Michele Ricci

Research Fellow

Email: m.ricci@unisg.it

University of Gastronomic Sciences | Pollenzo Campus
Piazza Vittorio Emanuele, 9
fraz. Pollenzo – 12042 Bra (Cn) – Italy



Michele Ricci started his academic formation at the University of Florence, obtaining a Food Science bachelor’s degree in 2016 with a thesis about developing analytical procedures for estimating B-group vitamins in different food products, after a one-year-long internship in a food chemistry lab as a laboratory technician.

In 2019, he graduated with a Master’s degree in Food Science and Technology from the University of Bologna with a thesis about the application of a microwave probe for the estimation of the water content in Italian Salami during ripening and an internship about the use of NMR technology at the Food Science Department in Cesena, Italy.

After graduation, he started a Ph. D. in the C3A department of the University of Trento (Italy) in collaboration with the Edmund Mach Foundation (FEM). The main topic of the project was statistical approaches for the context of the quality control procedure of an Italian D.O.P. hard hard-seasoned cheese. He obtained his doctorate in May 2023.

The project consisted of multiple activities, from a vast activity in data analysis and statistical modeling to the support of the activities of a sensory laboratory for quality control or quantitative descriptive sensory tests.

During the Project he studied the Sensory Science field, developing a strong interest in tools to infer significant information from the analytical sensory analysis and he learned how to use fluent programming languages such as R and Python to develop analytical solutions in multiple contexts, such as inferring information about the effect of multiple conditions of the production process from standard quality-control data, the analysis and reporting of multiple data from sensory analysis experiments, the development of image analysis algorithms, the analysis of complex multivariate datasets of volatile organic compounds and the development of an R package for a novel multivariate approach for the analysis of dynamic sensory data.




Perception and Quality



  • Food Science
  • Statistics
  • Sensory analysis