MATLAB for Chemometricians: Volume 33
Volume 33
Elsevier (Publisher)
Will be published approx. on 1. December 2029
Book
Paperback/Softback
492 pages
978-0-323-85428-3 (ISBN)
Description
MATLAB (R) 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.
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.
More details
Series
Language
English
Place of publication
Philadelphia
United States
Target group
Professional and scholarly
Product notice
Paperback (trade)
Unsewn / adhesive bound
Illustrations
Approx. 260 illustrations (195 in full color)
Dimensions
Height: 229 mm
Width: 152 mm
ISBN-13
978-0-323-85428-3 (9780323854283)
Copyright in bibliographic data is held by Nielsen Book Services Limited or its licensors: all rights reserved.
Schweitzer Classification
Persons
Dr. Federico Marini is currently associate professor of Chemometrics at Sapienza University of Rome. In 2006, he was awarded the Young Researcher Prize from the Italian Chemical Society and in 2012 he won the Chemometrics and Intelligent Laboratory Systems Award. His research activity focuses on chemometrics, ranging from the application of existing methods to real world problems to the design and development of novel algorithms. He is author of more than 150 papers in international journals and book chapters, and recently he edited and coauthored the book Chemometrics in Food Chemistry (Elsevier). He is member of the Editorial boards of several journals, and he serves as Associate Editor for Chemometrics in Wiley's Encyclopedia of Analytical Chemistry. He is the past-coordinator of the Chemometric group of the Italian Chemical Society and a member of the Chemometric study group of EUCheMS. Dr. Alessandra Biancolillo attained her PhD Degree in Spectroscopy and Chemometrics by the University of Copenhagen (KU) working on a joint project between the Norwegian Institute for Food and Fishery (NOFIMA) and KU. After this, she has been a post-doc researcher at the University of Rome "La Sapienza?, at the Catholic University of the Sacred Heart School of Medicine (Rome, Italy) and an ingenieurs de recherche (post-doc researcher) at the IRSTEA institute in Montpellier (France). Since 2019, she is researcher at the University of L'Aquila (Italy). Her main research topics are multi-block analysis, classification method development and feature selection. Jose Manuel Amigo is a Research Professor at IKERBASQUE, the Basque Foundation for Sciences in Bilbao and a Distinguished Professor at the Department of Analytical Chemistry, University of Basque Country, Spain. He obtained his PhD (Cum Laude) in Chemistry from the Autonomous University of Barcelona, Spain. He was employed as a post-doctoral student (2007 - 2009) and an Associate Professor (2010 - 2019) at the Department of Food Science of the University of Copenhagen, Denmark. In 2017, he was at the same time a guest Professor at the Federal University of Pernambuco, Brazil. Current research interests include hyperspectral and digital image analysis and the application of Chemometrics (i.e. Machine and Deep Learning). He has authored over 180 publications (150+ peer-reviewed papers, books, book chapters, proceedings, etc.) and has given more than 60 conferences and courses at international meetings. Jose has supervised or is currently supervising several MSc, PhD and Post Docs, and he is an editorial board member of four scientific journals within chemometrics. Moreover, he received the "2014 Chemometrics and Intelligent Laboratory Systems Award? for his achievements in the field of Chemometrics and the "2019 Tomas Hirschfeld Award? for his achievements in the field of Near Infrared.
Author
Associate Professor of Chemometrics, Sapienza University, Rome, Italy
Researcher, University of L'Aquila, Spain
Research Professor, IKERBASQUE, Basque Foundation for Sciences. Bilbao and Distinguished Professor at the Department of Analytical Chemistry, University of the Basque Country, Spain
Content
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
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