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| Preface | |
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| Introduction | |
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| Development of Chemometrics | |
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| Application Areas | |
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| How to Use this Book | |
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| Literature and Other Sources of Information | |
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| References | |
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| Experimental Design | |
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| Why Design Experiments in Chemistry? | |
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| Degrees of Freedom and Sources of Error | |
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| Analysis of Variance and Interpretation of Errors | |
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| Matrices, Vectors and the Pseudoinverse | |
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| Design Matrices | |
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| Factorial Designs | |
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| An Example of a Factorial Design | |
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| Fractional Factorial Designs | |
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| Plackett-Burman and Taguchi Designs | |
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| The Application of a Plackett-Burman Design to the Screening of Factors Influencing a Chemical Reaction | |
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| Central Composite Designs | |
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| Mixture Designs | |
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| A Four Component Mixture Design Used to Study Blending of Olive Oils | |
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| Simplex Optimization | |
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| Leverage and Confidence in Models | |
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| Designs for Multivariate Calibration | |
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| References | |
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| Statistical Concepts | |
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| Statistics for Chemists | |
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| Errors | |
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| Describing Data | |
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| The Normal Distribution | |
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| Is a Distribution Normal? | |
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| Hypothesis Tests | |
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| Comparison of Means: the t-Test | |
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| F-Test for Comparison of Variances | |
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| Confidence in Linear Regression | |
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| More about Confidence | |
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| Consequences of Outliers and How to Deal with Them | |
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| Detection of Outliers | |
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| Shewhart Charts | |
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| More about Control Charts | |
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| References | |
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| Sequential Methods | |
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| Sequential Data | |
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| Correlograms | |
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| Linear Smoothing Functions and Filters | |
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| Fourier Transforms | |
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| Maximum Entropy and Bayesian Methods | |
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| Fourier Filters | |
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| Peakshapes in Chromatography and Spectroscopy | |
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| Derivatives in Spectroscopy and Chromatography | |
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| Wavelets | |
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| References | |
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| Pattern Recognition | |
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| Introduction | |
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| Principal Components Analysis | |
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| Graphical Representation of Scores and Loadings | |
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| Comparing Multivariate Patterns | |
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| Preprocessing | |
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| Unsupervised Pattern Recognition: Cluster Analysis | |
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| Supervised Pattern Recognition | |
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| Statistical Classification Techniques | |
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| K Nearest Neighbour Method | |
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| How Many Components Characterize a Dataset? | |
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| Multiway Pattern Recognition | |
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| References | |
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| Calibration | |
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| Introduction | |
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| Univariate Calibration | |
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| Multivariate Calibration and the Spectroscopy of Mixtures | |
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| Multiple Linear Regression | |
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| Principal Components Regression | |
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| Partial Least Squares | |
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| How Good is the Calibration and What is the Most Appropriate Model? | |
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| Multiway Calibration | |
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| References | |
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| Coupled Chromatography | |
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| Introduction | |
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| Preparing the Data | |
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| Chemical Composition of Sequential Data | |
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| Univariate Purity Curves | |
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| Similarity Based Methods | |
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| Evolving and Window Factor Analysis | |
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| Derivative Based Methods | |
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| Deconvolution of Evolutionary Signals | |
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| Noniterative Methods for Resolution | |
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| Iterative Methods for Resolution | |
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| Equilibria, Reactions and Process Analytics | |
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| The Study of Equilibria using Spectroscopy | |
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| Spectroscopic Monitoring of Reactions | |
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| Kinetics and Multivariate Models for the Quantitative Study of Reactions | |
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| Developments in the Analysis of Reactions using On-line Spectroscopy | |
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| The Process Analytical Technology Initiative | |
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| References | |