# Olive Oils Classification via Laser-Induced Breakdown Spectroscopy
## 4 Conclusions  
In the present work, the LIBS technique combined with some of the most popular, open source, machine learning algorithms were used with great success for the discrimination/classification of some olive oils in terms of their geographical origin. Classification accuracies exceeding 90% were attained, suggesting the potential of this approach. In addition to the discrimination/classification issues, the effect of pre-processing of the LIBS spectroscopic data on the accuracy of the different predictive models was also investigated. It was found that pre-processing of the LIBS spectroscopic data, e.g., with the PCA algorithm, can improve the accuracy of some predictive models, e.g., the LDA one, while leaving unaffected the classification accuracies of k-NN and SVC models, despite the dimensionality reduction achieved. In fact, the pre-processing of the LIBS spectroscopic data with PCA has a great impact on the LDA predictive model accuracy, this last attaining a value up to \((94.0\pm 1.1)\%\). Overall, it can be concluded that pre-processing of the LIBS spectroscopic data by the PCA algorithm can be an effective method for minimization of the training time while, in some cases, it can considerably enhance the obtained accuracies.  
In addition, it was found that the most suitable and efficient machine learning models, in the case of such LIBS spectroscopic data sets, seem to be the linear ones. This qualitative finding is of great importance, because the selection of a suitable predictive model is not an easy task while, in general, there is no \"rule of thumb\", indicating what kind of algorithms are best suited for a specific task. Concluding, based on the great results obtained so far from LIBS measurements, and for a relatively small geographical region, i.e., the island of Crete, it can be expected that the next step, would be the validation of the presented approach on a larger geographical scale, which would include more regions and even more countries.  
The following are available online at [[http://www.mdpi.com/2076-3417/10/10/3462/s1](http://www.mdpi.com/2076-3417/10/10/3462/s1)]([http://www.mdpi.com/2076-3417/10/10/3462/s1](http://www.mdpi.com/2076-3417/10/10/3462/s1)), Figure S1: LIBS experimental setup, Figure S2: Panorama of the obtained spectra of the 36 olive oil samples, Table S1: Information about the samples, Table S2: Classification reports for (**a**) k-NN on raw data, (**b**) k-NN on preprocessed data, Table S3: Classification reports for (**a**) SVC on raw data, (**b**) SVC on preprocessed data, Table S4: Classification reports for (**a**) LDA on raw data, (**b**) LDA on preprocessed data.  
N.G. and D.S.: data curation; S.C.: funding acquisition; N.G. and D.S.: investigation; S.C.: methodology; S.C.: project administration; S.C.: resources; N.G.: software; S.C.: supervision; N.G. and D.S.: writing-original draft; S.C.: writing-review and editing. All authors have read and agreed to the published version of the manuscript.  
This research has been partially supported by Greek national funds through the Public Investments Program (PIP) of General Secretariat for Research and Technology (GSRT), under the Emblematic Action \"The Olive Road\" (project code: 2018\(\Sigma\)E01300000) and by the project \"HELLAS-CH\" (MIS 5002735) in the frame of <<Synergistic Action \"ELI-LASERLAB Europe, HiPER & IPERION-CH.gr\", which is implemented under the Action \"Reinforcement of the Research and Innovation Infrastructure\", funded by the Operational Program \"Competitiveness, Entrepreneurship and Innovation\" (NSRF 2014-2020) and co-financed by Greece and the European Union (European Regional Development Fund).  
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.  
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