MATLAB for Chemometricians: Data Handling in Science and Technology, cartea 33
Autor Federico Marini, Alessandra Biancolillo, Jose Manuel Amigoen Limba Engleză Paperback – 30 noi 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
Seria Data Handling in Science and Technology
ISBN-10: 0323854281
Pagini: 492
Ilustrații: Approx. 260 illustrations (195 in full color)
Dimensiuni: 152 x 229 mm
Editura: ELSEVIER SCIENCE
Seria Data Handling in Science and Technology
Cuprins
Section
I
Basics1.
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 analysis5. 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 material10. 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 analysis5. 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 material10. 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