Strawberries from Switzerland or olive oil from Italy can be sold at much higher prices than the same products from other countries, and the food industry spends a great deal of time fighting false declarations of geographical origin that are assumed to cause an estimated $30 million to $40 billion a year in economic damage.
One method for detecting food fraud is to determine the delta-O-18 value of a product sample, which characterizes the oxygen isotope ratio. This procedure is generally highly time consuming and costly. A case of suspected fraud involves 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.
Florian Cueni, PhD, a botanist at the University of Basel in Switzerland, has, in collaboration with Agroisolab GmbH, a company specializing in isotope analysis, has developed a model intended for use in simulating the oxygen isotope ratio in plants from individual regions, thereby eliminating the need for the time-consuming collection of reference data. The model is based on temperature, precipitation, and humidity data and information about the growing season of a plant, all of which are available from publicly accessible databases, according to a new study published in Nature Scientific Reports.
Dr. Cueni tested and validated the model on a unique delta-O-18 reference dataset for strawberries collected across Europe over the span of 11 years. The case study has shown that the model can simulate the origin of the strawberries with a high degree of accuracy.
“With minor adjustments to the parameters, our model can be used to determine all plant products,” says Ansgar Kahmen, PhD, a researcher in the department of environmental sciences and botany, at the University of Basel, who led the project, adding that the model makes it possible to simplify and speed up conventional isotope analysis by accurately simulating the regions of origin of agricultural foodstuffs.