Support vector machine for EELS oxidation state determination

Abstract

Electron Energy-Loss Spectroscopy (EELS) is a powerful and versatile spectroscopic technique used to study the composition and local optoelectronic properties of nanometric materials. Currently, this technique is generating large amounts of spectra per experiment, producing a huge quantity of data to analyse. Several strategies can be applied in order to classify these data to map physical properties at the nanoscale. In the present study, the Support Vector Machine (SVM) algorithm is applied to EELS, and its effectiveness identifying EEL spectra is assessed. Our results evidence the capacity of SVM to determine the oxidation state of iron and manganese in iron and manganese oxides, based on the ELNES of the white lines of the transition metal. The SVM algorithm is first trained with given datasets and then the resulting models are tested through noisy test data sets. We demonstrate that SVM exhibits a very good performance classifying these EEL spectra, despite the usual level of noise and instrumental energy shifts.