Food Adulteration and Authenticity
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.
KeywordsPartial Little Square Regression Hyperspectral Imaging Partial Little Square Regression Model Successive Projection Algorithm Mince Meat
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