ABE-IPSABE HOLDINGABE BOOKS
English Polski
Dostęp on-line

Książki

0.00 PLN
Schowek (0) 
Schowek jest pusty
Chemometrics: Data Driven Extraction for Science

Chemometrics: Data Driven Extraction for Science

Autorzy
Wydawnictwo John Wiley & Sons Inc
Data wydania 30/03/2018
Liczba stron 464
Forma publikacji książka w miękkiej oprawie
Poziom zaawansowania Dla profesjonalistów, specjalistów i badaczy naukowych
Język angielski
ISBN 9781118904664
Kategorie Prawdopodobieństwo i statystyka
494.55 PLN (z VAT)
$111.25 / €106.03 / £92.05 /
Produkt na zamówienie
Dostawa 5-6 tygodni
Ilość
Do schowka

Opis książki

A new, full-color, completely updated edition of the key practical guide to chemometrics


This new edition of this practical guide on chemometrics, emphasizes the principles and applications behind the main ideas in the field using numerical and graphical examples, which can then be applied to a wide variety of problems in chemistry, biology, chemical engineering, and allied disciplines. Presented in full color, it features expansion of the principal component analysis, classification, multivariate evolutionary signal and statistical distributions sections, and new case studies in metabolomics, as well as extensive updates throughout. Aimed at the large number of users of chemometrics, it includes extensive worked problems and chapters explaining how to analyze datasets, in addition to updated descriptions of how to apply Excel and Matlab for chemometrics.


Chemometrics: Data Driven Extraction for Science, Second Edition offers chapters covering: experimental design, signal processing, pattern recognition, calibration, and evolutionary data. The pattern recognition chapter from the first edition is divided into two separate ones: Principal Component Analysis/Cluster Analysis, and Classification. It also includes new descriptions of Alternating Least Squares (ALS) and Iterative Target Transformation Factor Analysis (ITTFA). Updated descriptions of wavelets and Bayesian methods are included.





Includes updated chapters of the classic chemometric methods (e.g. experimental design, signal processing, etc.)

Introduces metabolomics-type examples alongside those from analytical chemistry

Features problems at the end of each chapter to illustrate the broad applicability of the methods in different fields

Supplemented with data sets and solutions to the problems on a dedicated website



Chemometrics: Data Driven Extraction for Science, Second Edition is recommended for post-graduate students of chemometrics as well as applied scientists (e.g. chemists, biochemists, engineers, statisticians) working in all areas of data analysis. "...fills a gap in the chemometrics literature landscape. With its unique approach of learning-by-doing it is best suited for practitioners, which do not want to dig too deep into the theory and are not interested in a full coverage of methods. Nevertheless, the most important and usual applied chemometrics methods are introduced...The example data sets of the book are also worth exploring by itself, because they are well chosen and nicely structured."
Thomas Bocklitz, Analytical and Bioanalytical Chemistry (2019)

fills a gap in the
chemometrics literature landscape. With its unique approach
of learning-by-doing it is best suited for practitioners, which
do not want to dig too deep into the theory and are not interested
in a full coverage of methods. Nevertheless, the most
important and usual applied chemometrics methods are introduced
by Brereton (2018). The example data sets of the book
are also worth exploring by itself, because they are well chosen
and nicely structured.

Chemometrics: Data Driven Extraction for Science

Spis treści

Contents





1 Introduction

1.1 Historical Parentage





1.2 Developments since the 1970s





1.3 Software and Calculations





1.4 Further Reading





References





2 Experimental Design





2.1 Introduction





2.2 Basic Principles





2.3 Factorial Designs





2.4 Central Composite or Response Surface Designs





2.5 Mixture Designs





2.6 Simplex Optimisation





Problems





3 Signal Processing





3.1 Introduction





3.2 Basics





3.3 Linear Filters





3.4 Correlograms and Time Series Analysis





3.5 Fourier Transform Techniques





3.6 Additional Methods





Problems





4 Principal Component Analysis and Unsupervised Pattern Recognition





4.1 Introduction





4.2 The Concept and Need for Principal Components Analysis





4.3 Principal Components Analysis: The Method





4.4 Factor Analysis





4.5 Graphical Representation of Scores and Loadings





4.6 Pre-processing





4.7 Comparing Multivariate Patterns





4.8 Unsupervised Pattern Recognition: Cluster Analysis





4.9 Multi-way Pattern Recognition





Problems





5 Classification and Supervised Pattern Recognition





5.1 Introduction





5.2 Two-Class Classifiers





5.3 One-Class Classifiers





5.4 Multi-Class Classifiers





5.5 Optimisation and Validation





5.6 Significant Variables





Problems





6 Calibration





6.1 Introduction





6.2 Univariate Calibration





6.3 Multiple Linear Regression





6.4 Principal Components Regression





6.5 Partial Least Squares Regression





6.6 Model Validation and Optimisation





Problems





7 Evolutionary Multivariate Signals





7.1 Introduction





7.2 Exploratory Data Analysis and Pre-processing





7.3 Determining Composition





7.4 Resolution





Problems





A Appendix





A.1 Vectors and Matrices





A.2 Algorithms





A.3 Basic Statistical Concepts





A.4 Excel for Chemometrics





A.5 Matlab for Chemometrics

Polecamy również książki

Strony www Białystok Warszawa
801 777 223