Resolving spectral mixtures : with applications from ultrafast time-resolved spectroscopy to super-resolution imaging /

Detalles Bibliográficos
Clasificación:Libro Electrónico
Otros Autores: Ruckebusch, Cyril (Editor )
Formato: Electrónico eBook
Idioma:Inglés
Publicado: Amsterdam, Netherlands : Elsevier, 2016.
Colección:Data handling in science and technology ; v. 30.
Temas:
Spectral imaging.
Spectrum analysis.
Imagerie spectrale.
SCIENCE > Chemistry > Analytic.
Acceso en línea:Texto completo
Tabla de Contenidos:
  • 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.

Ejemplares similares