Discrimination of olive oils based on the olive cultivar origin by machine learning employing the fusion of emission and absorption spectroscopic data

Abstract In this work Laser-Induced Breakdown Spectroscopy (LIBS) and absorption spectroscopy aided by machine learning are employed for discriminating some extra virgin Greek olive oils of different olive cultivars for the first time. LIBS and absorption spectra of extra virgin olive oils belonging to Kolovi and Koroneiki cultivars, as well as mixtures of them, were collected, analyzed, and used to develop classification schemes employing Linear Discriminant Analysis and Gradient Boosting, the latter allowing the determination of the most important spectral features. Both algorithms were found to provide efficient classification of the olive oil spectra with accuracies exceeding 90%. Furthermore, for the first time, the emission spectra of LIBS were fused with the absorption spectra to create predictive models and their accuracies were found to be significantly improved. This work demonstrates the enhanced capabilities of LIBS and absorption spectroscopy and the potential of their combination for olive oil quality monitoring and control.