  In this paper, we explore the optimization of metal recycling with a focus on
real-time differentiation between alloys of copper and aluminium. Spectral
data, obtained through Prompt Gamma Neutron Activation Analysis (PGNAA), is
utilized for classification. The study compares data from two detectors, cerium
bromide (CeBr$_{3}$) and high purity germanium (HPGe), considering their energy
resolution and sensitivity. We test various data generation, preprocessing, and
classification methods, with Maximum Likelihood Classifier (MLC) and
Conditional Variational Autoencoder (CVAE) yielding the best results. The study
also highlights the impact of different detector types on classification
accuracy, with CeBr$_{3}$ excelling in short measurement times and HPGe
performing better in longer durations. The findings suggest the importance of
selecting the appropriate detector and methodology based on specific
application requirements.
