zhpsЖурнал прикладной спектроскопииZhurnal Prikladnoii Spektroskopii0514-7506B. I. Stepanov Institute of Physics of the National Academy of Scienceszhps-1537Research ArticleАННОТАЦИИ АНГЛОЯЗЫЧНЫХ СТАТЕЙABSTRACTS ENGLISH-LANGUAGE ARTICLESБыстрая идентификация и классификация металлических отходов с помощью лазерно-искровой эмиссионной спектроскопииRapid Identification and Classification of Metal Waste by Laser-Induced Breakdown SpectroscopyZhouZ.ZhouZ.

Нанкин

Nanjing

GaoW.GaoW.

Нанкин

Nanjing

JamaliS.JamaliS.

Джамшоро

Jamshoro

YuC.YuC.

Нанкин

Nanjing

LiuY.LiuY.

Нанкин

Nanjing

yuzhu.liu@gmail.comСовместный инновационный центр Цзянсу по технологиям атмосферной среды и оборудования (CICAEET), Нанкинский университет информационных наук и технологийJiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & TechnologyИнститут физики Университета СиндаInstitute of Physics, University of SindhСовместный инновационный центр Цзянсу по технологиям атмосферной среды и оборудования (CICAEET), Нанкинский университет информационных наук и технологий; Международная совместная лаборатория Цзянсу по метеорологической фотонике и оптоэлектронному обнаружению, Нанкинский университет информационных наук и технологийJiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, Jiangsu Collaborative Innovation Center on Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science & Technology; Jiangsu International Joint Laboratory on Meteorological Photonics and Optoelectronic Detection, Nanjing University of Information Science & Technology202429042024912310310Copyright © Zhou Z., Gao W., Jamali S., Yu C., Liu Y., 20242024Zhou Z., Gao W., Jamali S., Yu C., Liu Y.Zhou Z., Gao W., Jamali S., Yu C., Liu Y.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/1537

Предложен метод экспресс-идентификации и классификации металлического мусора (на примере пяти металлов) на основе совместного использования лазерно-искровой эмиссионной спектроскопии и машинного обучения. Идентифицировано несколько характерных линий Al, W, Fe, Cu, Sn, Pb и C. Для более эффективной классификации проведен анализ главных компонент с целью уменьшения размерности спектров. После уменьшения размерности образцы классифицированы с точностью 97.18% с использованием метода K-ближайших соседей. Для количественного анализа создана математическая модель из линейных формул, связывающих спектр и концентрацию на примере Fe, что заложило основу для более точной классификации.

The rapid identification and classification of metal garbage has been experimentally investigated. By combining laser-induced breakdown spectroscopy (LIBS) and machine learning, metal garbage can be effectively identified through spectral analysis. In this work, a novel method for metal garbage classification was developed, and a LIBS system was self-developed. As an example of metal recycling, five types of metal were adopted. Several characteristic lines of Al, W, Fe, Cu, Sn, Pb, and C were identified. For a more effective classification, principal component analysis was conducted to reduce the dimension of the spectra. Samples after the dimension reduction were classified by using K-nearest neighbors, and five types were obtained, exhibiting a final classification accuracy of 97.18%. Moreover, a mathematical model of the linear formulas between spectrum and concentration was established to achieve quantitative analysis with Fe taken as an example, laying the foundation for more refined classification.

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