Chemometrics artificial neural networks

Dixit, V. et al.. Identification and qnantification of industrial grade glycerol adulteration in red wine with Fourier transform infrared spectroscopy using chemometrics and artificial neural networks, Appl. Spectros., 59, 1553, 2005. 

Since in many applications minor absorption changes have to be detected against strong, interfering background absorptions of the matrix, advanced chemometric data treatment, involving techniques such as wavelet analysis, principle component analysis (PCA), partial least square (PLS) methods and artificial neural networks (ANN), is a prerequisite. 

Nature-inspired Methods in Chemometrics Genetic Algorithms and Artificial Neural Networks, edited by R. Leardi... 

Also nonlinear methods can be applied to represent the high-dimensional variable space in a smaller dimensional space (eventually in a two-dimensional plane) in general such data transformation is called a mapping. Widely used in chemometrics are Kohonen maps (Section 3.8.3) as well as latent variables based on artificial neural networks (Section 4.8.3.4). These methods may be necessary if linear methods fail, however, are more delicate to use properly and are less strictly defined than linear methods. 

McGovern et al.26 analyzed the expression of heterologous proteins in E. coli via pyrolysis mass spectrometry and FT-IR. The application was to a2-interferon production. To analyze the data, artificial neural networks (ANN) and PLS were utilized. Because cell pastes contain more mass than the supernatant, these were used for quantitative analyses. Both the MS and IR data were difficult to interpret, but the chemometrics used allowed researchers to gain some knowledge of the process. The authors show graphics indicating the ability to follow production via either technique. 

Y. Liu, B.R. Upadhyaya and M. Naghedolfeizi, Chemometric data analysis using artificial neural networks, Appl. Spectrosc., 47, 12-23 (1993). 

Key Words 2D-QSAR traditional QSAR 3D-QSAR nD-QSAR 4D-QSAR receptor-independent QSAR receptor-dependent QSAR high throughput screening alignment conformation chemometrics principal components analysis partial least squares artificial neural networks support vector machines Binary-QSAR selecting QSAR descriptors. 

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. 

R. J. H. Waddell, N. NicDaeid and D. Littlejohn, Classification of ecstasy tablets using trace metal analysis with the application of chemometric procedures and artificial neural networks algorithms. Analyst, 129(3), 2004, 235-240. 

E. A. Hernandez-Caraballo, F. Rivas, A. G. Perez and L. M. Marco-Parra, Evaluation of chemometric techniques and artificial neural networks for cancer screening using Cu, Fe, Se and Zn concentrations in blood serum. Anal. Chim. Acta, 533(2), 2005, 161-168. 

Blanco, M., Coello, J., Iturriaga, H., Maspoch, S. and Pages, J., NIR Calibration in Non-linear Systems Different PLS Approaches and Artificial Neural Networks Chemometrics Intell. Lab. Syst. 2000, 50, 75-82. 

One of the emerging biological and biomedical application areas for vibrational spectroscopy and chemometrics is the characterization and discrimination of different types of microorganisms [74]. A recent review of various FTIR (Fourier transform infrared spectrometry) techniques describes such chemometrics methods as hierarchical cluster analysis (HCA), principal component analysis (PCA), and artificial neural networks (ANN) for use in taxonomical classification, discrimination according to susceptibility to antibiotic agents, etc. [74],... 

Leardi R (ed) (2003) Nature-inspired methods in chemometrics Genetic algorithms and artificial neural networks. Elsevier, Amsterdam... 

Frake and co-workers " extensively evaluated numerous chemometric techniques for the NIRS prediction of mass median particle size determination of lactose monohydrate. Models evaluated in zero order (untreated) and second derivative were MLR, PLS (partial least squares), and ANN (artificial neural network). The researchers concluded that there is more than one way to treat data and achieve a good calibration model. The group also confirms previous observations that derivitization of data does not remove particle size effects (previously thought to contribute to baseline shift). 

The chemometric methods discussed above have found widespread applications in chromatography, and many theoretical and practical chromatographers have become familiar with these techniques and have applied them successfully. However, other less well-known methods have also found applicability in the analysis of chromatographic retention data. Thus, canonical variate analysis has been applied in pyrolysis GC-MS, artificial neural network for the prediction of GLC retention indices, and factor analysis for the study of the retention behavior of A-benzylideneaniline derivatives. 

To establish a correlation between the concentrations of different kinds of nucleosides in a complex metabolic system and normal or abnormal states of human bodies, computer-aided pattern recognition methods are required (15, 16). Different kinds of pattern recognition methods based on multivariate data analysis such as principal component analysis (PCA) (8), partial least squares (16), stepwise discriminant analysis, and canonical discriminant analysis (10, 11) have been reported. Linear discriminant analysis (17, 18) and cluster analysis were also investigated (19,20). Artificial neural network (ANN) is a branch of chemometrics that resolves regression or classification problems. The applications of ANN in separation science and chemistry have been reported widely (21-23). For pattern recognition analysis in clinical study, ANN was also proven to be a promising method (8). 

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