Data Handling in Science and Technology – serie

Fundamentals and Applications of Multiway Data Analysis provides comprehensive coverage of the main aspects of multiway analysis, including selected applications that can resolve complex analytical chemistry problems. This book follows on from Fundamentals and Analytical Applications of Multiway Calibration, (2015) by addressing new theoretical analysis and applications on subjects beyond multiway calibration and devoted to the analysis of multiway data for other purposes. Specifically, this new volume presents researchers a set of effective tools they can use to obtain the maximum information from instrumental data.This book includes the most advanced techniques, methods, and algorithms related to multiway modelling for solving calibration and classification tasks and the way they can be applied. It collects contributions from a selected number of well-known and active chemometric research groups across the world, each covering one or more subjects where their expertise is recognized and appreciated.

Includes chapters written by renowned international authors, all currently active in the subject fieldPresents coverage of all the main aspects of multi-way analytical data analysis, concerning the two main areas of interest: data generation and algorithmic models for data processingProvides up-to-date material with reference to current literature on the subject

Hyperspectral Imaging, Second Edition builds on the foundational insights of the first whilst reflecting the rapid advancements in hyperspectral and multispectral imaging technologies and methodologies in three directions: hardware, software, and applications. This heavily updated and expanded book not only covers the core analytical frameworks but also introduces new algorithms, hardware technology and cutting-edge applications that have emerged since the first edition. By incorporating the latest research findings and case studies, the contributions provide readers with enhanced tools and techniques for effective hyperspectral and multispectral image analysis across diverse scientific, industrial and field domains. Additionally, the book once again addresses evolving challenges and fresh opportunities in the field, ensuring that users are equipped with the most current knowledge and practices. This comprehensive update underscores the continued relevance and transformative potential of hyperspectral and multispectral imaging in contemporary research and industry. Hyperspectral Imaging, Second Edition is written for graduate students, academics and early researchers as well as industry scientists across various disciplines working with hyperspectral and multispectral images, including analytical chemistry, remote sensing, vegetation and crops, food and feed production, forensic sciences, biochemistry, medical imaging, pharmaceutical production, art studies, cultural heritage, among other topics.Provides a comprehensive roadmap of hyperspectral and multispectral image analysis, with benefits and considerations for each method discussedCovers state-of-the-art applications in different scientific fieldsDiscusses the implementation of hyperspectral devices in different environments

Most chemists, whether they are biochemists, organic, analytical, pharmaceutical or clinical chemists and many pharmacists and biologists need to perform chemical analysis. Consequently, they are not only confronted with carrying out the actual analysis, but also with problems such as method selection, experimental design, optimization, calibration, data acquisition and handling, and statistics in order to obtain maximum relevant chemical information. In other words: they are confronted with chemometrics. This book on chemometrics, written by some of the leaders in the field, aims to provide a thorough, up-to-date introduction to this subject. The reader is given the opportunity to acquaint himself with the tools used in this discipline and the way in which they are applied. Some practical examples are given and the reader is shown how to select the appropriate tools in a given situation.

As such the book provides the means to approach and solve analytical problems strategically and systematically, without the need for the reader to become a fully-fledged chemometrician. The authors' aim was to write a tutorial book which would be useful to readers at every level in this field.

Wavelets seem to be the most efficient tool in signal denoising and compression. They can be used in an unlimited number of applications in all fields of chemistry where the instrumental signals are the source of information about the studied chemical systems or phenomena, and in all cases where these signals have to be archived. The quality of the instrumental signals determines the quality of answer to the basic analytical questions: how many components are in the studied systems, what are these components like and what are their concentrations? Efficient compression of the signal sets can drastically speed up further processing such as data visualization, modelling (calibration and pattern recognition) and library search. Exploration of the possible applications of wavelets in analytical chemistry has just started and this book will significantly speed up the process.The first part, concentrating on theoretical aspects, is written in a tutorial-like manner, with simple numerical examples. For the reader's convenience, all basic terms are explained in detail and all unique properties of wavelets are pinpointed and compared with the other types of basis function. The second part presents applications of wavelets from many branches of chemistry which will stimulate chemists to further exploration of this exciting subject.

