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E-raamat: Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging

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Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution ImagingSuurem pilt
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Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Superresolution Imaging offers a comprehensive look into the most important models and frameworks essential to resolving the spectral unmixing problem-from multivariate curve resolution and multi-way analysis to Bayesian positive source separation and nonlinear unmixing. Unravelling total spectral data into the contributions from individual unknown components with limited prior information is a complex problem that has attracted continuous interest for almost four decades. Spectral unmixing is a topic of interest in statistics, chemometrics, signal processing, and image analysis. For decades, researchers from these fields were often unaware of the work in other disciplines due to their different scientific and technical backgrounds and interest in different objects or samples. This led to the development of quite different approaches to solving the same problem. This multi-authored book will bridge the gap between disciplines with contributions from a number of well-known and strongly active chemometric and signal processing research groups. Among chemists, multivariate curve resolution methods are preferred to extract information about the nature, amount, and location in time (process) and space (imaging and microscopy) of chemical constituents in complex samples. In signal processing, assumptions are usually around statistical independence of the extracted components. However, the chapters include the complexity of the spectral data to be unmixed as well as dimensionality and size of the data sets. Advanced spectroscopy is the key thread linking the different chapters. Applications cover a large part of the electromagnetic spectrum. Time-resolution ranges from femtosecond to second in process spectroscopy and spatial resolution covers the submicronic to macroscopic scale in hyperspectral imaging.Demonstrates how and why data analysis, signal processing, and chemometrics are essential to the spectral unmixing problemGuides the reader through the fundamentals and details of the different methodsPresents extensive plots, graphical representations, and illustrations to help readers understand the features of different techniques and to interpret resultsBridges the gap between disciplines with contributions from a number of well-known and highly active chemometric and signal processing research groups

Muu info

This comprehensive book presents an interdisciplinary approach to demonstrate how and why data analysis, signal processing, and chemometrics are essential to resolving the spectral unmixing problem
Contributors xv
Preface xix
Foreword xxi
1 Introduction
1(4)
C. Ruckebusch
1 Introduction
1(1)
2 The Spectral Mixture Problem
1(2)
3 Book Content and Organization
3(2)
2 Multivariate Curve Resolution-Alternating Least Squares for Spectroscopic Data
5(48)
A. de Juan
R. Tauler
1 MCR: The Concept and the Link with Spectroscopic Data
5(2)
2 MCR-ALS: Algorithm and Data Set Configuration
7(10)
2.1 MCR-ALS Algorithm: Steps
11(1)
2.2 Constraints
12(5)
3 MCR-ALS Applied to Process Analysis
17(11)
3.1 Encoding Process Information: Sequentiality and Physicochemical Models
17(4)
3.2 Multiset Analysis: Multiexperiment Analysis and Data Fusion
21(7)
4 MCR-ALS Applied to HSI Analysis
28(9)
4.1 Encoding Image Information: The Spatial Dimension
28(3)
4.2 Image Multiset Analysis
31(4)
4.3 MCR Postprocessing
35(2)
5 MCR-ALS and Quantitative Analysis
37(5)
5.1 Second-order Calibration
37(3)
5.2 First-order Calibration: Correlation Constraint
40(2)
