# name
Big data analysis applied to EEL spectroscopy

# metadata.dc.description[].value
Treballs Finals de Grau de Física, Facultat de Física, Universitat de Barcelona, Curs: 2018, Tutors: Sonia Estradé Albiol, Javier Blanco Portals

# metadata.dc.description.abstract[].value
In order to characterize an unknown sample, big data and machine learning methods are proposed. An electron energy loss (EEL) spectrum image obtained in the transmission electron microscope is analyzed. Applying principal component analysis (PCA) to image EEL spectra, the noise present in the raw data can be discarded, and comparing with existing datasets and alternatively through clustering analysis, the presence of vanadium and oxygen in the sample over a substrate with lanthanum and oxygen can be recognized

# metadata.dc.subject.classification[].value
Espectroscòpia de pèrdua d'energia d'electrons

# metadata.dc.title[].value
Big data analysis applied to EEL spectroscopy