In recent years Genetic Algorithms (GA) and Artificial Neural Networks (ANN) have progressively increased in importance amongst the techniques routinely used in chemometrics. This book contains contributions from experts in the field is divided in two sections (GA and ANN). In each part, tutorial chapters are included in which the theoretical bases of each technique are expertly (but simply) described. These are followed by application chapters in which special emphasis will be given to the advantages of the application of GA or ANN to that specific problem, compared to classical techniques, and to the risks connected with its misuse. This book is of use to all those who are using or are interested in GA and ANN. Beginners can focus their attentions on the tutorials, whilst the most advanced readers will be more interested in looking at the applications of the techniques. It is also suitable as a reference book for students. Subject matter is steadily increasing in importance Comparison of Genetic Algorithms (GA) and Artificial Neural Networks (ANN) with the classical techniques Suitable for both beginners and advanced researchers

Statistical Design-Chemometrics is applicable to researchers and professionals who wish to perform experiments in chemometrics and carry out analysis of the data in the most efficient way possible. The language is clear, direct and oriented towards real applications. The book provides 106 exercises with answers to accompany the study of theoretical principles. Forty two cases studies with real data are presented showing designs and the complete statistical analyses for problems in the areas chromatography, electroanalytical and electrochemistry, calibration, polymers, gas adsorption, semiconductors, food technology, biotechnology, photochemistry, catalysis, detergents and ceramics. These studies serve as a guide that the reader can use to perform correct data analyses.-Provides 42 case studies containing step-by-step descriptions of calculational procedures that can be applied to most real optimization problems-Contains 106 theoretical exercises to test individual learning and to provide classroom exercises and material for written tests and exams-Written in a language that facilitates learning for physical and biological scientists and engineers-Takes a practical approach for those involved in industrial optimization problems

This volume presents the basic mathematics of ranking methods through a didactic approach and the integration of relevant applications. Ranking methods can be applied in several different fields, including decision support, toxicology, environmental problems, proteomics and genomics, analytical chemistry, food chemistry, and QSAR.Covers a wide range of applications, from the environment and toxicology to DNA sequencingIncorporates contributions from renowned experts in the fieldMeets the increasing demand for literature concerned with ranking methods and their applications

The issues related to food science and authentication are of particular importance for researchers, consumers and regulatory entities. The need to guarantee quality foodstuff - where the word "quality" encompasses many different meanings, including e.g. nutritional value, safety of use, absence of alteration and adulterations, genuineness, typicalness, etc. - has led researchers to look for increasingly effective tools to investigate and deal with food chemistry problems. As even the simplest food is a complex matrix, the way to investigate its chemistry cannot be other than multivariate. Therefore, chemometrics is a necessary and powerful tool for the field of food analysis and control.

For food science in general and food analysis and control in particular, there are several problems for which chemometrics are of utmost importance. Traceability, i.e. the possibility of verifying the animal/botanical, geographical and/or productive origin of a foodstuff, is, for instance, one area where the use of chemometric techniques is not only recommended but essential: indeed, at present no specific chemical and/or physico-chemical markers have been identified that can be univocally linked to the origin of a foodstuff and the only way of obtaining reliable traceability is by means of multivariate classification applied to experimental fingerprinting results.

Another area where chemometrics is of particular importance is in building the bridge between consumer preferences, sensory attributes and molecular profiling of food: by identifying latent structures among the data tables, bilinear modeling techniques (such as PCA, MCR, PLS and its various evolutions) can provide an interpretable and reliable connection among these domains. Other problems include process control and monitoring, the possibility of using RGB or hyperspectral imaging techniques to nondestructively check food quality, calibration of multidimensional or hyphenated instruments etc.

Fundamentals and Analytical Applications of Multi-Way Calibration presents researchers with a set of effective tools they can use to obtain the maximum information from instrumental data. It includes the most advanced techniques, methods, and algorithms related to multi-way calibration and the ways they can be applied to solve actual analytical problems.

This book provides a comprehensive coverage of the main aspects of multi-way analysis, including fundamentals and selected applications of chemometrics that can resolve complex analytical chemistry problems through the use of multi-way calibration.

Includes the most advanced techniques, methods, and algorithms related to multi-way calibration and the ways they can be applied to solve actual analytical problems Presents researchers with a set of effective tools they can use to obtain the maximum information from instrumental data Provides comprehensive coverage of the main aspects of multi-way analysis, including fundamentals and selected applications of chemometrics

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

Hyperspectral Imaging, Volume 32, presents a comprehensive exploration of the different analytical methodologies applied on hyperspectral imaging and a state-of-the-art analysis of applications in different scientific and industrial areas. This book presents, for the first time, a comprehensive collection of the main multivariate algorithms used for hyperspectral image analysis in different fields of application. The benefits, drawbacks and suitability of each are fully discussed, along with examples of their application. Users will find state-of-the art information on the machinery for hyperspectral image acquisition, along with a critical assessment of the usage of hyperspectral imaging in diverse scientific fields.

Provides a comprehensive roadmap of hyperspectral image analysis, with benefits and considerations for each method discussed Covers state-of-the-art applications in different scientific fields Discusses the implementation of hyperspectral devices in different environments

Data Fusion Methodology and Applications explores the data-driven discovery paradigm in science and the need to handle large amounts of diverse data. Drivers of this change include the increased availability and accessibility of hyphenated analytical platforms, imaging techniques, the explosion of omics data, and the development of information technology. As data-driven research deals with an inductive attitude that aims to extract information and build models capable of inferring the underlying phenomena from the data itself, this book explores the challenges and methodologies used to integrate data from multiple sources, analytical platforms, different modalities, and varying timescales.

