|Laser-Induced Breakdown Spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination
|Year of Publication
|Gyftokostas, N, Stefas, D, Kokkinos, V, Bouras, C, Couris, S
|Scientific Reports, Nature Research Journals
Olive oil is a basic element of the Mediterranean diet and a key product for the economies of the Mediterranean countries. Thus, there is an added incentive in the olive oil business for fraud through practices like adulteration and mislabeling. In the present work, Laser Induced Breakdown Spectroscopy (LIBS) assisted by machine learning is used for the classification of 139 virgin olive oils in terms of their geographical origin. The LIBS spectra of these olive oil samples were used to train different machine learning algorithms, namely LDA, ERTC, RFC, XGBoost, and to assess their classification performance. In addition, the variable importance of the spectral features was calculated, for the identification of the most important ones for the classification performance and to reduce their number for the algorithmic training. The algorithmic training was evaluated and tested by means of classification reports, confusion matrices and by external validation procedure as well. The present results demonstrate that machine learning aided LIBS can be a powerful and efficient tool for the rapid authentication of the geographic origin of virgin olive oil.
Laser-Induced Breakdown Spectroscopy coupled with machine learning as a tool for olive oil authenticity and geographic discrimination