Chemometrics: a practical guide
An outstanding practical guide to the most common chemometric methods in use today
Chemometrics explains how to apply the most widely used pattern recognition and multivariate calibration techniques to solve data analysis problems. This practical guide describes all key methods in terms of processes and applications in order to help the reader easily identify the best technique for a given situation.
Drawing on years of industrial experience with chemometric tools, the authors share their six basic steps, or "habits," for achieving reliable chemometric results, and cover key areas such as:
* Defining and understanding the problem
* Experimental planning and design
* Preprocessing of samples and variables
* Supervised and unsupervised pattern recognition
* Classical and inverse methods of multivariate calibration
Complete with helpful chapter-end summaries, technical references, and more, this book is an invaluable hands-on resource for analytical chemists and laboratory scientists who use chemometrics in their work.
29 pages matching spectral residuals in this book
Results 1-3 of 29
What people are saying - Write a review
We haven't found any reviews in the usual places.
Defining the Problem
1 other sections not shown
analysis analytes approach baseline calculated calibration data calibration samples caustic concentration chemical chemometric class A samples classification clusters column concentration error Cook's distance cross-validation data set DCLS dendrogram described determine discussed distance Equation examined Fc3lc Fcalc values Habit indicate inherent dimensionality linear loadings matrix Mean Square Error Measurement Residual Plot measurement variables measurement vector Model and Sample multivariate nearest neighbors noise nsamp number of factors number of samples number of variables outlier pattern recognition PCA residuals percent variance ples PLS model prediction samples preprocessing principal components Principal components analysis problem pure component pure spectra range regression RMSEP Root Mean Square row space Sample Diagnostic sample leverage Sample Number sample vector Scores Plot Section sensor shown in Figure SIMCA model spectral residuals spectrum statistical prediction errors studentized residual Table temperature tion unknown samples unusual samples validation samples Variable Diagnostic Variable Number Figure wavelength Wavenumber