zhpsЖурнал прикладной спектроскопииZhurnal Prikladnoii Spektroskopii0514-7506B. I. Stepanov Institute of Physics of the National Academy of Scienceszhps-1425Research ArticleАННОТАЦИИ АНГЛОЯЗЫЧНЫХ СТАТЕЙABSTRACTS ENGLISH-LANGUAGE ARTICLESИспользование машинного обучения на базе лазерно-искровой эмиссионной спектроскопии для проведения судебно-медицинского атрибутивного анализа продуктов деления в суррогатных высокоактивных ядерных отходахExploring Machine-Learning-Enabled LIBS Towards Forensic Trace Attributive Analysis of Fission Products in Surrogate High-Level Nuclear WasteOnkangiJ. N.OnkangiJ. N.

Найроби

Joshua Nyairo Onkangi

Department of Physics

Nairobi 

 

joshuanyairo@students.uonbi.ac.keAngeyoH. K.AngeyoH. K.

Найроби

Hudson Kalambuka Angeyo

Department of Physics

Nairobi 

Университет НайробиUniversity of Nairobi202325112023906965965Copyright © Onkangi J.N., Angeyo H.K., 20232023Onkangi J.N., Angeyo H.K.Onkangi J.N., Angeyo H.K.Данная работа распространяется под лицензией Creative Commons Attribution 4.0.This work is licensed under a Creative Commons Attribution 4.0 License.https://zhps.ejournal.by/jour/article/view/1425

Исследована применимость лазерно-искровой эмиссионной спектроскопии и машинного обучения для прямого быстрого анализа отдельных продуктов деления (FP), а именно Y, Sr, Rb и Zr, в суррогатных высокоактивных ядерных отходах (постдетонационные обломки стекла, постдетонационные порошки и капли жидкости (в мкл) с места радиологического воздействия (RCS)). Разработаны стратегии калибровки искусственной нейронной сети для количественного анализа следов FP в этих материалах, которые позволили достичь прогнозирования >95 % для образцов всех типов. Из-за отсутствия соответствующих сертифицированных стандартных образцов для валидации метода использованы синтетические стандартные образцы с точностью ~91 %. На основании спектральных характеристик FP с помощью анализа главных компонент дифференцированы ядерные и неядерные отходы, продемонстрирован потенциал метода для ядерной судебной экспертизы и атрибутивного анализа RCS.

We investigated the utility of machine-learning-enabled LIBS for direct rapid analysis of selected fission products (FPs), namely Y, Sr, Rb, and Zr in surrogate high-level nuclear waste mimicking three hypothetical but realistic scenarios: post-detonation glass debris, post-detonation powders, and microliter liquid drops from a radiological crime scene (RCS). Artificial neural network calibration strategies for trace quantitative analysis of the FPs in these materials were developed and achieved >95% prediction for all sample types. Owing to a lack of appropriate certified reference materials synthetic reference standards materials were used to perform method validation to accuracies ˃91%. Based on the spectral responses of the FPs, principal component analysis successfully differentiated nuclear from non-nuclear waste, demonstrating the method’s potential for RCS nuclear forensic and attributive analysis.

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The authors declare that there are no conflicts of interest present.