Multivariate Analysis of Quality: An Introduction

Preface.

Acknowledgements.

OVERVIEW.

Why Multivariate Data Analysis?

Qualimetrics for Determining Quality.

A Layman's Guide to Multivariate Data Analysis.

METHODOLOGY.

Some Estimation Concepts.

Analysis of One Data Table X: Principle Component Analysis.

Analysis of Two Data Tables X and Y: Partial Least Squares Regression (PLSR).

Example of Multivariate Calibration Project.

Interpretation of Many Types of Data X and Y: Exploring Relationships in Interdisciplinary Data Sets.

Classification and Discrimination X_1, X_2, X_3: Handling Heterogeneous Sample Sets.

Validation X and Y.

Experimental Planning Y and X.

APPLICATIONS.

Multivariate Calibration: Quality Determination of Wheat From High-speed NIR Spectra.

Analysis of Questionnaire Data: What Determines Quality of the Working Environment?

Analysis of a Heterogeneous Sample Set: Predicting Toxicity From Quantum Chemistry.

Multivariate Statistical Process Control: Quality Monitoring of a Sugar Production Process.

Design and Analysis of Controlled Experiments: Reducing Loss of Quality in Stored Food.

Appendix A1: How the Present Book Relates to Some Mathematical Modelling Traditions in Science.

Appendix A2: Sensory Science.

Appendix A3.1: Bi-linear Modelling Has Many Applications.

Appendix A3.2: Common Problems and Pitfalls in Soft Modelling.

Appendix A4: Mathematical Details.

Appendix A5: PCA Details.

Appendix A6: PLS Regression Details.

Appendix A7: Modelling the Unknown.

Appendix A8: Non-linearity and Weighting.

Appendix A9: Classification and Outlier Detection.

Appendix A10: Cross-validation Details.

Appendix A11: Power Estimation Details.

Appendix A12: What Makes NIR Data So Information-rich?

Appendix A13: Consequences of the Working Environment Survey.

Appendix A14: Details of the Molecule Class Models.

Appendix A15: Forecasting the Future.

Appendix A16: Significance Testing with Cross-validation vs. ANOVA.

References.

Index.