# PGNAA Spectral Classification of Metal with Density Estimations

arXiv: https://arxiv.org/abs/2208.13836 · HTML: https://mlsanity.com/paper/2208.13836/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 33 references that are in the map.
- First posted on arXiv 29 Aug 2022.

## Builds on (most cited first, 6 of 6)

- [Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution](https://mlsanity.com/paper/1708.09022/) (2017)
- [Deep learning-based statistical noise reduction for multidimensional spectral data](https://mlsanity.com/paper/2107.00844/) (2021)
- [Semi-supervised on-device neural network adaptation for remote and portable laser-induced breakdown spectroscopy](https://mlsanity.com/paper/2104.03439/) (2021)
- [Unsupervised Machine Learning for Exploratory Data Analysis of Exoplanet Transmission Spectra](https://mlsanity.com/paper/2201.02696/) (2022)
- [Convolutional neural networks for classification and regression analysis of one-dimensional spectral data](https://mlsanity.com/paper/2005.07530/) (2020)
- [Raman spectroscopy in open world learning settings using the Objectosphere approach](https://mlsanity.com/paper/2111.06268/) (2021)

## Built on by (most cited first, 1 of 1)

- [PGNAA Spectral Classification of Aluminium and Copper Alloys with Machine Learning](https://mlsanity.com/paper/2404.14107/) (2024)

## Abstract (arXiv)

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.