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

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樓主
發(fā)表于 2025-3-21 17:32:46 | 只看該作者 |倒序瀏覽 |閱讀模式
書目名稱Introduction to Multivariate Calibration
副標題A Practical Approach
編輯Alejandro C. Olivieri
視頻videohttp://file.papertrans.cn/474/473947/473947.mp4
概述Introduces difficult concepts in a qualitative way with minimal use of mathematics.Describes a freely available software for practical work.Includes both theoretical and practical exercises to illustr
圖書封面Titlebook: Introduction to Multivariate Calibration; A Practical Approach Alejandro C. Olivieri Textbook 20181st edition Springer Nature Switzerland A
描述This book offers an introductory-level guide to the complex field of multivariate analytical calibration, with particular emphasis on real applications such as near infrared spectroscopy. It presents intuitive descriptions of mathematical and statistical concepts, illustrated with a wealth of figures and diagrams, and consistently highlights physicochemical interpretation rather than mathematical issues. In addition, it describes an easy-to-use and freely available graphical interface, together with a variety of appropriate examples and exercises. Lastly, it discusses recent advances in the field (figures of merit, detection limit, non-linear calibration, method comparison), together with modern literature references..
出版日期Textbook 20181st edition
關(guān)鍵詞Multivariate calibration; Partial least-squares regression; Analytical figures of merit; Software for m
版次1
doihttps://doi.org/10.1007/978-3-319-97097-4
isbn_softcover978-3-030-07302-2
isbn_ebook978-3-319-97097-4
copyrightSpringer Nature Switzerland AG 2018
The information of publication is updating

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沙發(fā)
發(fā)表于 2025-3-21 21:34:06 | 只看該作者
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.
板凳
發(fā)表于 2025-3-22 04:11:51 | 只看該作者
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.
地板
發(fā)表于 2025-3-22 07:46:04 | 只看該作者
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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發(fā)表于 2025-3-22 19:40:38 | 只看該作者
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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發(fā)表于 2025-3-22 21:16:33 | 只看該作者
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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發(fā)表于 2025-3-23 08:37:58 | 只看該作者
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.
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