Green Chemistry in Food Analysis: Conventional and Emerging Approaches provides systematic analysis and up-to-date coverage of green sample preparation techniques in food analysis. The book compiles and discusses the advantages and limitations of sample preparation techniques that are relevant to green chemistry, addresses the latest developments in green spectroscopic and chromatographic techniques for food analysis, identifies and proposes solutions for the development of new strategies to solve real problems, and provides systematic and comprehensive coverage of emerging green materials, analytical technologies, and their applications in food analysis. Specific chapters are devoted to artificial intelligence, microfluidics and nanotechnology. The book's final section covers applications of these methods for the detection of allergens, dyes, pathogens and mycotoxins. - Provides systematic analysis and up-to-date coverage of green sample preparation techniques in food analysis - Covers recent developments in the use of clean, rapid and non-invasive spectroscopic and chromatographic approaches in analytical food analysis - Introduces new green materials such as solvents, adsorbents and sensors that are transforming food analysis - Addresses cutting-edge and emerging technologies in food analysis such as artificial intelligence, microfluidics and nano sensor technology - Covers current and potential applications of emerging green technologies in food analysis

Green Approaches for Chemical Analysis addresses emerging trends and technologies for the development of green analytical methods. The book covers basic principles of Green Analytical Chemistry (GAC) and describes the most up-to-date strategies used in areas such as sample preparation, instrumental analysis, and use and synthesis of green solvents and sorbents for separation. Many applications of analytical methods are discussed from a "green perspective, such as multiresidue analysis, metabolomics, food analysis, environmental monitoring, and bio-clinical applications. Written by experts in their fields, the book's chapters offer a variety of green analytical solutions readers can apply to their own analytical needs. - Combines an overview of the fundamental principles of Green Analytical Chemistry with applications in many various fields of research, including food, the environment and bioanalysis - Gives a critical overview of current analytical strategies and the applicability of green alternatives for various analytical purposes, comparing the efficacy of these approaches - Clarifies the link between analytical sample preparation and other methods

A brief, authoritative introduction to field experimentation in the social sciences. Written by two leading experts on experimental methods, this concise text covers the major aspects of experiment design, analysis, and interpretation in clear language. Students learn how to design randomized experiments, analyze the data, and interpret the findings. Beyond the authoritative coverage of the basic methodology, the authors include numerous features to help students achieve a deeper understanding of field experimentation, including rich examples from the social science literature, problem sets and discussions, data sets, and further readings.

An accessible, contemporary introduction to the methods for determining cause and effect in the Social Sciences “Causation versus correlation has been the basis of arguments—economic and otherwise—since the beginning of time. Causal Inference: The Mixtape uses legit real-world examples that I found genuinely thought-provoking. It’s rare that a book prompts readers to expand their outlook; this one did for me.”—Marvin Young (Young MC) Causal inference encompasses the tools that allow social scientists to determine what causes what. In a messy world, causal inference is what helps establish the causes and effects of the actions being studied—for example, the impact (or lack thereof) of increases in the minimum wage on employment, the effects of early childhood education on incarceration later in life, or the influence on economic growth of introducing malaria nets in developing regions. Scott Cunningham introduces students and practitioners to the methods necessary to arrive at meaningful answers to the questions of causation, using a range of modeling techniques and coding instructions for both the R and the Stata programming languages.