Applied Chemometrics for Scientists

摘要: Preface. 1 Introduction. 1.1 Development of Chemometrics. 1.2 Application Areas. 1.3 How to Use this Book. 1.4 Literature and Other Sources Information. References. 2 Experimental Design. 2.1 Why Design Experiments in Chemistry? 2.2 Degrees Freedom Error. 2.3 Analysis Variance Interpretation Errors. 2.4 Matrices, Vectors the Pseudoinverse. 2.5 Matrices. 2.6 Factorial Designs. 2.7 An Example a 2.8 Fractional 2.9 Plackett-Burman Taguchi 2.10 The Screening Factors Influencing Chemical Reaction. 2.11 Central Composite 2.12 Mixture 2.13 A Four Component Used Study Blending Olive Oils. 2.14 Simplex Optimization. 2.15 Leverage Confidence Models. 2.16 Designs for Multivariate Calibration. 3 Statistical Concepts. 3.1 Statistics Chemists. 3.2 3.3 Describing Data. 3.4 Normal Distribution. 3.5 Is Distribution Normal? 3.6 Hypothesis Tests. 3.7 Comparison Means: t-Test. 3.8 F-Test Variances. 3.9 Linear Regression. 3.10 More about Confidence. 3.11 Consequences Outliers Deal with Them. 3.12 Detection Outliers. 3.13 Shewhart Charts. 3.14 Control 4 Sequential Methods. 4.1 4.2 Correlograms. 4.3 Smoothing Functions Filters. 4.4 Fourier Transforms. 4.5 Maximum Entropy Bayesian 4.6 4.7 Peakshapes Chromatography Spectroscopy. 4.8 Derivatives Spectroscopy Chromatography. 4.9 Wavelets. 5 Pattern Recognition. 5.1 5.2 Principal Components Analysis. 5.3 Graphical Representation Scores Loadings. 5.4 Comparing Patterns. 5.5 Preprocessing. 5.6 Unsupervised Recognition: Cluster 5.7 Supervised 5.8 Classification Techniques. 5.9 K Nearest Neighbour Method. 5.10 Many Characterize Dataset? 5.11 Multiway 6 6.1 6.2 Univariate 6.3 Calibration Mixtures. 6.4 Multiple 6.5 6.6 Partial Least Squares. 6.7 Good is What Most Appropriate Model? 6.8 7 Coupled 7.1 7.2 Preparing 7.3 Composition 7.4 Purity Curves. 7.5 Similarity Based 7.6 Evolving Window Factor 7.7 Derivative 7.8 Deconvolution Evolutionary Signals. 7.9 Noniterative Methods Resolution. 7.10 Iterative 8 Equilibria, Reactions Process Analytics. 8.1 Equilibria using 8.2 Spectroscopic Monitoring Reactions. 8.3 Kinetics Models Quantitative 8.4 Developments On-line 8.5 Analytical Technology Initiative. 9 Improving Yields Processes Using 9.1 9.2 Performance Synthetic 9.3 that Influence 9.4 Optimizing Variables. 9.5 Handling Variables 9.6 10 Biological Medical Applications 10.1 10.2 Taxonomy. 10.3 Discrimination. 10.4 Mahalanobis Distance. 10.5 Contingency Tables. 10.6 Support Vector Machines. 10.7 Discriminant 10.8 Micro-organisms. 10.9 Diagnosis 10.10 Metabolomics Nuclear Magnetic Resonance. 11 Macromolecules. 11.1 11.2 Sequence Alignment Scoring Matches. 11.3 Similarity. 11.4 Tree Diagrams. 11.5 Phylogenetic Trees. 12 Image 12.1 12.2 Scaling Images. 12.3 Filtering Image. 12.4 Enhancement 12.5 Regression 12.6 Alternating Squares as Employed 12.7 In 13 Food. 13.1 13.2 Determine Origin Food Product 13.3 Near Infrared 13.4 13.5 Sensory Analysis: Linking Properties. 13.6 Varimax Rotation. 13.7 Calibrating Descriptors Composition. Index.