MATLAB for Chemometricians
Autor Federico Marini, Alessandra Biancolillo, Jose Manuel Amigoen Limba Engleză Paperback – dec 2022
Programming tricks are introduced when discussing specific problems in a concrete manner. Readers will not only be able to use existing chemometric toolboxes, but also to write and develop their own, even with the possibility of building graphical user interfaces.
- Provides continuous interexchange among illustration of the main chemometric concepts behind the functions and how to translate theory into fully working tools
- Includes programming tasks illustrated by different examples and placed in the framework of specific chemometric problems
- Provides tasks to be performed—along with a presentation and discussion of algorithms—with gradually increasing complexity
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Specificații
ISBN-13: 9780323854283
ISBN-10: 0323854281
Pagini: 492
Ilustrații: Approx. 260 illustrations (195 in full color)
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
ISBN-10: 0323854281
Pagini: 492
Ilustrații: Approx. 260 illustrations (195 in full color)
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Cuprins
Section I Basics 1. Introduction to the Matlab environment and data types 2. Simple algebraic operations 3. Basic Matrix Algebra 4. Univariate data analysis and first plotting commands
Section II Multivariate analysis 5. From univariate to multivariate linear regression: starting working extensively with matrices 6. Introduction to Experimental Design: Generating factorial designs and 3D plotting 7. PCA and Exploratory analysis: Building model structures and a basic graphical user interface 8. Bilinear calibration (PCR and PLS): More on the algorithmic side 9. Classification: Building decision rules and advanced 2D and 3D plotting
Section III Advanced material 10. Multivariate curve resolution: Introducing the ALS algorithm and simple constraints 11. Multi-way modelling: How to deal with multi-way arrays and how to process them 12. Signal processing: Derivatives, noise reduction, alignment 13. Image analysis: what you see and what you can get from it
Section II Multivariate analysis 5. From univariate to multivariate linear regression: starting working extensively with matrices 6. Introduction to Experimental Design: Generating factorial designs and 3D plotting 7. PCA and Exploratory analysis: Building model structures and a basic graphical user interface 8. Bilinear calibration (PCR and PLS): More on the algorithmic side 9. Classification: Building decision rules and advanced 2D and 3D plotting
Section III Advanced material 10. Multivariate curve resolution: Introducing the ALS algorithm and simple constraints 11. Multi-way modelling: How to deal with multi-way arrays and how to process them 12. Signal processing: Derivatives, noise reduction, alignment 13. Image analysis: what you see and what you can get from it