Presents the first comprehensive textbook on data fusion, focusing on all aspects of data-driven discovery Includes comprehensible, theoretical chapters written for large and diverse audiences Provides a wealth of selected application to the topics included

Analyzing observed or measured data is an important step in applied sciences. The recent increase in computer capacity has resulted in a revolution both in data collection and data analysis. An increasing number of scientists, researchers and students are venturing into statistical data analysis; hence the need for more guidance in this field, which was previously dominated mainly by statisticians.

This handbook fills the gap in the range of textbooks on data analysis. Written in a dictionary format, it will serve as a comprehensive reference book in a rapidly growing field. However, this book is more structured than an ordinary dictionary, where each entry is a separate, self-contained entity. The authors provide not only definitions and short descriptions, but also offer an overview of the different topics. Therefore, the handbook can also be used as a companion to textbooks for undergraduate or graduate courses.

1700 entries are given in alphabetical order grouped into 20 topics and each topic is organized in a hierarchical fashion. Additional specific entries on a topic can be easily found by following the cross-references in a top-down manner. Several figures and tables are provided to enhance the comprehension of the topics and a list of acronyms helps to locate the full terminologies. The bibliography offers suggestions for further reading.

This book gives an overview of the numerical data analysis and signal treatment techniques that are used in chromatography and related separation techniques. Emphasis is given to the description of the symmetrical and asymmetrical chromatographic peak shape models. Both theoretical and empirical models are discussed.

The fundamentals of data acquisition, types and effect of baseline noise, and methods of improving the signal-to-noise ratio (either in time or in frequency and wavelet domain) are thoroughly discussed. Resolution enhancement techniques, such as curve fitting, deconvolution by Fourier and wavelet transforms, iterative deconvolution, Kalman filtering and multivariate methods of curve resolution are all discussed with several chromatographic examples. Quantitative analysis by peak area of peak height measurement, the precision and accuracy of the quantitation of stand-alone or overlapping and symmetrical or asymmetrical peaks are treated. In a separate chapter, guidelines are given for the use of transform techniques for the analysis of chromatograms.

A statistical description of peak overlap is given in the final chapters. Since the concept of resolution has to be reconsidered when one separates complex mixtures, the problem of resolution and overlap is quantitatively discussed by means of statistical methods, and by using Fourier analysis of the complex chromatogram.

Features of this book

. The ultimate source of numerical techniques to enhance chromatographic data

. Gives a detailed description of signal and resolution enhancement techniques in a manner applicable for enhancing not only chromatography, but also spectroscopic and other analytical signals

. The first book with a thorough overview of the statistics of peak overlap.

This is the first volume to encompass both the simple and more sophisticated methods for the numerical treatment of chromatograms. It is, therefore, the fundamental resource of numerical analysis methods for every analyst.

Now available is the second edition of a book which has been described as "...an exceptionally lucid, easy-to-read presentation... would be an excellent addition to the collection of every analytical chemist. I recommend it with great enthusiasm." (Analytical Chemistry)

N.R. Draper reviewed the first edition in Publication of the International Statistical Institute "...discussion is careful, sensible, amicable, and modern and can be recommended for the intended readership."

The scope of the first edition has been revised, enlarged and expanded. Approximately 30% of the text is new. The book first introduces the reader to the fundamentals of experimental design. Systems theory, response surface concepts, and basic statistics serve as a basis for the further development of matrix least squares and hypothesis testing. The effects of different experimental designs and different models on the variance-covariance matrix and on the analysis of variance (ANOVA) are extensively discussed. Applications and advanced topics (such as confidence bands, rotatability, and confounding) complete the text. Numerous worked examples are presented.

The clear and practical approach adopted by the authors makes the book applicable to a wide audience. It will appeal particularly to those with a practical need (scientists, engineers, managers, research workers) who have completed their formal education but who still need to know efficient ways of carrying out experiments. It will also be an ideal text for advanced undergraduate and graduate students following courses in chemometrics, data acquisition and treatment, and design of experiments.

The state-of-the-art of multivariate analysis in sensory science is described in this volume. Both methods for aggregated and individual sensory profiles are discussed. Processes and results are presented in such a way that they can be understood not only by statisticians but also by experienced sensory panel leaders and users of sensory analysis.

The techniques presented are focused on examples and interpretation rather than on the technical aspects, with an emphasis on new and important methods which are possibly not so well known to scientists in the field. Important features of the book are discussions on the relationship among the methods with a strong accent on the connection between problems and methods. All procedures presented are described in relation to sensory data and not as completely general statistical techniques.

Sensory scientists, applied statisticians, chemometricians, those working in consumer science, food scientists and agronomers will find this book of value.