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MATLAB® for Chemometricians, Volume 33 provides a complete introduction to the topic of MATLAB programming. Written by, and for, chemometricians, the book presents a very practical… Read more
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MATLAB® for Chemometricians, Volume 33 provides a complete introduction to the topic of MATLAB programming. Written by, and for, chemometricians, the book presents a very practical and task-oriented introduction on how to use MATLAB. Programming tips are contextualized within specific chemometric objectives, and each practical section is accompanied by theoretical background that describes the basic chemometrics concepts behind the algorithms for deeper understanding. The book starts from scratch (i.e., descriptions of the basic MATLAB layout and how to perform the most elementary algebraic operations) and leads readers through increasingly demanding tasks.
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.
Chemometrics researchers and those in analytical chemistry, spectroscopy, food chemistry metabolomics, and other related industries seeking to know more about the MATLAB environment and how to use it to perform chemometric calculations; MSc and PhD students approaching both MATLAB and chemometrics for the first time. Courses in applied analytical chemistry, spectroscopy and data mining
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
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