Hyperspectral Imaging for Food Quality Analysis and Control

Erscheinungsjahr 2016
ISBN: 978-0-12-810223-7
Verlag: William Andrew Publishing

Buch, Englisch, 496 Seiten, Format (B × H): 191 mm x 235 mm, Gewicht: 1110 g

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.

Zielgruppe

Food engineers and technologists working in research, product and process development, and operations.
Secondary audience: Undergraduate and graduate food science students exploring food quality and control

Autoren/Hrsg.

Sun, Da-Wen

Fachgebiete

• Technische Wissenschaften Verfahrenstechnik | Chemieingenieurwesen | Biotechnologie Lebensmitteltechnologie und Getränketechnologie

Inhaltsverzeichnis

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.

Über Autor(innen)

Sun, Da-Wen
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.