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fraud Scientists

Food fraud: Scientists develop ‘low-cost’ country of origin detection method

Fraud drains costs the global food industry an estimated $30 to $40 billion every year, PricewaterhouseCoopers estimates. According to the Consumer Brands Association, roughly 10% of commercially produced food and beverage products are affected by fraud.False claims can take many forms, from tampering and adulteration to misrepresentation and substitution. Scientists at the University of Basel…

Fraud drains costs the global food industry an estimated $30 to $40 billion every year, PricewaterhouseCoopers estimates. According to the Consumer Brands Association, roughly 10% of commercially produced food and beverage products are affected by fraud.

False claims can take many forms, from tampering and adulteration to misrepresentation and substitution. Scientists at the University of Basel in in Switzerland set out to address a common problem of food fraudulently placed on the market: false country of origin claims.

Strawberries from Switzerland or olive oil from Italy can be sold at much higher prices than the same products grown or manufactured in a different country. The economic motivation for fraudsters means the authorities and food industry invest a significant amount of time and money trying to fight false declarations of origin, the researchers noted.

One method for detecting food fraud is to determine the δ18O (delta-O-18) value of a product sample, which characterizes the oxygen isotope ratio. Until now, this procedure has been highly time consuming and costly. A case of suspected fraud involved not only collecting reference data from the claimed country of origin, but also comparative data from other regions to validate or disprove the product’s origin.

Cutting costs through model calculation

Basel botanist Dr. Florian Cueni has now developed a new model in collaboration with Agroisolab GmbH, a company specializing in isotope analysis. The research has been published in journal Scientific Reports.​ 

This model is intended for use in simulating the oxygen isotope ratio in plants from individual regions, eliminating the need for the time-consuming collection of reference data. It is based on temperature, precipitation and humidity data and information about the growing season of a plant, all of which are available from publicly acc

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