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Summary
Summary
Data analysis is a vital part of science today, and in assessing quality, multivariate analysis is often necessary in order to avoid loss of essential information. Martens provides a powerful and versatile methodology that enables researchers to design their investigations and analyse data effectively and safely, without the need for formal statistical training.
* Offers an introductory explanation of multivariate analysis by graphical 'soft modelling'
* Minimises mathematics, providing all technical details in the appendix
* Presents itself in an accessible style with cartoons, self-assessment questions and a wide range of practical examples
* Demonstrates the methodology for various types of quality assessment, ranging from human quality perception via industrial quality monitoring to environmental quality and its molecular basis
All data sets available FREE online on "Chemometrics World" (http://www.wiley.co.uk/wileychi/chemometrics)
Author Notes
Harald Martens, Professor of Chemometrics, Norwegian University of Science and Technology, and Technical University of Denmark. He is co-author of the best selling book, Multivariate Calibration, also published by John Wiley & Sons. He has won many awards for his work in data analysis including in 1999 the Galactic Industries Prize and the Herman World medal. His present developments have also been incorporated by CAMO Inc in their latest version of The Unscrambler.
Magni Martens, Professor of Sensory Science, Royal Veterinary & Agricultural University, Denmark. She has also won awards for her work in sensory science including in 2000 the prestigious Carlsberg Research Foundation proze. She has published numerous papers on multivariate data analysis for relating "soft" human quality perception to "hard" facts and measurements.
Table of Contents
| 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 |
