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

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書目名稱Introduction to Multivariate Calibration影響因子(影響力)




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書目名稱Introduction to Multivariate Calibration被引頻次




書目名稱Introduction to Multivariate Calibration被引頻次學(xué)科排名




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書目名稱Introduction to Multivariate Calibration讀者反饋




書目名稱Introduction to Multivariate Calibration讀者反饋學(xué)科排名

Criteria

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.

高貴領(lǐng)導(dǎo)

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.

intrude

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.

違反

headlong

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.

未成熟

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.

反饋

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.

值得

FAZE

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.