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Applied Chemometrics for Scientists

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ISBN-10: 0470016868

ISBN-13: 9780470016862

Edition: 2007

Authors: Richard G. Brereton

List price: $124.95
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Introducing most of the basic tools of chemometrics including experimental design, signal analysis, statistical methods for analytical chemistry and multivariate methods, this book goes on to discuss important applications including food chemistry, reaction monitoring, optimisation of processes, and medical applications.
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Book details

List price: $124.95
Copyright year: 2007
Publisher: John Wiley & Sons, Incorporated
Publication date: 4/2/2007
Binding: Hardcover
Pages: 396
Size: 6.88" wide x 9.90" long x 1.07" tall
Weight: 2.002
Language: English

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