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  • 1
    Electronic Resource
    Electronic Resource
    Optimization of pyrite bioleaching using Sulfolobus acidocaldarius (1993)
    Springer
    Applied microbiology and biotechnology 38 (1993), S. 702-707 
    Details
    ISSN: 1432-0614
    Source: Springer Online Journal Archives 1860-2000
    Topics: Biology , Process Engineering, Biotechnology, Nutrition Technology
    Notes: Abstract Optimization of batch pyrite bioleaching with Sulfolobus acidocaldarius was performed using statistical modelling and experimental design. First a screening design was made followed by response surface modelling. The dominating factors identified were pH, pulp density and particle size. The highest batch leaching rate after optimization was 270 mg iron·l−1·h−1 for 6% (w/v) pulp density, pH = 1.5 and particle size 〈20 μm. This represents a 3.5-fold increase from the leaching rate of 80 mg iron·l−1·h−1 obtained under our standard laboratory conditions.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/BF00182813
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    Paper (German National Licenses)
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  • 2
    Electronic Resource
    Electronic Resource
    Residual bilinearization. Part 1: Theory and algorithms (1990)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 4 (1990), S. 79-90 
    Details
    ISSN: 0886-9383
    Keywords: PLS ; Three-way matrices ; Calibration ; Residual bilinearization ; Background correction ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: When using hyphenated methods in analytical chemistry, the data obtained for each sample are given as a matrix. When a regression equation is set up between an unknown sample (a matrix) and a calibration set (a stack of matrices), the residual is a matrix R.The regression equation is usually solved by minimizing the sum of squares of R. If the sample contains some constituent not calibrated for, this approach is not valid. In this paper an algorithm is presented which partitions R into one matrix of low rank corresponding to the unknown constituents, and one random noise matrix to which the least squares restrictions are applied. Properties and possible applications of the algorithm are also discussed.In Part 2 of this work an example from HPLC with diode array detection is presented and the results are compared with generalized rank annihilation factor analysis (GRAFA).
    Additional Material: 2 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180040109
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    Paper (German National Licenses)
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  • 3
    Electronic Resource
    Electronic Resource
    A PLS kernel algorithm for data sets with many variables and fewer objects. Part 1: Theory and algorithm (1994)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 8 (1994), S. 111-125 
    Details
    ISSN: 0886-9383
    Keywords: PLS regression algorithm ; Kernel ; Many-variable data sets ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A fast PLS regression algorithm dealing with large data matrices with many variables (K) and fewer objects (N) is presented For such data matrices the classical algorithm is computer-intensive and memory-demanding. Recently, Lindgren et al. (J. Chemometrics, 7, 45-49 (1993)) developed a quick and efficient kernel algorithm for the case with many objects and few variables. The present paper is focused on the opposite case, i.e. many variables and fewer objects. A kernel algorithm is presented based on eigenvectors to the ‘kernel’ matrix XX TYYT, which is a square, non-symmetric matrix of size N × N, where N is the number of objects. Using the kernel matrix and the association matrices XXT (N × N) and YYT (N × N), it is possible to calculate all score and loading vectors and hence conduct a complete PLS regression including diagnostics such as R2. This is done without returning to the original data matrices X and Y. The algorithm is presented in equation form, with proofs of some new properties and as MATLAB code.
    Additional Material: 5 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180080204
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  • 4
    Electronic Resource
    Electronic Resource
    Interactive variable selection (IVS) for PLS. Part 1: Theory and algorithms (1994)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 8 (1994), S. 349-363 
    Details
    ISSN: 0886-9383
    Keywords: Variable selection ; PLS ; Calibration ; Modelling ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A modified PLS algorithm is introduced with the goal of achieving improved prediction ability. The method, denoted IVS-PLS, is based on dimension-wise selective reweighting of single elements in the PLS weight vector w. Cross-validation, a criterion for the estimation of predictive quality, is used for guiding the selection procedure in the modelling stage. A threshold that controls the size of the selected values in w is put inside a cross-validation loop. This loop is repeated for each dimension and the results are interpreted graphically. The manipulation of w leads to rotation of the classical PLS solution. The results of IVS-PLS are different from simply selecting X-variables prior to modelling. The theory is explained and the algorithm is demonstrated for a simulated data set with 200 variables and 40 objects, representing a typical spectral calibration situation with four analytes. Improvements of up to 70% in external PRESS over the classical PLS algorithm are shown to be possible.