6 MCR-ALS and Other Bilinear Decomposition Methods
42(2)
References
44(9)
3 Spectral Unmixing Using the Concept of Pure Variables
53(48)
S. Kucheryavskiy
W. Windig
A. Bogomolov
1 Introduction
53(1)
2 Case Studies
54(4)
3 Spectral Unmixing with Pure Variables
58(10)
3.1 Nonnegativity Constraint
67(1)
4 Chasing the Pure Variables
68(9)
4.1 Identifying the First Pure Variable
68(7)
4.2 Identifying Second and Further Pure Variables
75(2)
5 Investigation of Purity Characteristics
77(7)
6 Other Ways to Find the Pure Variables
84(7)
7 Pure Variables and MCR-ALS
91(3)
8 Discussion and Conclusions
94(4)
References
98(3)
4 Ambiguities in Multivariate Curve Resolution
101(34)
A. Malik
R. Tauter
1 Multivariate Curve Resolution and Ambiguities
101(4)
1.1 Permutation Ambiguity
102(1)
1.2 Intensity or Scalar Ambiguity
102(2)
1.3 Rotation Ambiguities
104(1)
2 Evaluation of MCR Ambiguities
105(4)
3 Estimation of the Extension of Rotation Ambiguities and of Their MCR Feasible Solutions
109(3)
3.1 Optimization Problem and Method
110(1)
3.2 Objective Function to Minimize
110(1)
3.3 Variables to Optimize
111(1)
4 MCR Constraints and Their Implementation
112(6)
4.1 Normalization and/or Closure Constraints
112(2)
4.2 Nonnegativity Constraints
114(1)
4.3 Selectivity and Local Rank Constraints
115(1)
4.4 Unimodality
116(1)
4.5 Model or Multilinearity Constraints
117(1)
4.6 Hard Modeling
117(1)
5 Implementation of the MCR-BANDS Method
118(1)
6 Example of Calculation of MCR Feasible Solutions Using the MCR-BANDS Method
118(4)
7 Comparison of Solutions Obtained by Different MCR Methods
122(3)
8 Comparison of the Ranges of MCR Feasible Solutions Obtained by Different Methods
125(5)
9 Conclusions
130(1)
References
130(5)
5 On the Analysis and Computation of the Area of Feasible Solutions for Two-, Three-, and Four-Component Systems
135(50)
M. Sawall
A. Jurß
H. Schroder
K. Neymeyr
1 Introduction
136(4)
1.1 Organization of the
Chapter
137(1)
1.2 Model Data Sets and Experimental Spectral Data
137(3)
2 MCR Methods
140(2)
2.1 The Singular Value Decomposition
140(1)
2.2 Reconstruction of the Pure Component Factors
141(1)
2.3 Application of Hard and Soft Constraints
141(1)
3 The Area of Feasible Solutions
142(9)
3.1 Development of the AFS Concept and Discussion of Methods for Its Numerical Computation
143(1)
3.2 The Set of Feasible Pure Component Spectra
144(1)
3.3 Reduction of the Degrees of Freedom
145(1)
3.4 Definition of the AFS
145(1)
3.5 Properties of the AFS
146(3)
3.6 Segment Structure of the AFS
149(2)
4 The AFS for Two-Component Systems
151(1)
4.1 Numerical AFS Computation for the Data Set 1
151(1)
5 Feasibility of Points in the AFS
152(3)
5.1 Soft Constraint-Based Feasibility Check
152(2)
5.2 The ssq-Function-Based Feasibility Check
154(1)
5.3 Geometric Constructive Feasibility Test
155(1)
6 AFS Computations for Three-Component Systems
155(8)
6.1 Borgen Plots and Computational Geometry
155(1)
6.2 Grid Search
156(1)
6.3 Triangle Enclosure
156(1)
6.4 MCR-Bands
157(1)
6.5 Polygon Inflation
157(6)
7 AFS Computations for Four-Component Systems
163(3)
7.1 The Slicing Method
164(1)
7.2 Polyhedron Inflation Method
164(2)
8 Reduction of the Rotational Ambiguity by Soft Constraints Represented in the AFS
166(3)
8.1 Equality Constraints: Locked Points in the AFS
166(1)
8.2 Further Soft Constraints
167(2)
9 Geometric Shapes of the AFS and AFS Dynamics
169(6)
9.1 Shapes of AFS Segments and Degenerated Segments
169(2)
9.2 AFS Dynamics
171(4)
10 The FACPACK Toolbox for AFS Computations
175(4)
10.1 Data Import
175(1)
10.2 The AFS Computation Module
175(2)
10.3 The Complementarity & AFS Module
177(1)
10.4 The Generalized Borgen Plot Module
177(2)
11 Outlook and Open Problems
179(2)
References
181(4)
6 Linear and Nonlinear Unmixing in Hyperspectral Imaging
185(40)
N. Dobigeon
Y. Altmann
N. Brun
S. Moussaoui
1 Spectral Mixture Analysis
185(5)
