Hyperspectral Imaging Analysis and Applications for Food Quality

In processing food, hyperspectral imaging, combined with intelligent software, enables digital sorters (or optical sorters) to identify and remove defects and foreign material that are invisible to traditional camera and laser sorters. Hyperspectral Imaging Analysis and Applications for Food Quality explores the theoretical and practical issues associated with the development, analysis, and application of essential image processing algorithms in order to exploit hyperspectral imaging for food quality evaluations. It outlines strategies and essential image processing routines that are necessary for making the appropriate decision during detection, classification, identification, quantification, and/or prediction processes. Features - Covers practical issues associated with the development, analysis, and application of essential image processing for food quality applications - Surveys the breadth of different image processing approaches adopted over the years in attempting to implement hyperspectral imaging for food quality monitoring - Explains the working principles of hyperspectral systems as well as the basic concept and structure of hyperspectral data - Describes the different approaches used during image acquisition, data collection, and visualization The book is divided into three sections. Section I discusses the fundamentals of Imaging Systems: How can hyperspectral image cube acquisition be optimized? Also, two chapters deal with image segmentation, data extraction, and treatment. Seven chapters comprise Section II, which deals with Chemometrics. One explains the fundamentals of multivariate analysis and techniques while in six other chapters the reader will find information on and applications of a number of chemometric techniques: principal component analysis, partial least squares analysis, linear discriminant model, support vector machines, decision trees, and artificial neural networks. In the last section, Applications, numerous examples are given of applications of hyperspectral imaging systems in fish, meat, fruits, vegetables, medicinal herbs, dairy products, beverages, and food additives.

Nrusingha Charan Basantia is a Senior Lead Scientist at Cavinkare Research Centre of Cavinkare Private Ltd., at Chennai, India. N. C. Basantia graduated as Ph.D, Dairy Chemistry at National Dairy Research Institute, Karnal, India in 2002 and as M. Sc, Dairy Chemistry at National Dairy Research Institute, Karnal, India in 1997. His technical expertise and experiences are analysis of dairy foods and others, HPLC and GC, and ISO laboratory work He is member of a number of Technical Committees such as Bureau of Indian Standard and ISO/IEC17025 ISO/IEC 17025 for Cosmetic Products. He is author and coauthor of numerous papers and book chapters. Mohammed Kamruzzaman is Professor and Head at the Department of Food Technology and Rural Industries of Bangladesh Agricultural University. Professor Kamruzzaman graduated as PhD, Food and Biosystems Engineering, in 2013, at University College Dublin, Ireland and as MSc, Food Science, Technology & Nutrition, in 2009 at Dublin Institute of Technology Ireland, and MS, Food Technology, in 2005 at Bangladesh Agricultural University, Bangladesh. He did Post-doctoral, doctoral and masters research at University of California, Davis (UC Davis), USA; at University of Tokyo, Japan; at University College Dublin, Ireland; at KaHo Sint-Lieven, (an associate of KU Leuven), Belgium; and at Bangladesh Agricultural University, Bangladesh He has teaching Experience at the Department of Food Technology and Rural Industries (DFTRI), Bangladesh Agricultural University (BAU). Bangladesh. He is an Editorial Board Member of Journal of Biosystems Engineering. and Member of Institute of Food Technologists (IFT). He is author of numerous articles in peer reviewed international journals and book chapters.

Imaging Systems. Fundamentals. Techniques. Image Acquisition, Calibration, Image Pre-processing. Image Segmentation. Data Extraction and Treatment. Detection. Chemometrics. Instrument Grading. Principal Component Analysis. Partial Least Squares Analysis. Fisher's Linear Discriminant Model. Support Vector Machines. Decision Tree. Multivariate Analysis and Techniques. Image Systems: A Part of Non-Invasive Sensing of Food. Image Systems: A Part of Non-Invasive Sensing of Food. Applications. Applications in Fish. Applications in Meat. Applications in Fruits. Applications in Vegetables. Applications in Medicinal Herbs. Applications in Dairy Products.