Machine Learning

Mathematical Biology Mathematics for Health
Data Knowledge
Ecology
Care and Ageing Big Data 
Epidemiology

 

 Machine Learning
Evolution & Genetics  

Project: TraSeaFood – Tracing the Geographic Origin of Seafoodimage3grafico2

Description: The TraSeaFood project aims to trace the source of marine endogenous resources captured and/or produced along the west and south-west coast of the Iberian Peninsula using elemental and/or biochemical signatures. Within the scope of this project, we intend to identify the elementary and biochemical signatures specific to each species and place of sampling, in order to be used for certification of origin. Elemental signatures are determined by inductively coupled plasma mass spectrometry (ICP-MS), with laser coupled (LA-ICP-MS) whenever necessary, whereas MS-based lipidomics, namely MS coupled to liquid chromatography (HILIC-ESI-MS), is used to determine biochemical signatures. The analysis will include the use of Machine Learning and advanced methods of multivariate statistics (classification, discrimination, and reduction of dimensionality). Ultimately, it is intended to build a package R that allows the worldwide dissemination of the use of the methodology of certification of origin of marine resources

CMA Researchers: Regina Bispo
Participant Institutions: CESAM, Univ. Aveiro CMA/NOVA

Methods: Machine learning | Multivariate statistics (classification, discrimination, and reduction of dimensionality)