Titlebook: Introduction to Multivariate Calibration; A Practical Approach Alejandro C. Olivieri Textbook 2024Latest edition The Editor(s) (if applicab

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978-3-031-64146-6The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Switzerl

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The Classical Least-Squares Model,The simplest first-order multivariate model, based on classical least squares, is discussed. Important concepts are introduced, which are common to other advanced models, such as the regression coefficients and the first-order advantage. The main limitations of the classical model are detailed.

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Principal Component Analysis,A brief introduction to principal component analysis is provided, with applications in the discovery of hidden patterns for data exploration, classification problems of one-class type, and to the development of inverse calibration models using full spectral information.

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ORBIT

Arbitrary

The Partial Least-Squares Model,The most popular first-order model based on partial least-squares is presented, and a range of applications are shown, from single and multiple analyte determinations to sample discrimination.

PLE

Models Considering the Noise Structure,The impact of the structure and properties of the instrumental noise on the multivariate models is discussed, introducing alternative calibration procedures which incorporate the noise structure into the models.

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Sample and Sensor Selection,Multivariate calibration models are usually implemented by first selecting appropriate calibration samples and working wavelengths. Different procedures are discussed for performing these important activities.