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.

In the present work, laser-induced breakdown spectroscopy, aided by some machine learning algorithms (i.e., linear discriminant analysis (LDA) and extremely randomized trees (ERT)), is used for the detection of honey adulteration with glucose syrup. In addition, it is shown that instead of the entire LIBS spectrum, the spectral lines of inorganic ingredients of honey (i.e., calcium, sodium, and potassium) can be also used for the detection of adulteration providing efficient discrimination. The constructed predictive models attained high classification accuracies exceeding 90% correct classification.