Recently, there has been a great increase in the number counterfeit food scandals. Hence it is necessary for the food industry and government to pay more attention to this type of fraud and implement measures to assist with the identification of counterfeits. Infant formula products are sold at high prices, while in some countries, like China, established brands are preferred (regardless of their higher price) over local brands. This is largely due to past fraud scandals, like the infant melamine milk scandal, which negatively affected consumer trust. Hence this acts as a driver for fraudsters to produce counterfeits of established brands for additional illegal profit. Nowadays, there is a lack of rapid non-destructive methods to detect infant formula product counterfeiting. The development of such techniques would be valuable for food businesses, but it could also empower the consumer to act as the last line of defence.
The main objective of the project is to develop prevention and early stage recognition strategies for counterfeit infant formula.
The project will focus on the prevention and detection of counterfeit infant formula products through different data sources. In this case, different data sources are explored to identify the authentic or counterfeit infant formula products. Risk factor study should be focused on first, so that the risk level of whole infant formula industry could be analysed and the key points and factors could be pointed out. To explore the early trends of counterfeit infant formula products, QR-code scanning technology and GIS (geographic information system) will be examined in combination. Then rapid low-cost detection methods are used to identify the authenticity of the infant formula products for the preliminary identification. Developed detection methods will be used to confirm the accuracy of the rapid detection methods and provide a base for laboratory-based confirmatory identity assessments.