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|a Resolving spectral mixtures :
|b with applications from ultrafast time-resolved spectroscopy to super-resolution imaging /
|c edited by Cyril Ruckebusch.
|
| 264 |
|
1 |
|a Amsterdam, Netherlands :
|b Elsevier,
|c 2016.
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| 300 |
|
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|a 1 online resource.
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| 336 |
|
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|a text
|b txt
|2 rdacontent
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| 337 |
|
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|a computer
|b c
|2 rdamedia
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| 338 |
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|a online resource
|b cr
|2 rdacarrier
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| 490 |
1 |
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|a Data handling in science and technology ;
|v volume 30
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| 500 |
|
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|a Includes index.
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| 588 |
0 |
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|a Online resource; title from PDF title page (ScienceDirect, viewed August 24, 2016).
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| 505 |
0 |
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|a Front Cover; Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging; Copyright; Contents; Contributors; Preface; Foreword; Chapter 1: Introduction; 1. Introduction; 2. The Spectral Mixture Problem; 3. Book Content and Organization; Chapter 2: Multivariate Curve Resolution-Alternating Least Squares for Spectroscopic Data; 1. MCR: The Concept and the Link with Spectroscopic Data; 2. MCR-ALS: Algorithm and Data Set Configuration; 2.1. MCR-ALS Algorithm: Steps; 2.2. Constraints; 3. MCR-ALS Applied to Process Analysis.
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| 505 |
8 |
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|a 3.1. Encoding Process Information: Sequentiality and Physicochemical Models3.1.1. Sequentiality; 3.1.2. Physicochemical Models; 3.2. Multiset Analysis: Multiexperiment Analysis and Data Fusion; 3.2.1. Multiexperiment Analysis; 3.2.2. Multitechnique Analysis (Data Fusion); 4. MCR-ALS Applied to HSI Analysis; 4.1. Encoding Image Information: The Spatial Dimension; 4.2. Image Multiset Analysis; 4.3. MCR Postprocessing; 5. MCR-ALS and Quantitative Analysis; 5.1. Second-order Calibration; 5.2. First-order Calibration: Correlation Constraint; 6. MCR-ALS and Other Bilinear Decomposition Methods.
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| 505 |
8 |
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|a 1.1. Permutation Ambiguity1.2. Intensity or Scalar Ambiguity; 1.3. Rotation Ambiguities; 2. Evaluation of MCR Ambiguities; 3. Estimation of the Extension of Rotation Ambiguities and of Their MCR Feasible Solutions; 3.1. Optimization Problem and Method; 3.2. Objective Function to Minimize; 3.3. Variables to Optimize; 4. MCR Constraints and Their Implementation; 4.1. Normalization and/or Closure Constraints; 4.2. Nonnegativity Constraints; 4.3. Selectivity and Local Rank Constraints; 4.4. Unimodality; 4.5. Model or Multilinearity Constraints; 4.6. Hard Modeling.
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| 505 |
8 |
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|a 5. Implementation of the MCR-BANDS Method6. Example of Calculation of MCR Feasible Solutions Using the MCR-BANDS Method; 7. Comparison of Solutions Obtained by Different MCR Methods; 8. Comparison of the Ranges of MCR Feasible Solutions Obtained by Different Methods; 9. Conclusions; References; Chapter 5: On the Analysis and Computation of the Area of Feasible Solutions for Two-, Three-, and Four-Component Systems; 1. Introduction; 1.1. Organization of the Chapter; 1.2. Model Data Sets and Experimental Spectral Data; 2. MCR Methods; 2.1. The Singular Value Decomposition.
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| 504 |
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|a Includes bibliographical references and index.
|
| 650 |
|
0 |
|a Spectral imaging.
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| 650 |
|
0 |
|a Spectrum analysis.
|
| 650 |
|
6 |
|a Imagerie spectrale.
|0 (CaQQLa)000297131
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| 650 |
|
7 |
|a SCIENCE
|x Chemistry
|x Analytic.
|2 bisacsh
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| 650 |
|
7 |
|a Spectral imaging.
|2 fast
|0 (OCoLC)fst01910210
|
| 650 |
|
7 |
|a Spectrum analysis.
|2 fast
|0 (OCoLC)fst01129108
|
| 700 |
1 |
|
|a Ruckebusch, Cyril,
|e editor.
|
| 776 |
0 |
8 |
|i Print version :
|z 9780444636386
|
| 830 |
|
0 |
|a Data handling in science and technology ;
|v v. 30.
|
| 856 |
4 |
0 |
|u https://sciencedirect.uam.elogim.com/science/bookseries/09223487/30
|z Texto completo
|
