Multivariate Analysis of Quality: An IntroductionData 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) |
Contents
OVERVIEW Chapters 13 | 1 |
Qualimetrics for Determining Quality | 25 |
and the Humanities | 49 |
METHODOLOGY Chapters 411 | 77 |
Principal Component Analysis | 93 |
Partial Least Squares | 111 |
Example of a Multivariate Calibration Project | 127 |
Exploring | 139 |
Effect Correction | 374 |
Appendix A5 PCA Details | 377 |
Equivalent PCA Representations | 378 |
Scaling and Rotation of Bilinear Models | 379 |
Correlation Loadings for X | 380 |
Missing Values | 381 |
Appendix A6 PLS Regression Details | 382 |
ta | 383 |
Handling | 157 |
Validation X? Ŷ?? | 177 |
Experimental Planning Y? X | 207 |
Quality Determination of Wheat | 235 |
What Determines Quality | 257 |
Predicting Toxicity | 275 |
Quality Monitoring | 297 |
Reducing | 323 |
APPENDICES A1A16 | 355 |
Appendix A2 Sensory Science | 362 |
Appendix A4 Mathematical Details | 370 |
Some Useful Statistical Expressions | 371 |
Median Quartiles Percentiles | 373 |
Comments on the PLSR Algorithm | 384 |
PLSR Draws on Two Sources of Structure | 385 |
Disagreement Between X and Y | 386 |
Appendix A7 Modelling the Unknown | 388 |
Appendix A8 Nonlinearity and Weighting | 389 |
the Use of Prior Knowledge to Scale the Input Variables for BLM | 390 |
Graphical Consequences of the Weighting | 391 |
Appendix A9 Classification and Outlier Detection | 394 |
Appendix A11 Power Estimation Details | 410 |
Appendix A13 Consequences of the Working Environment Survey | 418 |
| 427 | |
| 435 | |
Common terms and phrases
abscissa analytical Aopt Appendix assessment bi-linear model BLM method calibration model calibration samples causal Chapter COCOA cocoa-milk cocoa-odour colour correlation loadings cross-validation data analysis data set data table descriptors design factors DPLSR estimated experiment experimental design Experimental planning Figure indicator variables input data input variables interpretation jack-knifing JOBSATISFACTION latent variables linear linear model loading plot Martens matrix measured MILK milk-taste mixture design model parameters molecules mouthfeel multivariate data analysis NO2A number of PCs ordinate outliers PLSR model prediction error predictive ability problem protein raw data regression coefficients relevant reliability range replicates represent residuals response variables RMSEP(Y root mean square sample set score plot segments sensory analysis shows SIMCA soft modelling spectra stabiliser standard deviation standard uncertainty standardised statistical SUGAR summarised t₁ tion toxicity Type I error types validation values variation vector VISCOSITY wavelength WPOC X-data X-variables x₁
Popular passages
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