zhpsЖурнал прикладной спектроскопииZhurnal Prikladnoii Spektroskopii0514-7506B. I. Stepanov Institute of Physics of the National Academy of Scienceszhps-1307Research ArticleАННОТАЦИИ АНГЛОЯЗЫЧНЫХ СТАТЕЙABSTRACTS ENGLISH-LANGUAGE ARTICLESГибридные алгоритмы машинного обучения для прогнозирования количественного состава алюминиевых сплавов методом лазерно-искровой эмиссионной спектроскопииDifferent Hybrid Prediction’s Machine Learning Algorithms for Quantitative Analysis in Laser-Induced Breakdown SpectroscopyRezaeiM.RezaeiM.

Rezaei Mohsen.

Бехшахр

Mohsen Rezaei.

Behshahr

RezaeiF.RezaeiF.

Rezaei Fatemeh.

Тегеран

Fatemeh Rezaei.

Tehran

fatemehrezaei@kntu.ac.irKarimiP.KarimiP.

Karimi Parvin.

Тегеран

Parvin Karimi.

Tehran

Университет науки и технологий МазандаранаDepartment of Industrial Engineering, University of Science and Technology of MazandaranТехнологический университет K.N. ToosiDepartment of Physics, K.N. Toosi University of TechnologyЮжно-Тегеранский филиал Исламского университета АзадDepartment of Physics, South Tehran Branch, Islamic Azad University20233105202390352812Copyright © Rezaei M., Rezaei F., Karimi P., 20232023Rezaei M., Rezaei F., Karimi P.Rezaei M., Rezaei F., Karimi P.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/1307

Проведен количественный анализ образцов алюминия с помощью лазерно-искровой эмиссионной спектроскопии (LIBS) и различных классических подходов к машинному обучению. Nd:YAG-лазер с модулятором добротности на основной гармонике 1064 нм используется для создания плазмы LIBS Для прогнозирования концентраций составляющих стандартных алюминиевых сплавов. Прогнозирование концентрации выполнено с помощью линейных подходов регрессии опорных векторов (SVR), множественной линейной регрессии (MLR), анализа главных компонент (PCA), интегрированного с MLR (PCA-MLR) и SVR (PCA-SVR), а также нелинейных алгоритмов искусственной нейронной сети (ANN), ядерной регрессии опорных векторов (KSVR) и объединения традиционного анализа главных компонент с KSVR (РСА-KSVR) и ANN (РСА-ANN). Для улучшения количественного анализа применяется уменьшение размерности к различным методологиям на основе алгоритма PCA. Комбинация PCA-KSVR показывает наибольшую эффективность в прогнозировании большинства элементов среди других классических алгоритмов машинного обучения.

Laser-induced breakdown spectroscopy (LIBS) technique is employed for quantitative analysis of aluminum samples by different classical machine learning approaches. A Q-switch Nd:YAG laser at a fundamental harmonic of 1064 nm is utilized for the creation of LIBS plasma in order to predict constituent concentrations of the aluminum standard alloys. In the current research, concentration prediction is performed by linear approaches of support vector regression (SVR), multiple linear regression (MLR), principal component analysis (PCA) integrated with MLR (PCA-MLR), and SVR (PCA-SVR), as well as nonlinear algorithms of artificial neural network (ANN), kernelized support vector regression (KSVR), and the integration of traditional principal component analysis with KSVR (PCA-KSVR), and ANN (PCA-ANN). Furthermore, dimension reduction is applied to various methodologies by the PCA algorithm in order to improve the quantitative analysis. The results indicated that the combination of PCA with the KSVR algorithm model had the best efficiency in predicting most of the elements among other classical machine learning algorithms.

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