimal experimental designs for several polynomials in the calibration of analytical methods (1990)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 4 (1990), S. 171-185 
    Details
    ISSN: 0886-9383
    Keywords: Calibration ; Polynomial regression ; D-optimality ; Experimental design ; Model discrimination ; Lack of fit ; Least squares regression ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: In this paper a criterion is described for the construction of experimental designs for the evaluation of calibration models in analytical chemistry. The proposed criterion seeks a compromise between the D-optimal designs for estimating the parameters of different polynomial models. A computer algorithm is presented for a sequential construction of experimental designs using the optimality criterion. The performance of the optimality criterion and the computer algorithm is elaborated for the problem of discrimination between a first- to a third-degree polynomial for the calibration of analytical methods. An experimental design consisting of replicate measurements at five distinct levels equally spaced over the calibration range proved a good solution.
    Additional Material: 6 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180040208
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 9
    Electronic Resource
    Electronic Resource
    Evolutionary determination of physico-chemical parameters and concentrations of analytes from titration data (1994)
    Springer
    Fresenius' Zeitschrift für analytische Chemie 348 (1994), S. 626-632 
    Details
    ISSN: 1618-2650
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract The usefulness of the Genetic Algorithm (GA) in the determination of physico-chemical parameters and concentrations of analytes from titration data has been tested. Two types of reactions have been considered: (a) M+T⇌MT, and (b) M1+T⇌M1T, M2+T⇌M2T, M1+I⇌M1I, M2+I⇌M2I, where M, M1 and M2 denote analytes, T-titrant, and I — an auxiliary agent (e.g. an indicator). Up to 4 parameters, in case of reaction (a) above, and up to 11 parameters, in case of reactions (b), were determined simultaneously from the simulated photometric titration curves. The results obtained by the GA were refined using the simplex method. The correlations between the parameters have been visualized on the basis of GA data and according to an approach which uses searches through the surface of fitting criterion vs. parameters. The detailed results obtained concern a set of parameters assumed in the examples, but it can be concluded that the GA appears very useful, especially in finding the starting values of parameters for further refinement by an optimization method of more local scope, e.g. the simplex method.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00325562
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
  • 10
    Electronic Resource
    Electronic Resource
    Calibration of titration methods (1992)
    Springer
    Fresenius' Zeitschrift für analytische Chemie 342 (1992), S. 10-14 
    Details
    ISSN: 1618-2650
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Summary A calibration method is proposed which makes it possible to use titration techniques in the presence of systematic errors, even if these errors depend on the concentrations of the analytes. The approach uses an empirical calibration model which approximates the relationship between apparent (found) and true concentrations of the analytes. Also a “calibration” of the physical model of the titration process is proposed, as well as a method of determination of the model parameters, which are useful when analytes are determined by fitting the model to experimental data of titration. Both approaches, empirical and based on the physical model, may be applied jointly. The example presented reveals high efficiency of the proposed approach in cases when a deficient physicochemical model of the titration process is used in the determination of an analyte concentration (simulated titration data applied). The calibration proposed may be considered as a generalization of the titrant standardization used in the conventional volumetric analysis. It may be applied to all titration techniques and for all methods of end-point detection and determination of the concentration of analytes. It opens new possibilities for the development of titration methods.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00321681
    Library Location Call Number Volume/Issue/Year Availability
    BibTip Others were also interested in ...
    Paper (German National Licenses)
    Fulltext
Close ⊗
This website uses cookies and the analysis tool Matomo. More information can be found here...