Elemental identification of sealed cargo based on fast neutron activation analysis and artificial neural network

Abstract

Securing global trade requires efficient screening of containers for threat materials. This work demonstrates a novel approach combining fast neutron activation analysis and artificial neural network (ANN) to identify elemental composition of sealed cargo, in particular elements carbon, oxygen, and nitrogen, which are the main components of explosives. This study shows that Rapidly Relocatable Tagged Neutron Inspection System in combination with ANN is a potential promising solution for the inspection of sealed containers, allowing precise identification of elements and detection of potential threats without the need to open the containers.