1. Chemometrics and Multivariate Calibration -- 2. The Classical Least-Squares Model -- 3. The Inverse Least-Squares Model -- 4. Principal Component Analysis -- 5. Principal Component Regression.

This second edition builds upon the success of its predecessor with expanded and updated content throughout most chapters, accompanied by the addition of a new chapter on Non-linearities in Multivariate Calibration. This latest chapter explores advanced topics including Radial Basis Functions (RBF) and Feed-forward Networks (FFN), providing practical insights and real-world applications within the context of multivariate calibration. Readers will find intuitive explanations of mathematical and statistical concepts, supported by a wealth of figures and diagrams, alongside discussions on recent advancements in the field such as figures of merit, detection limits, and method comparison. With its emphasis on real applications and comprehensive updates, the second edition remains an invaluable resource for students, researchers, and practitioners navigating the complexities of multivariate analytical calibration.