Assuming only background knowledge of algebra and elementary calculus, and access to a modern personal computer, Nonlinear Computer Modeling of Chemical and Biochemical Data presents the fundamental basis and procedures of data modeling by computer using nonlinear regression analysis. Bypassing the need for intermediary analytical stages, this method allows for rapid analysis of highly complex processes, thereby enabling reliable information to be extracted from raw experimental data. By far the greater part of the book is devoted to selected applications of computer modeling to various experiments used in chemical and biochemical research. The discussions include a short review of principles and models for each technique, examples of computer modeling for real and theoretical data sets, and examples from the literature specific to each instrumental technique. The book also offers detailed tutorial on how to construct suitable models and a score list of appropriate mathematics software packages.
Assuming only background knowledge of algebra and elementary calculus, and access to a modern personal computer, Nonlinear Computer Modeling of Chemical and Biochemical Data presents the fundamental basis and procedures of data modeling by computer using nonlinear regression analysis. Bypassing the need for intermediary analytical stages, this method allows for rapid analysis of highly complex processes, thereby enabling reliable information to be extracted from raw experimental data. By far the greater part of the book is devoted to selected applications of computer modeling to various experiments used in chemical and biochemical research. The discussions include a short review of principles and models for each technique, examples of computer modeling for real and theoretical data sets, and examples from the literature specific to each instrumental technique. The book also offers detailed tutorial on how to construct suitable models and a score list of appropriate mathematics software packages.
Part I: General Introduction to Regression Analysis. Introduction to Nonlinear Modeling of Data: What is Nonlinear Modeling? Objectives of this Book. Anayzing Data with Regression Analysis: Linear Models. Nonlinear Regression Analysis. Sources of Mathematics Software Capable of Linear and Nonlinear Regression. Building Models for Experimental Data: Sources of Data and Background Contributions. Examples of Model Types. Finding the Best Models. Correlation Between Parameters and Other Convergence Problems: Correlations and How to Minimize Them. Avoiding Pitfalls in Convergence. Part II: Selected Applications. Titrations: Introduction. Macromolecular Equilibria and Kinetics: Linked Thermodynamic Models: The Concept of Linked Functions. Applications of Thermodynamic Linkage. Secondary Structure of Proteins by Infrared Spectroscopy: Introduction. Analysis of Spectra--Examples. Nuclear Magnetic Resonance Relaxation: Fundamentals of NMR Relaxation. Applications from NMR in Solution. Applications from NMR in the Solid State. Small-Angle X-Ray Scattering (SAXS) of Proteins: Theoretical Considerations. Applications. Ultracentrifugation of Macromolecules: Sedimentation. Voltammetric Methods: General Characteristics of Voltammetry. Steady State Voltammetry. Cyclic Voltammetry. Square Wave Voltammetry.Chronocoulometry: Basic Principles. Estimation of Diffusion Coefficients. Surface Concentrations of Adsorbates from Double Potential Steps. Rate Constant for Reaction of a Product of an Electrochemical Reaction. Automated Resolution of Multiexponential Decay Data: Considerations for Analyses of Overlapped Signals. Automated Analysis of Data with an Unknown Number of Exponentials. Chromatography and Multichannel Detection Methods: Overlapped Chromatographic Peaks with Single-Channel Detection. Multichannel Detection. Appendix. Subject Index.
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