The term magnetic resonance covers a wide range of techniques, spectroscopy, relaxation and imaging. In turn, these areas are evolving and leading to various new applications of NMR and ESR in food science and nutrition. From assessment of meat quality, through to a study of beer components and the effect of microwaves on potato texture, Magnetic Resonance in Food Science: Latest Developments provides an account of the state of the art in this lively area. Coverage includes: recent developments in magnetic resonance; human aspects of food; structure and dynamics in food; and food quality control. With contributions from international experts, this book is essential reading for academics and industrialists in food science. It is the latest in a series of titles in this area published by the RSC.

Biology has entered the age of Big Data. The technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce questions to a comparison of algebraic invariants, such as numbers, which are typically easier to solve. Topological data analysis is a rapidly-developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolutionary biology alongside mathematicians interested in applied topology.

An integrated package of powerful probabilistic tools and key applications in modern mathematical data science.

This work presents a data visualization technique that combines graph-based topology representation and dimensionality reduction methods to visualize the intrinsic data structure in a low-dimensional vector space. The application of graphs in clustering and visualization has several advantages. A graph of important edges (where edges characterize relations and weights represent similarities or distances) provides a compact representation of the entire complex data set. This text describes clustering and visualization methods that are able to utilize information hidden in these graphs, based on the synergistic combination of clustering, graph-theory, neural networks, data visualization, dimensionality reduction, fuzzy methods, and topology learning. The work contains numerous examples to aid in the understanding and implementation of the proposed algorithms, supported by a MATLAB toolbox available at an associated website.

Publisher Description

Molecular biology operates at three levels – genes, proteins and metabolites. This book is unique in that it provides a comprehensive description of an approach (metabonomics) to characterise the endogenous metabolites in a living system, complementing gene and protein studies (genomics and proteomics). These "omics" methods form the basis for understanding biology at a systems level. The Handbook of Metabonomics and Metabolomics aims to be the definitive work on the rapidly expanding subjects of metabolic profiling, metabolite and biomarker identification, encompassing the fields of metabonomics and metabolomics. It covers the principles of the subject, the analytical and statistical techniques used and the wide variety of applications.* comprehensive description of an approach (metabonomics) to characterise the endogenous metabolites in a living system, complementing gene and protein studies* aims to be the definitive work on the rapidly expanding subjects of metabolic profiling, metabolite and biomarker identification* covers the principles of the subject, the analytical and statistical techniques used and the wide variety of applications.