Multivariate Analysis of Quality: An Introduction
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)
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OVERVIEW Chapters 13
Qualimetrics for Determining Quality
and the Humanities
METHODOLOGY Chapters 411
Principal Component Analysis
Partial Least Squares
Example of a Multivariate Calibration Project
Response Surface Analysis
Appendix A2 Sensory Science
Appendix A4 Mathematical Details
Appendix A5 PCA Details
Appendix A7 Modelling the Unknown
Validation X? Y??
l1 Experimental Planning Y? X
Quality Determination of Wheat
What Determines Quality
abscissa analytical Appendix assessment bi-linear model BLM method calibration model calibration samples causal Chapter l0 COCOA colour correlation loadings cross-validation data set data table defined descriptors design factors DPLSR estimated example experiment experimental design Experimental planning Figure full model indicator variables input data input variables interpretation jack-knifing latent variables linear linear model loading plot Martens matrix mean square error measured mixture design model parameters molecules multivariate data analysis number of PCs optimal number ordinate outliers PLSR model prediction error predictive ability problem protein raw data red litmus regression coefficients relevant reliability range replicates represent residuals response variables RMSEP(Y root mean square sample set score plot sensory analysis shows SIMCA soft modelling spectra stabiliser standard deviation standard uncertainty standardised statistical statistical power sub-models SUGAR summarised tion toxicity Type I error types validation values variation vector wavelength WPOc X-data X-variables