Without a trace

According to the US Food and Drug Administration( FDA), when someone intentionally leaves out, takes out, or substitutes a valuable ingredient or part of a food, it is known as economically motivated adulteration( EMA). One of the most recognizable forms of EMA – and one of the most challenging to prevent – is food fraud. One of the challenges when assessing food fraud prevention is frequency – product counterfeiting and food fraud are relatively rare events, which make it hard to predict when such an event might occur in the future. Deep data dives are a preferred course of action in food fraud prevention, and for good reason. The more insight gained from a situation or problem, the more effective a company can be at addressing an issue. But how can companies apply data and key learnings to prevent food fraud when it remains relatively rare in the world today? Typically, a quantitative or statistical model would be employed but with rare events, there will be little to no previous data from which to extrapolate future expectations quantitatively. This quantitative and analytical limitation has led to a focus on prevention and reducing vulnerabilities. Prevention includes a multi-layered approach of activities such as supplier approvals, product authenticity testing, and traceability.