2 Linear Unmixing
190(5)
2.1 The Linear Mixing Model (LMM)
190(2)
2.2 Endmember Extraction Algorithms
192(1)
2.3 Inversion Algorithms
192(2)
2.4 Unsupervised SU: Joint Extraction and Inversion
194(1)
3 Nonlinear Unmixing
195(12)
3.1 Intimate Mixture Models
197(2)
3.2 Bilinear Mixing Models
199(3)
3.3 Polynomial Mixing Models
202(2)
3.4 Robust Linear Models
204(1)
3.5 Nonparametric SU Techniques
205(2)
4 Experiments: Unmixing EELS Data
207(5)
5 Conclusion
212(1)
Acknowledgments
213(1)
References
214(11)
7 Independent Components Analysis: Theory and Applications
225(54)
D. Jouan-Rimbaud Bouveresse
D.N. Rutledge
1 Introduction
225(8)
1.1 The History of Independent Components Analysis
225(2)
1.2 The Cocktail Party Problem
227(1)
1.3 Principal Components Analysis vs Independent Components Analysis
227(6)
2 Theory
233(32)
2.1 The Principles of ICA
233(2)
2.2 Data Preprocessing
235(2)
2.3 The Different Algorithms
237(5)
2.4 Application of JADE-ICA on Simulated Data
242(1)
2.5 Determination of the Number of Components to Extract by ICA
243(11)
2.6 Regression on Independent Components Proportions (Scores)
254(2)
2.7 Independent Components-Discriminant Analysis
256(5)
2.8 Comparison with Multiway Methods
261(1)
2.9 Comparison with MCR
262(1)
2.10 An Unexpected Property of ICA
263(2)
3 Some Applications of ICA
265(8)
3.1 One-Dimensional Data
266(2)
3.2 Two-Dimensional Data
268(1)
3.3 Three-Dimensional (and More) Data
268(5)
4 Conclusion
273(1)
References
273(6)
8 Bayesian Positive Source Separation for Spectral Mixture Analysis
279(32)
D. Brie
S. Moussaoui
S. Miron
C. Carteret
M. Dossot
1 Introduction
280(1)
2 Geometrical Interpretation of the NMF Model
281(4)
2.1 Preliminaries
281(1)
2.2 Chen's Necessary and Sufficient Uniqueness Condition
282(1)
2.3 A Necessary Uniqueness Conditions
282(1)
2.4 Convex Cone and Uniqueness
282(2)
2.5 Reducing the Set of Admissible Solutions
284(1)
3 The Bayesian Positive Source Separation Algorithm
285(9)
3.1 A Bayesian Approach to Spectral Mixture Analysis
286(1)
3.2 Noise Distribution and Likelihood
287(1)
3.3 Prior Distributions of Pure Spectra and Mixing Coefficients
287(1)
3.4 Posterior Density and Resulting Criterion
288(2)
3.5 Bayesian Formulation of the PMF Criterion
290(2)
3.6 Inference of the Hyperparameters
292(1)
3.7 Estimation via Markov Chain Monte Carlo Methods
293(1)
4 Applications
294(13)
4.1 Near Infrared Spectra of a Mixture of Alcanes
294(3)
4.2 Raman Analysis of a Kerolite Sample
297(2)
4.3 Polarized Raman Spectroscopy
299(8)
5 Conclusion
307(1)
References
307(4)
9 Multivariate Curve Resolution of Wavelet Compressed Data
311(22)
P. de B. Harrington
1 Introduction
311(3)
2 Theory
314(7)
3 Experimental Details
321(2)
4 Discussion of Results
323(7)
5 Conclusion
330(1)
Acknowledgments
331(1)
References
331(2)
10 Chemometric Resolution of Complex Higher Order Chromatographic Data with Spectral Detection
333(20)
D.K. Pinkerton
K.M. Pierce
R.E. Synovec
1 Introduction
333(3)
2 Instrument Design and Utilization to Produce High-Order Chemical Separations Data
336(6)
2.1 Goals for Two-Way Data: GC--MS and LC--DAD
336(2)
2.2 Goals for Three-Way Data: GC x GC--TOFMS and LC x LC--DAD
338(4)
3 Chemometric Resolution Methods
342(5)
3.1 MCR-ALS
343(1)
3.2 GRAM
344(1)
3.3 Parafac
345(2)
4 Applications and Conclusions
347(2)
References
349(4)
11 Multivariate Curve Resolution of (Ultra)Fast Photoinduced Process Spectroscopy Data
353(28)
O. Devos
S. Aloise
M. Sliwa
R. Metivier
J.-P. Placial
C. Ruckebusch
1 Introduction
353(2)
2 Photoinduced Processes
355(1)
2.1 Photophysical Processes
355(1)
2.2 Photochemical Processes
356(1)
3 Time-Resolved Spectroscopy
356(4)
3.1 Time-Resolved Absorption Spectroscopy
356(1)
3.2 Specificity of Ultrafast TRS
357(3)
4 MCR of TRS Data
360(4)
4.1 Preprocessing Time-Resolved Spectra
361(1)
4.2 MCR Basics
362(1)
4.3 Hard-Soft Multivariate Curve Resolution
