Ana Isabel Novo de Barros
University of Trás-os-Montes
University of Trás-os-Montes and Alto Douro
Ana Barros Assistant Professor with habilitation at the Chemistry Department of the University of Trás-os-Montes and Alto Douro, in the field of Organic Chemistry, Food Chemistry and Food Analysis. Integrated member from the Centre for the Research and Technology of Agro-Environmental and Biological Sciences (CITAB), responsible for the Phytochemicals Laboratory, and Coordinator of the Agri-Food Quality Group. Leader of the Thematic Line “Technology and innovation in the agri-food and forestry chains for a more competitive bioeconomy” and Director of the same Research Centre, since May 2017, rated with Very Good by the FCT's evaluation panel.
Author of 70 international peer reviewed papers and holder of 8 national and international patents. Coordinator of one ON2 project as the principal investigator and more 15 projects as participant. Supervised 6 PhD theses and 40 Master's theses. The current goals are linked to UNESCO codes: Biochemistry (2302), Analytical Chemistry (2301), Organic Chemistry (2306), Food Science and Technology (3309) and Nutrition (3206). The main lines of research are focus on:
- Synthesis and structural characterization by NMR of potentially bioactive phenolic compounds.
- Characterization and quantification of bioactive compounds of food matrices and by-products by HPLC.
- Evaluation of pre-harvest factors (species, variety, ripeness and agro-environmental factors) and post-harvest factors (agronomic, environmental, transport, storage or processing) in the content of bioactive phytochemicals.
- Evaluation of biological activity in vitro (antioxidant activity and capacity to modulate oxidative stress in cell models) of phytochemical constituents.
- Development of ingredients and functional foods based on the content of bioactive polyphenols available in the analyzed matrices.
- Use of infrared spectroscopy to determine the nutritional and phytochemical composition and as a technique for developing predictive models.
Vegetable foods, by-products, nutritional composition, amino acids, minerals, phenolic compounds, vitamin C, functional foods, bioactivity, HPLC-PAD, HPLC-PAD-MS / MS, UPLC / MS / MS, UPLC / FL, FTIR / NIR / MIR, NMR, antioxidant capacity.