Based on the integration of computer vision and spectrscopy techniques, hyperspectral imaging is a novel technology for obtaining both spatial and spectral information on a product. Used for nearly 20 years in the aerospace and military industries, more recently hyperspectral imaging has emerged and matured into one of the most powerful and rapidly growing methods of non-destructive food quality analysis and control.
Hyperspectral Imaging for Food Quality Analysis and Control provides the core information about how this proven science can be practically applied for food quality assessment, including information on the equipment available and selection of the most appropriate of those instruments.
Additionally, real-world food-industry-based examples are included, giving the reader important insights into the actual application of the science in evaluating food products.
- Presentation of principles and instruments provides core understanding of how this science performs, as well as guideline on selecting the most appropriate equipment for implementation
- Includes real-world, practical application to demonstrate the viability and challenges of working with this technology
- Provides necessary information for making correct determination on use of hyperspectral imaging
Food engineers and technologists working in research, product and process development, and operations.
Undergraduate and graduate food science students exploring food quality and control
Part I: Fundamentals
Principles of Hyperspectral Imaging Technology
Spectral Pre-Processing Techniques
Hypercube Classification Methods
Hyperspectral Image Processing Techniques
Hyperspectral Imaging Instruments
Part II: Applications
Pork Quality Using a Hyperspectral Imaging System
Automated Poultry Carcass Inspection by Hyperspectral-Multispectral Line-Scan Imaging System
Quality Evaluation of Fish Fillets by Hyperspectral Imaging
Bruise Detection of Apples Using Hyperspectral Imaging; Analysis of Hyperspectral Images of Citrus Fruits
Using NIR Hyperspectral Imaging for Bruise Detection of Strawberr
Visualization of Sugar Distribution of Melons by Hyperspectral Technique
Measuring Ripening of Tomatoes Using Imaging Spectrometry
Hyperspectral Reflectance Imaging for Detection of Bruising on Pickling Cucumber
Classification of Wheat Kernels Using Near-Infrared Reflectance Hyperspectral Imaging.
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- © Academic Press 2010
- 20th May 2010
- Academic Press
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BACK OF BOOK 17 March 2016 Dr. Da-Wen Sun is internationally recognized for his leadership in food engineering research and education and is a highly respected journal editor. He is the recipient of numerous awards and honors including election to the Royal Irish Academy in 2010, selection as a Member of Academia Europaea (The Academy of Europe) in 2011, induction as a Fellow of International Academy of Food Science and Technology in 2012, recipient of the International Association for Food Protection (IAFP) Freezing Research Award in 2013, recipient of the International Association of Engineering and Food (IAEF) Lifetime Achievement Award in 2015, and named as a Thomson Reuters Highly Cited Researcher in 2015. Dr. Da-Wen Sun is internationally recognized for his leadership in food engineering research and education and a highly respected journal editor. He is the recipient of numerous awards and honors including election to the Royal Irish Academy in 2010, selection as a Member of Academia Europaea (The Academy of Europe) in 2011, induction as a Fellow of International Academy of Food Science and Technology in 2012, the International Association for Food Protection (IAFP) Freezing Research Award in 2013, the International Association of Engineering and Food (IAEF) Lifetime Achievement Award in 2015 and naming as 2015 Thomson Reuters Highly Cited Researcher. His many scholarly works have become standard reference materials for researchers in the areas of computer vision/hyperspectral imaging, computational fluid dynamics modelling, and vacuum cooling. Results of his work have been published in more than 400 peer-reviewed journal papers (Web of Science h-index = 66), among them; thirty papers have been selected by ESI as highly-cited papers, ranking him first in the world in Agricultural Sciences.
Professor of Food and Biosystems Engineering, South China University of Technology, China, and University College Dublin (UCD), Ireland