Meat adulteration detection through digital image analysis of histological cuts using LBP
This addresses food fraud detection for public health and economic stakeholders, but appears incremental as it applies an existing method to a new dataset.
The paper tackled the problem of detecting bovine meat adulteration by evaluating digital image analysis methods, specifically the Local Binary Pattern (LBP) algorithm, on histological cut images, achieving results that indicate potential for fraud identification.
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.