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Journal ArticleDOI

A review of near infrared spectroscopy and chemometrics in pharmaceutical technologies.

27 Jul 2007-Journal of Pharmaceutical and Biomedical Analysis (Elsevier)-Vol. 44, Iss: 3, pp 683-700
TL;DR: This review focuses on chemometric techniques and pharmaceutical NIRS applications, covering qualitative analyses, quantitative methods and on-line applications for near-infrared spectroscopy for pharmaceutical forms.
About: This article is published in Journal of Pharmaceutical and Biomedical Analysis.The article was published on 2007-07-27. It has received 1041 citations till now.
Citations
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Journal ArticleDOI
TL;DR: This review focuses on the variable selection methods in NIR spectroscopy with some classical approaches and sophisticated methods such as successive projections algorithm (SPA), uninformative variable elimination (UVE) and elaborate search-based strategies.

860 citations

Journal ArticleDOI
TL;DR: In this article, the progress and applications of infrared techniques in biomass study, and compares the infrared and the wet chemical methods for composition analysis, are summarized and compared, in addition to reviewing recent studies of biomass structure and composition.

645 citations

Journal ArticleDOI
TL;DR: An overview of latent variable methods used in pharmaceutics and integrated with advanced characterization techniques such as vibrational spectroscopy is provided.

453 citations


Cites methods from "A review of near infrared spectrosc..."

  • ...50 IR imaging Qualitative analysis of solid forms PCA Roggo et al. (2005)...

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  • ...38 Raman Identification of tablets SVMd, PLS Roggo et al. (2010)...

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Journal ArticleDOI
TL;DR: In this paper the main classes of antioxidants are presented: vitamins, carotenoids and polyphenols.

401 citations

References
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Book
16 Jul 1998
TL;DR: Thorough, well-organized, and completely up to date, this book examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks.
Abstract: From the Publisher: This book represents the most comprehensive treatment available of neural networks from an engineering perspective. Thorough, well-organized, and completely up to date, it examines all the important aspects of this emerging technology, including the learning process, back-propagation learning, radial-basis function networks, self-organizing systems, modular networks, temporal processing and neurodynamics, and VLSI implementation of neural networks. Written in a concise and fluid manner, by a foremost engineering textbook author, to make the material more accessible, this book is ideal for professional engineers and graduate students entering this exciting field. Computer experiments, problems, worked examples, a bibliography, photographs, and illustrations reinforce key concepts.

29,130 citations

Book
01 Jan 1983
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Abstract: The methodology used to construct tree structured rules is the focus of this monograph. Unlike many other statistical procedures, which moved from pencil and paper to calculators, this text's use of trees was unthinkable before computers. Both the practical and theoretical sides have been developed in the authors' study of tree methods. Classification and Regression Trees reflects these two sides, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.

14,825 citations

Journal ArticleDOI
TL;DR: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points, so it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.
Abstract: The nearest neighbor decision rule assigns to an unclassified sample point the classification of the nearest of a set of previously classified points. This rule is independent of the underlying joint distribution on the sample points and their classifications, and hence the probability of error R of such a rule must be at least as great as the Bayes probability of error R^{\ast} --the minimum probability of error over all decision rules taking underlying probability structure into account. However, in a large sample analysis, we will show in the M -category case that R^{\ast} \leq R \leq R^{\ast}(2 --MR^{\ast}/(M-1)) , where these bounds are the tightest possible, for all suitably smooth underlying distributions. Thus for any number of categories, the probability of error of the nearest neighbor rule is bounded above by twice the Bayes probability of error. In this sense, it may be said that half the classification information in an infinite sample set is contained in the nearest neighbor.

12,243 citations