  For environmental, sustainable economic and political reasons, recycling
processes are becoming increasingly important, aiming at a much higher use of
secondary raw materials. Currently, for the copper and aluminium industries, no
method for the non-destructive online analysis of heterogeneous materials are
available. The Prompt Gamma Neutron Activation Analysis (PGNAA) has the
potential to overcome this challenge. A difficulty when using PGNAA for online
classification arises from the small amount of noisy data, due to short-term
measurements. In this case, classical evaluation methods using detailed peak by
peak analysis fail. Therefore, we propose to view spectral data as probability
distributions. Then, we can classify material using maximum log-likelihood with
respect to kernel density estimation and use discrete sampling to optimize
hyperparameters. For measurements of pure aluminium alloys we achieve near
perfect classification of aluminium alloys under 0.25 second.

Machine Learning;Materials Science