    Additional Material: 9 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180080505
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  • 5
    Electronic Resource
    Electronic Resource
    The kernel algorithm for PLS (1993)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 7 (1993), S. 45-59 
    Details
    ISSN: 0886-9383
    Keywords: Partial least squares ; PLS algorithm ; Kernel ; Multivariate image analysis ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: A fast and memory-saving PLS regression algorithm for matrices with large numbers of objects is presented. It is called the kernel algorithm for PLS. Long (meaning having many objects, N) matrices X (N × K) and Y (N × M) are condensed into a small (K × K) square ‘kernel’ matrix XTYYTX of size equal to the number of X-variables. Using this kernel matrix XTYYTX together with the small covariance matrices XTX (K × K), XTY (K × M) and YTY (M × M), it is possible to estimate all necessary parameters for a complete PLS regression solution with some statistical diagnostics. The new developments are presented in equation form. A comparison of consumed floating point operations is given for the kernel and the classical PLS algorithm. As appendices, a condensed matrix algebra version of the kernel algorithm is given together with the MATLAB code.
    Additional Material: 6 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180070104
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    Paper (German National Licenses)
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  • 6
    Electronic Resource
    Electronic Resource
    Dedicated principal properties for peptide QSARs: Theory and applications (1990)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 4 (1990), S. 241-253 
    Details
    ISSN: 0886-9383
    Keywords: Peptide QSAR ; Dedicated principal properties ; Principal component analysis ; Partial least squares projections to latent structures ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Dedicated principal properties (DPPs) are presented. The DPP approach is an iterative process based on PLS wherein principal properties are calibrated on QSARs with improved predictive capabilities as a consequence. The DPP approach is illustrated by two examples. In the first example the z-scales developed for structural description in peptide QSARs are optimized according to the DPP approach. In the second example DDPs based on simulated pentapeptide-like sequences are calculated and evaluated.A comparison is made of the predictive power of peptide QSAR models based on structural description of the peptides with (a) 29 physico-chemical variables with (b) three principal properties (z-scales) and (c) three sets of DPPs. The results show that models based on all 29 variables give better predictions than models based on principal properties. Modelling based on DPPs improves the predictive power to the same level as models based on all 29 variables.
    Additional Material: 7 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180040305
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    Paper (German National Licenses)
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  • 7
    Electronic Resource
    Electronic Resource
    Residual bilinearization. Part 2: Application to HPLC - diode array data and comparison with rank annihilation factor analysis (1990)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 4 (1990), S. 135-146 
    Details
    ISSN: 0886-9383
    Keywords: PLS ; Three-way matrices ; Calibration ; Residual bilinearization ; Background correction ; GRAFA ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: The partial least squares-residual bilinearization (PLS-RBL) approach to background correction presented in Part 1 of this work is demonstrated with an example from HPLC with diode array detection. Data are also evaluated with generalized rank annihilation factor analysis (GRAFA) and results are compared.
    Additional Material: 4 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180040205
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    Paper (German National Licenses)
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  • 8
    Electronic Resource
    Electronic Resource
    Kernel-based PLS regression; Cross-validation and applications to spectral data (1994)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 8 (1994), S. 377-389 
    Details
    ISSN: 0886-9383
    Keywords: Kernel PLS regression ; Cross-validation ; Model dimensionality ; Multivariate image regression ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Multivariate images are very large data structures and any type of regression for their analysis is very computer-intensive. Kernel-based partial least squares (PLS) regression, presented in an earlier paper, makes the calculation phase more rapid and less demanding in computer memory. The present paper is a direct continuation of the first paper. In this study the kernel PLS algorithm is extended to include cross-validation for determination of the optimal model dimensionality. To show the applicability of the kernel algorithm, two examples from multivariate image analysis are used. The first example is an image from an airborne scanner of size 9 × 512 × 512. It consists of nine images which are regressed against a constructed dependent image to test the accuracy of the kernel algorithm when used on large data structures. The second example is a satellite image of size 7 × 512 × 512. Several different regression models are presented together with a comparison of their predictive capabilities. The regression models are also used as examples for showing the use of cross-validation.
    Additional Material: 3 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180080604
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    Paper (German National Licenses)
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