01788nas a2200169   4500008004100000245015200041210006900193490000700262520105700269100002601326700001701352700002201369700002401391700002101415700002001436856016201456       2021                        eng d00aClassification of Greek olive from different regions by machine learning - aided Laser - Induced Breakdown Spectroscopy and Absorption Spectroscopy0 aClassification of Greek olive from different regions by machine 0 v263 aIn 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.1 aGyftokostas, Nikolaos1 aNanou, Eleni1 aStefas, Dimitrios1 aKokkinos, Vasileios1 aBouras, Christos1 aCouris, Stelios  uhttps://telematics.upatras.gr/telematics/publications/classification-greek-olive-different-regions-machine-learning-aided-laser-induced-breakdown?language=en