|Support type:||Scholarships in Brazil - Scientific Initiation|
|Effective date (Start):||March 01, 2021|
|Effective date (End):||December 31, 2021|
|Field of knowledge:||Physical Sciences and Mathematics - Chemistry - Organic Chemistry|
|Principal researcher:||Daniel Rinaldo|
|Home Institution:||Faculdade de Ciências (FC). Universidade Estadual Paulista (UNESP). Campus de Bauru. Bauru , SP, Brazil|
Almonds are characterized by their high nutritional and commercial value, but currently there is growing attention to their vegetable residues (shell and hull), which are hardly characterized and exploited in relation to their properties as valuable sources of bioactive compounds (represented mainly phenolic acids and flavonoids), which are of therapeutic interest due to these secondary metabolites. For the extraction of these compounds, High Performance Liquid Chromatography (HPLC) is widely used as a routine analytical technique and uses acetonitrile (MeCN) as a highly toxic and polluting solvent, which results in considerable impacts to the environment and to the analyst. The scenario becomes even more negative when toxic solvents are also used in the extraction process, because in this stage, large volumes of solvents are generally used. This trend, in addition to resulting in considerable negative impacts on the environment and the experimenters involved, is completely inconsistent with the current principles of Green Analytical Chemistry, in addition to representing potential damage to ecosystems as a whole and to the biotic and abiotic relationships intrinsic to them, field of study from Natural Products. In this context, a holistic perspective is needed in such procedures, which guarantee a compromise between analytical efficiency and sustainability. Based on these aspects, this project aims to develop extraction methodologies of low environmental impact to obtain these secondary metabolites present in almond residues, using non-polluting and sustainable solvents (ethanol and deep natural eutectic solvents, or NADES ), whose efficiency will be optimized by multiparametric methods.