Hyperspectral Imaging for Food Quality Analysis and Control

by Da-Wen Sun

Overview

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.

Product Details

ISBN-13:	9780128102237
Publisher:	Elsevier Science
Publication date:	08/19/2016
Pages:	496
Product dimensions:	7.50(w) x 9.25(h) x (d)

About the Author

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.

Table of Contents

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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From the Publisher

Visualize the constituents of food samples at the pixel level for more accurate and detailed information than possible with any other technology