TY - JOUR T1 - Classification of Greek olive from different regions by machine learning - aided Laser - Induced Breakdown Spectroscopy and Absorption Spectroscopy JF - Open Access Journal, MDPI, Special Issue Characterization of Olive Products from Greece Y1 - 2021 A1 - Nikolaos Gyftokostas A1 - Eleni Nanou A1 - Dimitrios Stefas A1 - Vasileios Kokkinos A1 - Christos Bouras A1 - Stelios Couris AB - In the present work, the emission and the absorption spectra of numerous Greek olive oil samples and mixtures of them, obtained by two spectroscopic techniques, namely Laser-Induced Breakdown Spectroscopy (LIBS) and Absorption Spectroscopy, and aided by machine learning algorithms, were employed for the discrimination/classification of olive oils regarding their geographical origin. Both emission and absorption spectra were initially preprocessed by means of Principal Component Analysis (PCA) and were subsequently used for the construction of predictive models, employing Linear Discriminant Analysis (LDA) and Support Vector Machines (SVM). All data analysis methodologies were validated by both “k-fold” cross-validation and external validation methods. In all cases, very high classification accuracies were found, up to 100%. The present results demonstrate the advantages of machine learning implementation for improving the capabilities of these spectroscopic techniques as tools for efficient olive oil quality monitoring and control. VL - 26 IS - 5 ER -