PGNAA Spectral Classification of Metal with Density Estimations

TL;DR

This paper introduces a novel spectral classification method using PGNAA for non-destructive online analysis of metals, achieving rapid and accurate identification of aluminium alloys despite noisy data.

Contribution

It proposes a new approach to classify spectral data as probability distributions using kernel density estimation, enabling fast and robust metal classification.

Findings

Near perfect classification of aluminium alloys within 0.25 seconds

Effective handling of noisy, short-term measurement data

Potential for real-time, non-destructive metal analysis

Abstract

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.