04365cam a2200517Ii 4500 ocn956953629 OCoLC 20190328114816.0 m o d cr cnu|||unuuu 160818s2016 ne ob 001 0 eng d N$T eng rda pn N$T OPELS EBLCP IDEBK YDX OCLCF N$T QCL OCLCQ VGM OCLCQ MERER OCLCQ U3W D6H WYU 956998955 958096780 958393272 1066656416 9780444636447 (electronic bk.) 0444636447 (electronic bk.) 9780444636386 (print) 0444636382 (print) (OCoLC)956953629 (OCoLC)956998955 (OCoLC)958096780 (OCoLC)958393272 (OCoLC)1066656416 QC454.O66 SCI 013010 bisacsh 543/.5 23 Resolving spectral mixtures : with applications from ultrafast time-resolved spectroscopy to super-resolution imaging / [electronic resource] edited by Cyril Ruckebusch. Amsterdam, Netherlands : Elsevier, 2016. 1 online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier Data handling in science and technology ; volume 30 Includes index. Online resource; title from PDF title page (ScienceDirect, viewed August 24, 2016). 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. 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. 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. 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. Includes bibliographical references and index. Spectral imaging. Spectrum analysis. SCIENCE Chemistry Analytic. bisacsh Spectral imaging. fast (OCoLC)fst01910210 Spectrum analysis. fast (OCoLC)fst01129108 Electronic books. Electronic book. Ruckebusch, Cyril, editor. Data handling in science and technology ; v. 30. ScienceDirect http://www.sciencedirect.com/science/bookseries/09223487/30 247400 247400