Nuclear Forensic Analysis via Machine Learning Assisted Laser-based Spectroscopy and Spectral Imaging
Abstract
Nuclear forensics (NF) is a systematic and scientific methodology designed to identify,
categorize, and characterize seized nuclear and radiological materials (NRM). The aim of NF
is to reveal the geographical origin, process/production history, age and intended use of the
NRM to prevent future diversions and thefts, thereby strengthening the national security of a
country. The complexity of the signatures utilizing the existing methods poses an analytical and
interpretation challenge. Hence, the need to develop rapid, non-invasive and non-destructive
techniques to speed up NF investigations. Laser Based Induced Breakdown Spectroscopy
(LIBS) fingerprints the elements associated with the spectral peaks, while Laser Raman
microspectrometry (LRM) uniquely identifies specific chemical compounds and
microstructures in a sample based on molecular vibrations. Although these methods have high
accuracy and versatility following little or no sample preparation, their practical utility is
limited due to the complexity of the samples and the interpretative challenges of multivariate
data. Machine learning (ML) techniques can overcome these limitations and help analyse this
complicated and bulky data. LIBS and LRM combined with ML possess the power to conduct
direct, rapid NF analysis of limited size NRM with accuracy and precision. The uranium lines
at 386.592 nm, 385.957 nm and 385.464 nm were identified as NF signatures of uranium in
cellulose and uranium ore surrogates (uranium mineral ores and high background soil samples).
The detection limit for uranium in cellulose was determined at 76 ppm. Multivariate calibration
models in artificial neural network (back-propagation algorithm) were developed using
resonant and weak uranium lines. The calibration model using weak U-lines predicted the
uranium concentration in the certified reference material (CRM) RGUMix (101 ppm) and
RGU-1 (400 ppm) at relative error of prediction (REP) = 2.97% and 2.25% respectively, while
using resonant U-lines at REP = 69.31% and 4.25%, respectively. The calibration model
utilizing weak U-lines predicted the uranium content in the uranium mineral ores in the range
of (112 - 1000) ppm. Application of principal component analysis (PCA) on the complete LIBS
spectra of uranium ore surrogates revealed patterns that were related to their origin. PCA
applied to selective spectral regions of uranium mineral ores successfully grouped them into
their mineral mines (origin). NF signatures associated with uranium molecules in uranyl nitrate,
uranyl sulphate, uranyl chloride and uranium trioxide samples were identified at 865 cm-1, 868
cm-1, 861 cm-1, and 848 cm-1respectively using LRM (laser λ= 532 nm, 785 nm). Spectral imaging
on simulate samples of uranium and uranium ore surrogates using NF signatures demonstrated
the distribution of uranium molecules. Thus, ML assisted laser-based spectroscopy and spectral
imaging, have the potential to not only perform rapid, direct and minimally intrusive qualitative
and quantitative analysis of trace uranium, but also aid in the source attribution of uranium ore
surrogates and distribution of uranium molecules.
Publisher
University of Nairobi
Rights
Attribution-NonCommercial-NoDerivs 3.0 United StatesUsage Rights
http://creativecommons.org/licenses/by-nc-nd/3.0/us/Collections
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