Multivariate Analysis of Quality An Introduction

Name: Multivariate Analysis of Quality An Introduction
Price: 207.56 USD
Availability: InStock
ISBN: 9780471974284

ISBN-10: 0471974285

ISBN-13: 9780471974284

Edition: 2000

Authors: Harald Martens, M. Martens

List price: $387.95

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Description:

Providing an introductory explanation of multivariate analysis, this volume describes the application for the assessment of quality and includes many examples from food science to illustrate the theory.

Book details

List price: $387.95
Copyright year: 2000
Publisher: John Wiley & Sons, Incorporated
Publication date: 2/8/2001
Binding: Hardcover
Pages: 468
Size: 6.26" wide x 9.80" long x 1.26" tall
Weight: 1.738
Language: English



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



How the Present Book Relates to Some Mathematical Modelling Traditions in Science



Sensory Science



Bi-linear Modelling Has Many Applications



Common Problems and Pitfalls in Soft Modelling



Mathematical Details



PCA Details



PLS Regression Details



Modelling the Unknown



Non-linearity and Weighting



Classification and Outlier Detection



Cross-validation Details



Power Estimation Details



What Makes NIR Data So Information-rich?



Consequences of the Working Environment Survey



Details of the Molecule Class Models



Forecasting the Future



Significance Testing with Cross-validation vs ANOVA


References


Index