Chemometrics Data Analysis for the Laboratory and Chemical Plant By Richard G. Brereton (informative)
Free download Chemometrics Data Analysis for the Laboratory and Chemical Plant By Richard G. Brereton
Authors of: Chemometrics Data Analysis for the Laboratory and Chemical Plant By Richard G. Brereton
Richard G. Brereton
Table of Contents in Chemometrics Data Analysis for the Laboratory and Chemical Plant By Richard G. Brereton
Preface
This text is a product of several years activities from myself. First and foremost, the task of educating students in my research group from a wide variety of backgrounds over the past 10 years has been a significant formative experience, and this has allowed me to develop a large series of problems which we set every 3 weeks and present answers in seminars.
From my experience, this is the best way to learn chemometrics! In addition, I have had the privilege to organise international quality courses mainly for industrialists with the participation as tutors of many representatives of the best organisations and institutes around the world, and I have learnt from them.
Different approaches are normally taken when teaching industrialists who may been countering chemometrics for the first time in mid-career and have a limited period of a few days to attend a condensed course, and university students who have several months or even years to practice and improve. However, it is hoped that this book represents a symbiosis of both needs.
Acknowledgements
1 Introduction
1.1 Points of View
1.2 Software and Calculations
1.3 Further Reading
1.3.1 General
1.3.2 Specific Areas
1.3.3 Internet Resources
1.4 References
2 Experimental Design
2.1 Introduction
2.2 Basic Principles
2.2.1 Degrees of Freedom
2.2.2 Analysis of Variance and Comparison of Errors
2.2.3 Design Matrices and Modelling
2.2.4 Assessment of Significance
2.2.5 Leverage and Confidence in Models
2.3 Factorial Designs
2.3.1 Full Factorial Designs
2.3.2 Fractional Factorial Designs
2.3.3 Plackett–Burman and Taguchi Designs
2.3.4 Partial Factorials at Several Levels: Calibration Designs
2.4 Central Composite or Response Surface Designs
2.4.1 Setting Up the Design
2.4.2 Degrees of Freedom
2.4.3 Axial Points
2.4.4 Modelling
2.4.5 Statistical Factors
2.5 Mixture Designs
2.5.1 Mixture Space
2.5.2 Simplex Centroid
2.5.3 Simplex Lattice
2.5.4 Constraints
2.5.5 Process Variables
2.6 Simplex Optimisation
2.6.1 Fixed Sized Simplex
2.6.2 Elaborations
2.6.3 Modified Simplex
2.6.4 Limitations
Problems
3 Signal Processing
3.1 Sequential Signals in Chemistry
3.1.1 Environmental and Geological Processes
3.1.2 Industrial Process Control
3.1.3 Chromatograms and Spectra
3.1.4 Fourier Transforms
3.1.5 Advanced Methods
3.2 Basics
3.2.1 Peakshapes
3.2.2 Digitisation
3.2.3 Noise
3.2.4 Sequential Processes
3.3 Linear
3.3.1 Smoothing Functions
3.3.2 Derivatives
3.3.3 Convolution
3.4 Correlograms and Time Series Analysis
3.4.1 Auto-correlograms
3.4.2 Cross-correlograms
3.4.3 Multivariate Correlograms
3.5 Fourier Transform Techniques
3.5.1 Fourier Transforms
3.5.2 Fourier Filters
3.5.3 Convolution Theorem
3.6 Topical Methods
3.6.1 Kalman Filters
3.6.2 Wavelet Transforms
3.6.3 Maximum Entropy (Maxent) and Bayesian Methods
Problems
4 Pattern Recognition
4.1 Introduction
4.1.1 Exploratory Data Analysis
4.1.2 Unsupervised Pattern Recognition
4.1.3 Supervised Pattern Recognition
4.2 The Concept and Need for Principal Components Analysis
4.2.1 History
4.2.2 Case Studies
4.2.3 Multivariate Data Matrices
4.2.4 Aims of PCA
4.3 Principal Components Analysis: the Method
4.3.1 Chemical Factors
4.3.2 Scores and Loadings
4.3.3 Rank and Eigenvalues
4.3.4 Factor Analysis
4.3.5 Graphical Representation of Scores and Loadings
4.3.6 Preprocessing
4.3.7 Comparing Multivariate Patterns
4.4 Unsupervised Pattern Recognition: Cluster Analysis
4.4.1 Similarity
4.4.2 Linkage
4.4.3 Next Steps
4.4.4 Dendrograms
4.5 Supervised Pattern Recognition
4.5.1 General Principles
4.5.2 Discriminant Analysis
4.5.3 SIMCA
4.5.4 Discriminant PLS
4.5.5 K Nearest Neighbours
4.6 Multiway Pattern Recognition
4.6.1 Tucker3 Models
4.6.2 PARAFAC
4.6.3 Unfolding
Problems
5 Calibration
5.1 Introduction
5.1.1 History and Usage
5.1.2 Case Study
5.1.3 Terminology
5.2 Univariate Calibration
5.2.1 Classical Calibration
5.2.2 Inverse Calibration
5.2.3 Intercept and Centring
5.3 Multiple Linear Regression
5.3.1 Multidetector Advantage
5.3.2 Multiwavelength Equations
5.3.3 Multivariate Approaches
5.4 Principal Components Regression
5.4.1 Regression
5.4.2 Quality of Prediction
5.5 Partial Least
5.5.1 PLS1
5.5.2 PLS2
5.5.3 Multiway PLS
5.6 Model Validation
5.6.1 Autoprediction
5.6.2 Cross-validation
5.6.3 Independent Test Sets
Problems
6 Evolutionary Signals
6.1 Introduction
6.2 Exploratory Data Analysis and Preprocessing
6.2.1 Baseline Correction
6.2.2 Principal Component Based Plots
6.2.3 Scaling the Data
6.2.4 Variable Selection
6.3 Determining Composition
6.3.1 Composition
6.3.2 Univariate Methods
6.3.3 Correlation and Similarity Based Methods
6.3.4 Eigenvalue Based Methods
6.3.5 Derivatives
6.4 Resolution
6.4.1 Selectivity for All Components
6.4.2 Partial Selectivity
6.4.3 Incorporating Constraints
Problems
Appendices
A.1 Vectors and Matrices
A.2 Algorithms
A.3 Basic Statistical Concepts
A.4 Excel for Chemometrics
A.5 Matlab for Chemometrics
Index
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