Data Science for Sensory and Consumer Scientists is a comprehensive textbook that provides a practical guide to using data science in the field of sensory and consumer science through real-world applications. It covers key topics including data manipulation, preparation, visualization, and analysis, as well as automated reporting, machine learning, text analysis, and dashboard creation. Written by leading experts in the field, this book is an essential resource for anyone looking to master the tools and techniques of data science and apply them to the study of consumer behavior and sensory-led product development. Whether you are a seasoned professional or a student just starting out, this book is the ideal guide to using data science to drive insights and inform decision-making in the sensory and consumer sciences. Key Features: • Elucidation of data scientific workflow. • Introduction to reproducible research. • In-depth coverage of data-scientific topics germane to sensory and consumer science. • Examples based in industrial practice used throughout the book

As we move further into the 21st Century, sensory and consumer studies continue to develop, playing an important role in food science and industry. These studies are crucial for understanding the relation between food properties on one side and human liking and buying behaviour on the other. This book by a group of established scientists gives a comprehensive, up-to-date overview of the most common statistical methods for handling data from both trained sensory panels and consumer studies of food. It presents the topic in two distinct sections: problem-orientated (Part I) and method orientated (Part II), making it to appropriate for people at different levels with respect to their statistical skills. This book succesfully: Makes a clear distinction between studies using a trained sensory panel and studies using consumers. Concentrates on experimental studies with focus on how sensory assessors or consumers perceive and assess various product properties. Focuses on relationships between methods and techniques and on considering all of them as special cases of more general statistical methodologies It is assumed that the reader has a basic knowledge of statistics and the most important data collection methods within sensory and consumer science. This text is aimed at food scientists and food engineers working in research and industry, as well as food science students at master and PhD level. In addition, applied statisticians with special interest in food science will also find relevant information within the book.

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.

Mathematical Engineering of Deep Learning provides a complete and concise overview of deep learning using the language of mathematics. The book provides a self-contained background on machine learning and optimization algorithms and progresses through the key ideas of deep learning. These ideas and architectures include deep neural networks, convolutional models, recurrent models, long/short-term memory, the attention mechanism, transformers, variational auto-encoders, diffusion models, generative adversarial networks, reinforcement learning, and graph neural networks. Concepts are presented using simple mathematical equations together with a concise description of relevant tricks of the trade. The content is the foundation for state-of-the-art artificial intelligence applications, involving images, sound, large language models, and other domains. The focus is on the basic mathematical description of algorithms and methods and does not require computer programming. The presentation is also agnostic to neuroscientific relationships, historical perspectives, and theoretical research. The benefit of such a concise approach is that a mathematically equipped reader can quickly grasp the essence of deep learning. Key Features: A perfect summary of deep learning not tied to any computer language, or computational framework. An ideal handbook of deep learning for readers that feel comfortable with mathematical notation. An up-to-date description of the most influential deep learning ideas that have made an impact on vision, sound, natural language understanding, and scientific domains. The exposition is not tied to the historical development of the field or to neuroscience, allowing the reader to quickly grasp the essentials. Deep learning is easily described through the language of mathematics at a level accessible to many professionals. Readers from fields such as engineering, statistics, physics, pure mathematics, econometrics, operations research, quantitative management, quantitative biology, applied machine learning, or applied deep learning will quickly gain insights into the key mathematical engineering components of the field.

Sports analytics is on the rise, with top soccer clubs, bookmakers, and broadcasters all employing statisticians and data scientists to gain an edge over their competitors. Many popular books have been written exploring the mathematics of soccer. However, few supply details on how soccer data can be analysed in real-life. The book addresses this issue via a practical route one approach designed to show readers how to successfully tackle a range of soccer related problems using the easy-to-learn computer language R. Through a series of easy-to-follow examples, the book explains how R can be used to: Download and edit soccer data Produce graphics and statistics Predict match outcomes and final league positions Formulate betting strategies Rank teams Construct passing networks Assess match play Soccer Analytics: An Introduction Using R is a comprehensive introduction to soccer analytics aimed at all those interested in analysing soccer data, be they fans, gamblers, coaches, sports scientists, or data scientists and statisticians wishing to pursue a career in professional soccer. It aims to equip the reader with the knowledge and skills required to confidently analyse soccer data using R, all in a few easy lessons.

Natural Language Processing in the Real World is a practical guide for applying data science and machine learning to build Natural Language Processing (NLP) solutions. Where traditional, academic-taught NLP is often accompanied by a data source or dataset to aid solution building, this book is situated in the real world where there may not be an existing rich dataset. This book covers the basic concepts behind NLP and text processing and discusses the applications across 15 industry verticals. From data sources and extraction to transformation and modelling, and classic Machine Learning to Deep Learning and Transformers, several popular applications of NLP are discussed and implemented. This book provides a hands-on and holistic guide for anyone looking to build NLP solutions, from students of Computer Science to those involved in large-scale industrial projects.

The sixth edition of this classic text brings sensory evaluation to life for new students and experienced professionals alike. A full array of sensory methods is covered – including descriptive techniques, discrimination testing, and consumer research, plus guidance on test design, statistical analysis, and how to translate results into insights for actionable decisions. Like its predecessors, Sensory Evaluation Techniques, Sixth Edition gives a clear and concise presentation of practical solutions, accepted methods, and standard practices, in addition to advanced techniques. What’s new in the sixth edition: An expanded chapter on Sensory Physiology, including recent research on individual differences in perception A thorough discussion of Thurstonian theory and its application to discrimination methods, including the Tetrad Test New sections on technology in sensory evaluation, including a discussion of software options for data collection Improved & updated case studies to aid learning comprehension Updated appendices for Spectrum Method attributes, references, and scales Updated references Online supplemental content Sensory Evaluation Techniques remains a practical, relevant, and flexible resource, providing how-to information for a wide variety of users in industry, government, and academia who need the most current information to conduct effective sensory research. It also supplies students with the necessary theoretical background in sensory evaluation methods, applications, and interpretations.