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Title: Application of Multivariate Data Analysis Techniques for the Portable Isotopic Neutron Spectroscopy system

Abstract

The Portable Isotopic Neutron Spectroscopy (PINS) is a commercialized system developed by Idaho National Laboratory (INL) to examine chemical compounds non-destructively, utilizing Prompt Gamma Neutron Activation Analysis (PGNAA) techniques. The PINS systems have been successfully deployed around the world to identify chemical munitions and containers. The PINS system takes advantage of a high-resolution gamma-ray spectrum from a high-purity germanium (HPGe) detector, and gamma-ray peak analysis provides input to its chemical identification logic with the probabilistic decision tree (PDT). Gamma-ray peak analyses, however, require knowledge of pre-selected gamma-ray peaks whose energies, intensities, and origins are well studied, and some peaks might be excluded from the decision making processes due to lack of current nuclear data. In contrast, Multivariate Analysis (MVA) treats a whole gamma-ray spectrum as a collection of multiple variables or a pattern. The effectiveness of chemical identification algorithm is determined by the availability of a wide range of data to train an algorithm to identify chemical fills with accuracy. INL has a collection of gamma-ray spectra of various chemical-fills from the field-deployed PINS systems over the years, and it was envisaged to combine such a database with the principle of multivariate statistics. Therefore, in parallel with the current decisionmore » tree algorithm, an MVA-based chemical identification algorithm was developed as an independent verification of the current results. The Principal Component Analysis (PCA) method was adopted to build an MVA-based identification algorithm, using the collected field data to refine and validate the algorithm. A benchmarking study of the MVA-based algorithm’s performance was conducted alongside the current algorithm, with the results presented and discussed in this study.« less

Authors:
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [1];  [1];  [1]; ORCiD logo [1]
  1. Idaho National Laboratory
Publication Date:
Research Org.:
Idaho National Lab. (INL), Idaho Falls, ID (United States)
Sponsoring Org.:
USDOE Office of Nuclear Energy (NE)
OSTI Identifier:
1566066
Report Number(s):
INL/CON-17-41836-Rev000
DOE Contract Number:  
DE-AC07-05ID14517
Resource Type:
Conference
Resource Relation:
Conference: 2017 IEEE Nuclear Science Symposium and Medical Imaging Conference, Atlanta, GA, 10/21/2017 - 10/28/2017
Country of Publication:
United States
Language:
English
Subject:
46 - INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND TECHNOLOGY; 97 - MATHEMATICS AND COMPUTING; 73 - NUCLEAR PHYSICS AND RADIATION PHYSICS; Portable Isotopic Neutron Spectroscopy; Prompt Gamma Neutron Activation Analysis; Gamma-ray spectroscopy; Multivariate Data Analysis; Chemical Warfare Agents; Principal Component Analysis; HPGe detector; Neutron Activation Analysis; Chemical Identification

Citation Formats

Lee, D., Wharton, J., Bucher, B., Caffrey, A., Krebs, K., and Seabury, E. Application of Multivariate Data Analysis Techniques for the Portable Isotopic Neutron Spectroscopy system. United States: N. p., 2018. Web. doi:10.1109/NSSMIC.2017.8533106.
Lee, D., Wharton, J., Bucher, B., Caffrey, A., Krebs, K., & Seabury, E. Application of Multivariate Data Analysis Techniques for the Portable Isotopic Neutron Spectroscopy system. United States. doi:10.1109/NSSMIC.2017.8533106.
Lee, D., Wharton, J., Bucher, B., Caffrey, A., Krebs, K., and Seabury, E. Thu . "Application of Multivariate Data Analysis Techniques for the Portable Isotopic Neutron Spectroscopy system". United States. doi:10.1109/NSSMIC.2017.8533106. https://www.osti.gov/servlets/purl/1566066.
@article{osti_1566066,
title = {Application of Multivariate Data Analysis Techniques for the Portable Isotopic Neutron Spectroscopy system},
author = {Lee, D. and Wharton, J. and Bucher, B. and Caffrey, A. and Krebs, K. and Seabury, E.},
abstractNote = {The Portable Isotopic Neutron Spectroscopy (PINS) is a commercialized system developed by Idaho National Laboratory (INL) to examine chemical compounds non-destructively, utilizing Prompt Gamma Neutron Activation Analysis (PGNAA) techniques. The PINS systems have been successfully deployed around the world to identify chemical munitions and containers. The PINS system takes advantage of a high-resolution gamma-ray spectrum from a high-purity germanium (HPGe) detector, and gamma-ray peak analysis provides input to its chemical identification logic with the probabilistic decision tree (PDT). Gamma-ray peak analyses, however, require knowledge of pre-selected gamma-ray peaks whose energies, intensities, and origins are well studied, and some peaks might be excluded from the decision making processes due to lack of current nuclear data. In contrast, Multivariate Analysis (MVA) treats a whole gamma-ray spectrum as a collection of multiple variables or a pattern. The effectiveness of chemical identification algorithm is determined by the availability of a wide range of data to train an algorithm to identify chemical fills with accuracy. INL has a collection of gamma-ray spectra of various chemical-fills from the field-deployed PINS systems over the years, and it was envisaged to combine such a database with the principle of multivariate statistics. Therefore, in parallel with the current decision tree algorithm, an MVA-based chemical identification algorithm was developed as an independent verification of the current results. The Principal Component Analysis (PCA) method was adopted to build an MVA-based identification algorithm, using the collected field data to refine and validate the algorithm. A benchmarking study of the MVA-based algorithm’s performance was conducted alongside the current algorithm, with the results presented and discussed in this study.},
doi = {10.1109/NSSMIC.2017.8533106},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2018},
month = {11}
}

Conference:
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