Francesco Dell’Endice holds a PhD in Spectroscopy from University of Zurich and has a background in Aerospace Engineering. During his PhD he has been involved in building a hyperspectral camera for the European Space Agency, whose goal was to measure the distribution of biochemical properties in natural targets such as plants, trees, water and soil from a satellite. In early 2010, he founded QualySense with the mission of bringing biochemical sorting in the food industry. To date, QualySense has won several prizes and has closed several projects with leading food and seed companies worldwide.


The consumer preferences for certain food products strongly depends on the quality of the raw materials such as grains and beans, which influence: 1. The sensorial properties of the end-products 2. The possibility of applying health related claims 3. The overall nutritional value. Today, raw material quality is measured on an average basis and no insight on its variance isavailable: e.g. two lots of wheat grains may have the same average protein content but very different standard deviation. This is due to the fact that there are no high-speed single kernel analyzers. QualySense team has developed, with the support of the USDA and of Swiss research laboratories, the QSorter Explorer robot, a high-speed single kernel analyzer and sorter with a multi-kg-per-hour. The QSorter Explorer transports each kernel at the speed of 50 per second. A Near-Infrared reflectance spectrum between 900 nm and 1700 nm and a color image are taken per each kernel and processed by artificial intelligence algorithms to concurrently measure quality parameters such as biochemical properties (e.g. protein, oil, sugar), physical parameters(e.g. size, shape, color), or defects (e.g. broken, shriveled, infected by diseases). The QSorter Explorer is mainly used to: 4. Develop novel food and drink products sorting kernels based on quality criteria 5. Accelerate manual quality inspection procedures in processing plants To date, some of the successful applications of the QSorter Explorer robot include: • Identification of other cereals in lots of “gluten-free” oats. • Measurement of the ratio between Oleic and Linoleic acids in peanuts. • Prediction of coffee cup quality from unroasted beans. • Prediction of baking quality parameters from unprocessed wheat kernels. QualySense is also developing a higher capacity version of the QSorter robot.