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<p>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.</p>

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

  • 期刊论文

  • 发表日期：

    2025年1月29日

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  • Hadi Shahabinejad,
  • Davorin Sudac,
  • Karlo Nad,
  • Isabelle Espagnon,
  • Clotilde de Sainte Foy,
  • Bertrand Perot,
  • Cedric Carasco,
  • Alix Sardet,
  • Edwin Friedmann,
  • Jean Philippe Poli,
  • Jessica Delgado,
  • Felix Pino,
  • Sandra Moretto,
  • Christine Mer,
  • Guillaume Sannie,
  • Jasmina Obhodas

摘要

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.