Yılmaz, Vadi SuYilmaz, Vadi SuEseller, Kemal EfeAslan, ÖzgürAslan, OzgurBayraktar, EminEseller, Kemal EfeElectrical-Electronics EngineeringMechanical EngineeringDepartment of Electrical & Electronics Engineering2024-07-052024-07-05202302411-513410.3390/inventions80200542-s2.0-85153715175https://doi.org/10.3390/inventions8020054https://hdl.handle.net/20.500.14411/2513aslan, ozgur/0000-0002-1042-0805This paper aims toward the successful detection of harmful materials in a substance by integrating machine learning (ML) into laser-induced breakdown spectroscopy (LIBS). LIBS is used to distinguish five different synthetic polymers where eight different heavy material contents are also detected by LIBS. Each material intensity-wavelength graph is obtained and the dataset is constructed for classification by a machine learning (ML) algorithm. Seven popular machine learning algorithms are applied to the dataset which include eight different substances with their wavelength-intensity value. Machine learning algorithms are used to train the dataset, results are discussed and which classification algorithm is appropriate for this dataset is determined.eninfo:eu-repo/semantics/openAccessLIBSrubber-polymershardnessmachine learningclassificationArticle82WOS:000978185800001