Ciboris has recently finished a research project entitled ‘Food fingerprinting as a tool to control food authenticity’, subsidized by VLAIO (Flemish government: Vlaamse Agentschap Innoveren en Ondernemen. This project focused on the detection of various types of food fraud in oregano and rice. Our partners were Ghent University (Prof. B. De Meulenaer, Prof. L. Jaxcsens, Prof. K. Demeestere and Prof. C. Walgraeve), CRA-W (Centre Walloon de Recherches Agronomiques) and ML2Grow (company for advanced machine learning, spin-off UGent).
We recently published an article in VMT about this project. It can be found, when following this link (in dutch) 'Onderzoek: fraude met oregano en rijst snel detecteren met analytische vingerafdruk (vmt.nl)
Several analytical techniques, such as Near Infrared and Mid-Infrared Spectroscopy, Hyperspectral Imaging, Gas Chromatography coupled to Mass Spectrometry and Proton-transfer Reaction Time-of-Flight Mass spectrometry, combined with chemometrics, were examined to evaluate their potential to solve different food fraud and quality control issues.
Concerning oregano, successful models were made to determine the country of origin, to identify adulteration and for batch-to-batch control. With our database of genuine oregano samples and chemometric models, it was possible to differentiate oregano samples from Italy, Turkey, Israel and South-America. It was also shown that we can achieve batch-to-batch control from incoming raw materials. Adulteration with sumac, myrtle, olive tree and cistus leaves was examined. It is possible with our database and chemometric models to detect adulteration starting up from 10% adulteration.
In our rice project, we have achieved two major goals. With our database of genuine rice samples and chemometric models, it is possible to distinguish rice samples coming from different countries i.e. Thailand, Vietnam, Spain, Italy and Pakistan. Additionally we could differentiate between different varieties as Basmati, White, Glutinous, Loto, Jsendra and Puntal rice.
We also performed some data-fusion to obtain more robust and more accurate classification models. For origin assessment of rice, we combined NIR and GC-MS data to develop a classification model. We did a validation of the classification model resulting in following confusion matrix with prediction rates for origin assessment:
It is clear the studied techniques can be used for different food fraud questions in herbs such as oregano and cereals such as rice. It is important to note this type of analysis requires a partner that has access to the right samples and knowledge. If you have questions concerning food fraud detection for your company, Ciboris can be a partner to setup food fraud detection for your specific needs.
Current projects also include food fraud in fruit juice and vegetables.
Tel.: +32 9 330 10 10