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  • 1
    Electronic Resource
    Electronic Resource
    Mechanochemical Synthesis of Zinc Ferrite, ZnFe〈sub〉2〈/sub〉O〈sub〉4〈/sub〉 (2007)
    s.l. ; Stafa-Zurich, Switzerland
    Materials science forum Vol. 534-536 (Jan. 2007), p. 201-204 
    Details
    ISSN: 1662-9752
    Source: Scientific.Net: Materials Science & Technology / Trans Tech Publications Archiv 1984-2008
    Topics: Mechanical Engineering, Materials Science, Production Engineering, Mining and Metallurgy, Traffic Engineering, Precision Mechanics
    Notes: Mechanochemical synthesis of zinc ferrite, ZnFe2O4, was attempted from a powdermixture of iron (III) oxide, alpha-Fe2O3 and zinc (II) oxide, ZnO. Nanocrystalline zinc ferrite,ZnFe2O4 powders were successfully synthesized only by milling for 30 hours. The X-raydiffraction spectrum of the as-milled powders (without heating) showed twelve ZnFe2O4 peaks andfour weak peaks of coexisting unreacted Fe2O3. The crystallite size of the mechanochemicallysynthesizedZnFe2O4 was 26.3 nm. Evidence of the ZnFe2O4 formation was absent for the powdersmilled for 10 and 20 hours; milling lowered the crystallinity of the starting materials. Heating aftermilling enhanced the formation of ZnFe2O4, the crystal growth of ZnFe2O4 and the unreactedstarting materials. The unreacted starting materials decreased their amounts by heating at highertemperatures
    Type of Medium: Electronic Resource
    URL: http://www.tib-hannover.de/fulltexts/2011/0528/02/14/transtech_doi~10.4028%252Fwww.scientific.net%252FMSF.534-536.201.pdf
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  • 2
    Electronic Resource
    Electronic Resource
    Classification of wine samples by means of artificial neural networks and discrimination analytical methods (1997)
    Springer
    Fresenius' journal of analytical chemistry 359 (1997), S. 143-149 
    Details
    ISSN: 1432-1130
    Source: Springer Online Journal Archives 1860-2000
    Topics: Chemistry and Pharmacology
    Notes: Abstract  The three-layer artificial neural network (ANN) model with back-propagation (BP) of error was used to classify wine samples in six different regions based on the measurements of trace amounts of B, V, Mn, Zn, Fe, Al, Cu, Sr, Ba, Rb, Na, P, Ca, Mg,  K using an inductively coupled plasma optical emission spectrometer (ICP-OES). The ANN architecture and parameters were optimized. The results obtained with ANN were compared with those obtained by cluster analysis, principal component analysis, the Bayes discrimination method and the Fisher discrimination method. A satisfactory prediction result (100%) by an artificial neural network using the jackknife leave-one-out procedure was obtained for the classification of wine samples containing six categories.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1007/s002160050551
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  • 3
    Electronic Resource
    Electronic Resource
    Robust principal component analysis by projection pursuit (1993)
    New York, NY : Wiley-Blackwell
    Journal of Chemometrics 7 (1993), S. 527-541 
    Details
    ISSN: 0886-9383
    Keywords: Principal component analysis ; Projection pursuit ; Simulated annealing algorithm ; Robust statistics ; Chemistry ; Analytical Chemistry and Spectroscopy
    Source: Wiley InterScience Backfile Collection 1832-2000
    Topics: Chemistry and Pharmacology
    Notes: Principal component analysis (PCA) is a widely used technique in chemometrics. The classical PCA method is, unfortunately, non-robust, since the variance is adopted as the objective function. In this paper, projection pursuit (PP) is used to carry out PCA with a criterion which is more robust than the variance. In addition, the generalized simulated annealing (GSA) algorithm is introduced as an optimization procedure in the process of PP calculation to guarantee the global optimum. The results for simulated data sets show that PCA via PP is resistant to the deviation of the error distribution from the normal one. The method is especially recommended for use in cases with possible outlier(s) existing in the data.
    Additional Material: 7 Ill.
    Type of Medium: Electronic Resource
    URL: http://dx.doi.org/10.1002/cem.1180070606
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