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Article Citation - WoS: 27Citation - Scopus: 36Determination of Ca Addition To the Wheat Flour by Using Laser-Induced Breakdown Spectroscopy (libs)(Springer, 2016) Bilge, Gonca; Sezer, Banu; Eseller, Kemal Efe; Berberoglu, Halil; Koksel, Hamit; Boyaci, Ismail HakkiThe aim of the study was to determine Ca addition to the flour by using laser-induced breakdown spectroscopy (LIBS) as a quick and simple multi-elemental spectroscopy method. Different amounts of CaCO3-added wheat flour were analyzed using LIBS to determine Ca content and Ca/K ratio, which is used for discrimination of natural and Ca-added flour. LIBS spectra were quantitatively evaluated with partial least square (PLS) method as a multivariate data analysis method to eliminate the matrix effect. Ca and Ca/K calibration graphs of PLS method showed good linearity with coefficient of determinations (R (2)) 0.999. Limit of detection values for Ca and Ca/K analysis were calculated as 25.9 ppm and 0.013, respectively. Furthermore, the results were found to be consistent with the data obtained from atomic absorption spectroscopy method as a reference method for flour samples.Article Citation - WoS: 2Citation - Scopus: 2Classification of Different Recycled Rubber-Epoxy Composite Based on Their Hardness Using Laser-Induced Breakdown Spectroscopy (libs) With Comparison Machine Learning Algorithms(Mdpi, 2023) Yilmaz, Vadi Su; Yılmaz, Vadi Su; Eseller, Kemal Efe; Aslan, Ozgur; Aslan, Özgür; Bayraktar, Emin; Eseller, Kemal Efe; Yılmaz, Vadi Su; Aslan, Özgür; Eseller, Kemal Efe; Electrical-Electronics Engineering; Department of Electrical & Electronics Engineering; Mechanical Engineering; Electrical-Electronics Engineering; Mechanical Engineering; Department of Electrical & Electronics EngineeringThis 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.
