Izvestiya of Saratov University.

Chemistry. Biology. Ecology

ISSN 1816-9775 (Print)
ISSN 2541-8971 (Online)

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Kolesnikova S. S., Monakhova Y. B., Mushtakova S. P. SPECTRO-CHEMOMETRICAL DETERMINATION OF DIFFERENT METALS IN COMPLEX MIXTURES. Izvestiya of Saratov University. Chemistry. Biology. Ecology, 2011, vol. 11, iss. 1, pp. 25-31. DOI: 10.18500/1816-9775-2011-11-1-25-31, EDN: OGDLRX

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Language: 
Russian
Heading: 
Chemistry
Article type: 
Article
UDC: 
543.422.3:543.272.8
DOI: 
10.18500/1816-9775-2011-11-1-25-31
EDN: 
OGDLRX

SPECTRO-CHEMOMETRICAL DETERMINATION OF DIFFERENT METALS IN COMPLEX MIXTURES

Autors: 
Kolesnikova S. S., Saratov State University
Monakhova Yu. B., Saratov State University
Mushtakova Svetlana Petrovna, Saratov State University
Abstract: 

The application of independent component analysis algorithms for simultaneous determination of non-ferrous metals and metals of platinum group in their two- and three-component model mixtures was studied. The comparison between different chemometrical algorithms MILCA, SNICA, SIMPLISMA, RADICAL, JADE was made. The MILCA is the most effective algorithm for analysis of studied systems. The qualitative and quantitative analysis of artificial mixtures of systems of different metals was made.

Key words: 
NON-FERROUS METALS
METALS OF PLATINUM GROUP
chemometrics
INDEPENDENT COMPONENT ANALYSIS
Reference: 
  1. Иванов В.М. Гетероциклические азотсодержащие азосоединения. М., 1982. 230 с.                                                                 
  2. Leger M.N., Wentzell P.D. Dynamic Monte Carlo self-modeling curve resolution method for multicomponent mixtures // Chemom. Intell. Lab. Syst. 2002. Vol.62. P.171-188.                                                                                                                                                             
  3. Moussaoui S., Carteret C., Brie D., Mohammad-Djafari A. Bayesian analysis of spectral mixture data using Markov Chain Monte Carlo Methods // Chemom. Intell. Lab. Syst. 2006. Vol.81. P.137-148.                                                                                                                       
  4. Douglas S.C., Yuan Z., Oja E. Average convergence behavior of the FastICA algorithm for Blind Source Separation // ICA. 2006. P.790-798.           
  5. Feng Lu, Shu Li, Jian Le, Guiliang Chen, Yan Cao, Yunpeng Qi, Yifeng Chai, Yutian Wu. <http://www.sciencedirect.com/science?_ob=ArticleURL&_udi=B6TF4-4N7SBS3-6&_user=10&_coverDate=04%2F25%2F2007&_rdoc=1&_fmt=high&_orig=search&_sort=d&_docanchor=&view=c&_searchStrId=1385100963&_rerunOrigin=google&_acct=C000050221&_version=1&_urlVersion=0&_userid=10&md5=0bcb9eefe22701977164e8ee5d5be064> A new method for testing synthetic drugs adulterated in herbal medicines based on infrared spectroscopy // Anal. Chim. Acta. 2007. Vol.589. P.200-207.  EDN: KDWYWX                                                                                                                                                                           
  6. Astakhov S. A., Stogbauer H., Kraskov A., Grassberger P. Monte Carlo algorithm for Least Dependent Non-Negative Mixture Decomposition // Anal. Chem. 2006. Vol. 78. P. 1620-1627.                                                                                                                                        
  7. Stogbauer H., Kraskov A., Astakhov S.A., Grassberger P. Least-dependent-component analysis based on mutual infor-mation // Phys. Rev. E. 2004. Vol. 70. P. 066123 [17 p.].  EDN: JFMNFO                                                                                                                                                                                                                                                 
  8. Windig W., Guilment J. Spectroscopic comparison between humic and fulvic acids from urban waste compost and soil // Anal. Chem. 1991. Vol. 63. P. 1425-1432.                                                                                                                                                                                                                         
  9. Journґee1 M., Absil P.-A., Sepulchre R. Optimization on the orthogonal group for independent component analysis // Springer-Verlag Berlin Heidelberg. 2007. P. 564.                                                                                                                                                                                                                                                                                                                                          
  10.    CardosoJ.-F. High-order contrasts for independent com-ponent analysis // Neural Computation. 1999. Vol. 11, № 1. P. 157.                                                                                                                                                                           
  11. Milman B. Identification of chemical compounds // Trends in Analytical Chemistry. 2005. Vol. 24, № 6. P. 493.  EDN: MGZQIH                   
  12. Astakhov S. A., Stogbauer H., Kraskov A., Grassberger P. Spectral mixture decomposition by Least Dependent Compo-nent analysis // URL: http://arxiv.org/abs/physics/ 0412029.                                                                                                                                       
  13.  Cichocki A., Amari S. Adaptive Blind Signal and Image Processing. Learning Algorithms and Applications. N.Y., 2002.                                                    
Received: 
19.04.2010
Accepted: 
19.03.2010
Published: 
19.02.2011
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