Multivariate analysis artificial neural network

Wu et al. [46] used the approach of an artificial neural network and applied it to drug release from osmotic pump tablets based on several coating parameters. Gabrielsson et al. [47] applied several different multivariate methods for both screening and optimization applied to the general topic of tablet formulation they included principal component analysis and... 

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Use of multivariate approaches based on classification modelling based on cluster analysis, factor analysis and the SIMCA technique [98,99], and the Kohonen artificial neural network [100]. All these methods, though rarely implemented, lead to very good results not achievable with classical strategies (comparisons, amino acid ratios, flow charts) and, moreover it is possible to know the confidence level of the classification carried out. 

Reasonable noise in the spectral data does not affect the clustering process. In this respect, cluster analysis is much more stable than other methods of multivariate analysis, such as principal component analysis (PCA), in which an increasing amount of noise is accumulated in the less relevant clusters. The mean cluster spectra can be extracted and used for the interpretation of the chemical or biochemical differences between clusters. HCA, per se, is ill-suited for a diagnostic algorithm. We have used the spectra from clusters to train artificial neural networks (ANNs), which may serve as supervised methods for final analysis. This process, which requires hundreds or thousands of spectra from each spectral class, is presently ongoing, and validated and blinded analyses, based on these efforts, will be reported. 

The methods of data analysis depend on the nature of the final output. If the problem is one of classification, a number of multivariate classifiers are available such as those based on principal components analysis (SIMCA), cluster analysis and discriminant analysis, or non-linear artificial neural networks. If the required output is a continuous variable, such as a concentration, then partial least squares regression or principal component regression are often used [20]. 

PCA can be used, for instance, to select a smaller set of descriptors to be used for multivariate regression and artificial neural networks. A set of descriptors can be divided into subclasses, such as geometrical, topological, or constitutional. A principal component analysis including the required output can then be performed for each subclass and the descriptor nearest to the output can be selected, checking if it improved the predictive power of a model. 

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). 

Additionally, a variety of analytical equipment and techniques that allow the examination of small- (and micro-) scale microbial cultures and their products have become available. Examples include near infrared and Fourier transform infrared spectroscopy, which offer the ability for in situ detection of specific compounds in fermentation broth [22]. However, sensitivity and the required sample volumes pose serious obstacles that still have to be overcome. Another alternative is offered by sensitive pyrolysis mass spectroscopy, which was demonstrated to be suitable for quantitative analysis of antibiotics in 5-pl aUquots of fermentation broth when combined with multivariate calibration and artificial neural networks [91]. The authors concluded that a throughput of about 12,000 isolates per month could be expected. Furthermore, standard chromatographic methods such as gas chromatography or high-performance liquid chromatography, possibly in combination with mass spectroscopy (MS) for detection, can provide simultaneous quantitative detection of many metabolic products. 

Olive oil is an ideal candidate for multivariate analysis. For economic reasons, the labelling of olive oils is frequently falsified (Collins, 1993 Firestone and Reina, 1987 Firestone, Carson and Reina, 1988 Firestone et al., 1985 Li-Chan, 1994 Simpkins and Harrison, 1995b Zamora, Navarro and Hidalgo, 1994), so there is a need for easy and cheap methods for identification. This chapter concentrates on the application of multivariate methods to nuclear magnetic resonance (NMR) and pyrolysis mass spectrometry (PyMS) data. It provides a brief introduction to principal components analysis (PCA), principal components regression (PCR), partial least squares regression (PLS) and the use of artificial neural networks (ANNs), then moves on to variable selection and its application to olive oil data. 

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