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Food Safety pp 127-148 | Cite as

Food Adulteration and Authenticity

  • M. KamruzzamanEmail author
Chapter

Abstract

Authenticity and detection of adulteration are the increasing challenges for researchers, consumers, industries, and regulatory agencies. Traditional approaches may not be the most effective option to fight against adulteration. Much effort has been spent in both academia and industry to develop rapid and nondestructive optical techniques for detecting adulteration. Among them, hyperspectral imaging is one of the most promising. Hyperspectral imaging is a rapid, reagentless, nondestructive analytical technique that integrates spectroscopic and imaging techniques into one system to attain both spectral and spatial information simultaneously from an object that cannot be achieved with either digital imaging or conventional spectroscopic techniques. Associated with multivariate analyses, the technique has been successfully implemented for rapid and nondestructive inspection of various food products. In this chapter, latest research outcomes for authenticity and detecting adulteration using hyperspectral imaging will be highlighted and described. Additionally, challenges, opportunities, and future trends of hyperspectral imaging will also be discussed.

Keywords

Partial Little Square Regression Hyperspectral Imaging Partial Little Square Regression Model Successive Projection Algorithm Mince Meat 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  1. 1.Department of Food Technology and Rural Industries, Faculty of Agricultural Engineering and TechnologyBangladesh Agricultural UniversityMymensinghBangladesh

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