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Artificial neural networks analysis

Turkoglu J, Ozarslan R, Sakr A. Artificial neural network analysis of a direct compression tabletting study. Eur J Pharm Biopharm 1995 41 315-22. [Pg.699]

Freeman, R. Goodacre, R. Sisson, P. R. Magee, J. G. Ward, A. C. Lightfoot, N. F. Rapid identification of species within the Mycobacterium tuberculosis complex by artificial neural network analysis of pyrolysis mass spectra. J. Med. Microbiol. 1994, 40,170-173. [Pg.341]

Chun, J. Atalan, E. Ward, A. C. Goodfellow, M. Artificial neural network analysis of pyrolysis mass spectrometric data in the identification of Streptomyces strains. FEMS Microbiol. Lett. 1993,107,321-325. [Pg.341]

Ziegler, C., Harsch, A., and Gopel, W. (2000). Natural neural networks for quantitative sensing of neurochemicals An artificial neural network analysis. Sens. Actuators B Chem. 65,160-162. [Pg.44]

Gutes, A., Cespedes, F., Alegret, S., and del Valle, M. (2005). Determination of phenolic compounds by a polyphenol oxidase amperometric biosensor and artificial neural network analysis. Biosens. Bioelectron. 20(8), 1668-1673. [Pg.112]

Z. Roger, Selection of the quasi-optimal inputs in chemometric modelling by artificial neural networks analysis, Anal. Chim. Acta, 490(1-2), 2003, 31-40. [Pg.278]

N.E. Rapid Identification of Species within the Mycobacterium Tuberculosis Complex by Artificial Neural Network Analysis of Pyrolysis Mass Spectra, J. Med. Microbiol. 40(3), 170-173 (1994). [Pg.143]

Boydston-White, S., Romeo, M.) Chernenko, T Regina, A., Miljkovic, M. and Diem, M. (2006) Cell-cycle-dependent variations in FTIR micro-spedra of single proliferating HeLa cells principal component and artificial neural network analysis. Biochim. Biophys. Acta, 1758 (7), 908-14. [Pg.200]

Buchl, N. R., Wenning, M., Seiler, H., Mietke-Hofinann, H., Scherer, S. (2008). Reliable identification of closely related issatchenkia and pichia species using artificial neural network analysis of fourier-transform infrared spectra. Yeast, 25, 787-798. [Pg.99]

Shah HN, Rajakaruna L, Ball G, Misra R, Al-Shahib A, Fang M, Gharbia SE. Tracing the transition of methicillin resistance in sub-populations of Staphylococcus aureus, using SELDI-TOF mass spectrometry and artificial neural network analysis. Syst Appl Microbiol. 2011 34 81-6. [Pg.302]

Chen, Y.D., Zheng, S., Yu, J.K. and Hu, X., Artificial neural networks analysis of surface-enhanced laser desorption/ionization mass spectra of serum protein pattern distinguishes colorectal cancer from healthy population. Clin. Cancer Res., 10(24), 8380-8385 (2004). [Pg.500]

Such definitive classification may be achieved with the aid of multivariate pattern recognition techniques such as hierarchical clustering, linear discriminant analysis (LDA) and artificial neural network analysis. Hierarchical clustering techniques compare sets of data (e.g. individually acquired spectra or spectra acquired by mapping of tissue) and group the data according to some measure of similarity. For mapping data, the application of cluster analysis... [Pg.113]


See other pages where Artificial neural networks analysis is mentioned: [Pg.464]    [Pg.282]    [Pg.468]    [Pg.114]    [Pg.170]    [Pg.35]    [Pg.25]    [Pg.441]    [Pg.216]    [Pg.226]    [Pg.178]    [Pg.413]    [Pg.90]   
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