Titlebook: Introduction to Multivariate Calibration; A Practical Approach Alejandro C. Olivieri Textbook 20181st edition Springer Nature Switzerland A

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书目名称Introduction to Multivariate Calibration影响因子(影响力)




书目名称Introduction to Multivariate Calibration影响因子(影响力)学科排名




书目名称Introduction to Multivariate Calibration网络公开度




书目名称Introduction to Multivariate Calibration网络公开度学科排名




书目名称Introduction to Multivariate Calibration被引频次




书目名称Introduction to Multivariate Calibration被引频次学科排名




书目名称Introduction to Multivariate Calibration年度引用




书目名称Introduction to Multivariate Calibration年度引用学科排名




书目名称Introduction to Multivariate Calibration读者反馈




书目名称Introduction to Multivariate Calibration读者反馈学科排名

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Chemometrics and Multivariate Calibration,The relationship between univariate, multivariate, and multi-way calibrations is discussed, with emphasis in the analytical advantages which can be achieved in going from simple to more complex data structures.

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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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The Inverse Least-Squares Model,The first and simplest inverse least-squares calibration model, also called multiple linear regression, is discussed in detail. Advantages and disadvantages are discussed for a model which today is still in use for some applications. Proposals are given for developing advanced calibration models.

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Principal Component Regression,A modern multivariate model incorporating all required characteristics is discussed, based on the combination of principal component analysis and inverse least-squares regression.

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The Optimum Number of Latent Variables,The relevant issue of optimizing the number of latent variables in full-spectral inverse models is discussed, with emphasis on interpretation rather on statistical and mathematical issues.

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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.

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Mathematical Pre-processing,Multivariate calibration models sometimes require one to pre-process the instrumental data with mathematical techniques. Criteria are discussed for performing this relevant activity. The objective is to reduce the impact of physical phenomena or changes in the instrumental response over time.