This book provides a comprehensive overview of how modern spectroscopic techniques can be applied to evaluate beverage quality. It focuses on demonstrating the practical utility of methods such as Near Infrared (NIR), UV–Visible, Raman, fluorescence and Laser-Induced Breakdown spectroscopy. By incorporating statistical tools and artificial intelligence, it shows how data analysis enhances the accuracy, efficiency, and reliability of spectroscopic assessments. Structured into two main parts, chapters balance theoretical foundations with real-world applications. The first half of the book introduces individual spectroscopic techniques, covering their principles and operational fundamentals in dedicated chapters. The second half applies these techniques to specific beverage categories, including juices, milk, alcoholic drinks, and selected components such as sugars and apple juice. It also integrates statistical and AI-based methods where they have been seen to improve analytical outcomes. This volume aims to bridge a gap in existing literature by bringing multiple techniques together in a single, cohesive resource while highlighting recent advancements in the field. It will be an ideal reference for postgraduate students and researchers in the food and beverage industry working with applications of spectroscopic techniques. Key features:Covers the applications of statistical tools and artificial intelligence for spectroscopic data analysis rarely described in other books. Presents enhanced beverage quality analysis learning experience using a combination of more than one spectroscopic technique and artificial intelligence wherever appropriate. Contains the minimum, necessary mathematical details needed to suit novice researchers.
