Juan Antonio Di Lorenzo
Machine learning-driven prediction of chemical compound sweetness based on molecular descriptors.
Rel. Marco Agostino Deriu, Lorenzo Pallante, Marta Malavolta. Politecnico di Torino, Master of science program in Biomedical Engineering, 2021
|
Preview |
PDF (Tesi_di_laurea)
- Thesis
Licence: Creative Commons Attribution Non-commercial No Derivatives. Download (4MB) | Preview |
Abstract
Taste is a complex sensation related to the perception of food flavours, acting as a key control mechanism to defend the organism from poisons or noxious substances: the five basic taste sensations, namely bitter, sour, salty, sweet and umami, evoked either alone or in combination by food molecules, may stimulate the intake or rejection of the latter. Sugars (e.g., glucose, sucrose) constitute a prominent example of this mechanism, inducing a powerfully motivating sensory response toward food in light of their role as one of the primary energy sources for the body. On the other hand, the well-known correlation between sugar abuse and diseases development, e.g., diabetes, obesity or cardiovascular problems, is driving the tendency to substitute natural sugars with low-calorie sweeteners, to promote an overall healthier lifestyle.
This consideration is spiking the interest in the development of artificial sweet molecules with a low calorie content but a strong sweetening ability, a process that can greatly benefit from the molecular-level identification of key chemical and physical features that ultimately result in a specific gustatory sensation
Relators
Academic year
Publication type
Number of Pages
Course of studies
Classe di laurea
Aziende collaboratrici
URI
![]() |
Modify record (reserved for operators) |
