  Food fraud has been an area of great concern due to its risk to public
health, reduction of food quality or nutritional value and for its economic
consequences. For this reason, it's been object of regulation in many countries
(e.g. [1], [2]). One type of food that has been frequently object of fraud
through the addition of water or an aqueous solution is bovine meat. The
traditional methods used to detect this kind of fraud are expensive,
time-consuming and depend on physicochemical analysis that require complex
laboratory techniques, specific for each added substance. In this paper, based
on digital images of histological cuts of adulterated and not-adulterated
(normal) bovine meat, we evaluate the of digital image analysis methods to
identify the aforementioned kind of fraud, with focus on the Local Binary
Pattern (LBP) algorithm.
