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Metabolomics is a powerful tool for studying the chemical composition of biological systems. It can be used to identify and quantify metabolites, which are small molecules that play a role in metabolism. Metabolites can be used to track changes in biological systems in response to environmental conditions, such as stress or disease.
In this research, we will use both targeted and untargeted metabolomics workflows to study native species. We will use advanced analytical techniques, such as mass spectrometry and nuclear magnetic resonance spectroscopy. We will then use multivariate statistics to analyze the data and identify patterns that can be used to understand the biological systems of these plants and species.
Members: Dr. Manuela García; Dr. Federico Brigante
Metabolomics-based classification of solanum spp. through alkaloid markers and machine learning.
Metabolomics-based classification of solanum spp. through alkaloid markers and machine learning.
We are searching for markers in Solanum spp within the alkaloid and glycosylated alkaloid family, which are the major compounds in species of this genus, in order to find classification rules for the species. This is being done through metabolomics using NMR (400 MHz) and multivariate statistical and machine learning methods.
Latest Articles Published:
Latest Articles Published:
Authenticity assessment of commercial bakery products with chia, flax and sesame seeds: Application of targeted and untargeted metabolomics results from seeds and lab-scale cookies. Food Control, 2022, 140, 109114 https://doi.org/10.1016/j.foodcont.2022.109114
Identification of chia, flax and sesame seeds authenticity markers by NMR-based untargeted metabolomics and their validation in bakery products containing them. Food Chemistry, 2022, 387, 132925. https://doi.org/10.1016/j.foodchem.2022.132925
Comparative metabolite fingerprinting of chia, flax and sesame seeds using LC-MS untargeted metabolomics. Food Chemistry, 2022, 371, 131355 https://doi.org/10.1016/j.foodchem.2021.131355
Targeted metabolomics to assess the authenticity of bakery products containing chia, sesame and flax seeds. Food Chemistry, 2020, 312, 126059 https://doi.org/10.1016/j.foodchem.2019.126059
Discovery of food identity markers by metabolomics and machine learning technology. Scientific Reports, 2019, 9(1), 9697 https://doi.org/10.1038/s41598-019-46113-y (Open Access)
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