Computational methods network analysis

Abstract Validation of analytical methods of well-characterised systems, such as are found in the pharmaceutical industry, is based on a series of experimental procedures to establish selectivity, sensitivity, repeatability, reproducibility, linearity of calibration, detection limit and limit of determination, and robustness. It is argued that these headings become more difficult to apply as the complexity of the analysis increases. Analysis of environmental samples is given as an example. Modern methods of analysis that use arrays of sensors challenge validation. The output may be a classification rather than a concentration of analyte, it may have been established by imprecise methods such as the responses of human taste panels, and the state space of possible responses is too large to cover in any experimental-design procedure. Moreover the process of data analysis may be done by non-linear methods such as neural networks. Validation of systems that rely on computer software is well established. 

In the past, nature was either studied by experiment and a link to a simple, all-encompassing theory was sought. In the last three decades, however, methods evolved to simulate atomic motion with computers, as described in Sects. 1.3.6-8, and to predict properties by neural network techniques, as summarized in Appendix 4. Both techniques are neither theory nor experiment. The simulations let us see the changes in the microscopic sttucture in slow motion and give a base for the understanding of physical problems which at present are too comphcated to fit into a simple theory. The neural network analysis, in tnm, looks for a method to use implicit functional relationships between the properties of different substances which are too difficult to ascertain explicidy [3]. The method of Appendix 6, produces a computer program which then correlates the properties. 

Hurtado, J.E. Alvarez, D.A. 2001. Neural-network-based reliability analysis a comparative study. Comput. Methods Appl. Mech. Engrg. 191 113-132. 

Algorithms Infrared Data Correlations with Chemical Structure Infrared Spectra Interpretation by the Characteristic Frequency Approach Machine Learning Techniques in Chemistry Molecular Models Visualization Neural Networks in Chemistry NMR Data Correlation with Chemical Structure Partial Least Squares Projections to Latent Structures (PLS) in Chemistry Shape Analysis Spectroscopic Databases Spectroscopy Computational Methods Structure Determination by Computer-based Spectrum Interpretation Zeolites Applications of Computational Methods. 

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