363(1)
5 Applications
364(12)
5.1 Resolving Controversy about BP Photophysics
364(3)
5.2 A Photochromic Study of CMTE
367(5)
5.3 Clarification of the Photochromism of Anils by HS-MCR
372(4)
6 Concluding Remarks
376(1)
References
376(5)
12 Experimental and Data Analytical Approaches to Automating Multivariate Curve Resolution in the Analysis of Hyperspectral Images
381(28)
D.M. Haaland
H.D.T. Jones
J.A. Timlin
1 Introduction
381(1)
2 Theory of MCR Analyses
382(2)
3 Approaches to Improve the Probability of Successful MCR Solutions
384(7)
3.1 Experimental Approaches for Successful MCR Analysis
384(1)
3.2 Automated Preprocessing strategies for Improving MCR Analysis
385(6)
4 Comparison of Standard and Automated Preprocessing Approaches to MCR Analyses of Realistically Simulated and Real Hyperspectral Confocal Fluorescence Images
391(14)
4.1 Image Acquisition Details
391(1)
4.2 Realistically Simulated Hyperspectral Images of Macrophage Cells
391(6)
4.3 Hyperspectral Confocal Fluorescence Images of Immune Cells Labeled with Quantum Dots
397(8)
5 Conclusions
405(1)
Acknowledgments
406(1)
References
406(3)
13 Multiresolution Analysis and Chemometrics for Pattern Enhancement and Resolution in Spectral Signals and Images
409(44)
M. Li Vigni
M. Cocchi
1 Introduction
409(2)
2 Methods
411(16)
2.1 Wavelet Transform
411(4)
2.2 Feature Selection in Wavelet Domain
415(9)
2.3 WT in the Context Multivariate Image Analysis
424(3)
3 Applications
427(18)
3.1 WILMA+GA PLS for the Calibration of Flour Leavening Compounds
427(5)
3.2 Classification of Animal Feed by WPTER
432(4)
3.3 Wavelet-Based Multivariate Image Analysis for the Evaluation of Bread Surface Defectiveness
436(6)
3.4 Wavelet-Based Multivariate Image Analysis of TEM Images for the Characterization of Innovative Nanomaterials
442(3)
4 Remarks
445(2)
Acknowledgments
447(1)
References
447(6)
14 A Smoothness Constraint in Multivariate Curve Resolution-Alternating Least Squares of Spectroscopy Data
453(24)
S. Hugelier
O. Devos
C. Ruckebusch
1 Introduction
453(2)
2 Smoothing Signals and Images
455(6)
2.1 Smooth vs Rough Information
456(1)
2.2 Smoothing with Splines
456(2)
2.3 Smoothing Images with Tensor Products of Splines
458(3)
2.4 Parameter Settings
461(1)
3 Smoothness Constraints in MCR-ALS
461(9)
3.1 Application to Process Spectroscopy Data
462(3)
3.2 Constraining Smoothness in HSI Data
465(5)
4 Case Studies
470(5)
4.1 Ultrafast Time-Resolved Absorption Spectroscopy of Salicylidene Aniline
470(2)
4.2 Hyperspectral Imaging of an Oil-in-Vinegar Emulsion
472(3)
5 Concluding Remarks
475(1)
Acknowledgments
475(1)
References
475(2)
15 Super-Resolution in Vibrational Spectroscopy: From Multiple Low-Resolution Images to High-Resolution Images
477(42)
M. Offroy
L. Duponchel
1 Introduction
478(2)
2 The Super-Resolution Concept
480(4)
2.1 The Analytical Model Used in the Super-Resolution Concept
481(2)
2.2 The Super-Resolution Model in Far-Field Imaging Spectroscopy
483(1)
3 Criteria to Measure the Spatial Resolution in Imaging Spectroscopy
484(2)
4 Case Study #1: Super-Resolution Concept in MIR Spectroscopy [ 16]
486(8)
4.1 FTIR Instrument
486(1)
4.2 The Target Sample
487(1)
4.3 The Intrinsic Spatial Resolution Evaluation with a Target Sample
488(4)
4.4 The Super-Resolution Concept Evaluation with the Target Sample
492(2)
4.5 Super-Resolution in MIR Imaging for Real Sample Exploration
494(1)
5 Optimization and Study of the Super-Resolution Concept in Near-Infrared Spectroscopy [ 19]
494(9)
5.1 NIR Imaging Instrumentation
496(1)
5.2 Spatial Resolution Evaluation Before and After Super-Resolution
497(3)
5.3 The Super-Resolution Concept Applied on Pharmaceutical Samples in NIR Imaging
500(3)
6 Optimization and Study of the Super-Resolution Concept for Raman Confocal Imaging [ 24]
503(12)
6.1 Raman Instrumentation
503(1)
