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Olivieri A.C. Introduction to Multivariate Calibration: A Practical Approach

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Olivieri A.C. Introduction to Multivariate Calibration: A Practical Approach
Springer, 2024. — 309 p.
This book contains several new sections that provide even more in-depth knowledge on the topics. New content on the classical least-squares model, which shows its advantages and limitations in greater detail, was added. Additionally, the book contains a new section on the inverse least-squares model, which explains how it differs from the classical model and its applications in chemometrics. Furthermore, a new chapter on principal component analysis, which covers the concept in greater detail and its applications in chemometrics, is added. This book also includes several real-world examples to help you better understand the topic. Overall, this book provides the reader with even more comprehensive knowledge on chemometrics and multivariate calibration, making it an essential resource for students and professionals alike.
Foreword
Preface
About This Book
Chemometrics and Multivariate Calibration
The Classical Least-Squares Model
The Inverse Least-Squares Model
Principal Component Analysis
Principal Component Regression
The Optimum Number of Latent Variables
The Partial Least-Squares Model
Models Considering the Noise Structure
Sample and Sensor Selection
Mathematical Pre-processing
Analytical Figures of Merit
MVC1: Software for Multivariate Calibration
Non-linearity and Artificial Neural Networks. Radial Basis Functions and Kernel Partial Least-Squares
Non-linearity and Artificial Neural Networks. Multi-layer Perceptron
Solutions to Exercises
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