Elsevier Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Super-Resolution Imaging (Data Handling in Science and Technology): Volume 30

Product Description 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 problem Guides the reader through the fundamentals and details of the different methods Presents extensive plots, graphical representations, and illustrations to help readers understand the features of different techniques and to interpret results Bridges the gap between disciplines with contributions from a number of well-known and highly active chemometric and signal processing research groups Review 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 From the Back Cover Resolving Spectral Mixtures: With Applications from Ultrafast Time-Resolved Spectroscopy to Super-resolution Imaging, Volume 30 in the Data Handling in Science and Technology Series, offers a comprehensive look into the most important models and frameworks essential to resolving the spectral unmixing problem in chemistry. This issue remains of high interest in chemometrics, statistics, and image processing. However, the diversity of disciplines and methods (multivariate curve resolution, mixture analysis, blind source separation, linear unmixing, etc.) can be a serious impediment to wider understanding and dissemination of the spectral mixture problem. By taking a multi-angle and cross-disciplinary approach, this book provides a comprehensive and comprehensible description of the current state of the art. The book is multi-authored, written as a collection of independent chapters. Basic concepts and main methods are presented in the first part of the book, while the second part is oriented toward applications in chemistry and remote sensing. Some chapters are written as tutorial; others are reviews. Together, these chapters translate into a real interplay enabling the reader to understand the issues discussed and to enrich its own perspective. Key features Aimed