6.2 The Intrinsic Spatial Resolution Evaluation with a Target Sample
504(7)
6.3 Spectral and Spatial Characterizations of Aerosols
511(4)
7 Conclusion
515(1)
References
515(4)
16 Multivariate Curve Resolution for Magnetic Resonance Image Analysis: Applications in Prostate Cancer Biomarkers Development
519(32)
J.M. Prats-Montalban
E. Aguado Sarrio
A. Ferrer
1 Introduction
519(2)
2 State-of-the-Art Methods
521(5)
2.1 Dynamic Contrast Enhanced-Magnetic Resonance Imaging
521(2)
2.2 Diffusion Weighted-Magnetic Resonance Imaging
523(3)
3 New Biomarkers Development
526(21)
3.1 Materials and Methods
528(1)
3.2 Dynamic Contrast Enhanced-Magnetic Resonance Imaging
529(8)
3.3 Diffusion Weighted-Magnetic Resonance Imaging
537(10)
4 Conclusions
547(1)
Acknowledgments
547(1)
References
547(4)
17 Endmember Library Approaches to Resolve Spectral Mixing Problems in Remotely Sensed Data: Potential, Challenges, and Applications
551(28)
B. Somers
L. Tits
D. Roberts
E. Wetherley
1 Introduction
552(3)
2 Endmember Library-Based SMA Approaches
555(4)
2.1 Iterative Mixture Cycles
555(1)
2.2 Sparse Unmixing
555(3)
2.3 Machine Learning-Based Estimation of Land Cover Fractions
558(1)
3 Challenges of Endmember Library-Based SMA Approaches
559(4)
3.1 How to Build Comprehensive Endmember Libraries for SMA?
559(2)
3.2 How to Deal with Extensive Spectral Libraries?
561(2)
3.3 Preprocessing of Endmember Libraries as a Way to Further Increase Unmixing Accuracy?
563(1)
4 Applications of Endmember Library Approaches to Resolve Spectral Mixing Problems in Remotely Sensed Data
563(10)
4.1 Case Study 1: MESMA for Studying Fire Severity and PostFire Recovery in a Mediterranean-Climate Ecosystem
563(5)
4.2 Case Study 2: MESMA for Studying Crop Vigor State at a Subpixel Level
568(5)
5 Conclusions
573(1)
Acknowledgments
573(1)
References
574(5)
18 Spectral-Spatial Unmixing Approaches in Hyperspectral VNIR/SWIR Imaging
579(34)
N. Gorretta
C. Gomez
1 Introduction
579(4)
2 Background on Linear Spectral Unmixing
583(6)
2.1 Dimension Reduction Step
583(2)
2.2 Endmembers Spectra Extraction and Their Identification
585(2)
2.3 Abundance Estimation
587(1)
2.4 Alternative Linear Unmixing Methods
587(1)
2.5 Difficulties in Spectral Unmixing
588(1)
3 Use of Spatial Information in Unmixing Process
589(14)
3.1 Use of Spatial Information for Endmember Extraction
589(8)
3.2 Spatial Information in Abundance Estimation
597(6)
4 Conclusions
603(2)
References
605(8)
19 Sparse-Based Modeling of Hyperspectral Data
613(22)
R. Calvini
A. Ulrici
J.M. Amigo
1 Introduction
613(2)
1.1 Hyperspectral Imaging in Context
613(1)
1.2 Feature Extraction Methods
614(1)
2 Theory: Sparse Methods
615(3)
2.1 The Lasso
616(1)
2.2 Sparse Principal Component Analysis
617(1)
3 Sparse-Based Image Exploration: Applications
618(13)
3.1 Detection of Differences Between Groups of Homogeneous Samples: Arabica and Robusta Green Coffee
619(5)
3.2 Detection of Outliers: Plastics Pieces
624(7)
4 Conclusions
631(1)
References
632(3)
Index 635
Cyril Ruckebusch is currently a Professor at Ecole PolytechLille, Université de Lille - Sciences et Technologies. He is doing his research at LASIR, a mixed CNRS-University Lille research unit. Cyril was previously Associate Professor at University of Lille since 2008 when he obtained the qualification for full-professorship (habilitation in physical chemistry). He received his PhD in Engineering Science in 2000. His current research focuses mainly on the development and application of chemometrics in advanced spectroscopy and imaging. He has published over 70 papers in international journals and coordinated international scientific collaboration research programs and industrial and technological projects. He is Associate Editor for reviews of the Journal of Chemometrics and Editorial Adviser of Analytica Chimica Acta.
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