Incorporating Random Tree Statistical Learning Classifier to Authenticate PDO Kalamata Olive Oil Blended with Aigialeia Olive Oil from the Geographic Region of Peloponnese in Greece

Authentication of Protected Designation of Origin (PDO) Kalamata olive oil is required to assess its quality in the marketplace compared with other olive oil varieties. Concretely, Kalamata is located in southern Greece in the geographic county of Messenia, which is part of the geographic region of Peloponnese and is famous for its extra virgin olive oil produced from the Koroneiki olive variety. Intuitively, PDO Kalamata olive oil, established by Council regulation (EC) No 510/2006, owes its quality and special characteristics to the geographical environment, olive tree variety, and human factor. However, authentication of the PDO Kalamata olive oil is a challenging task when it is blended with other olive oil varieties, such as the Aigialeia olive oil variety that is cultivated in the geographic county of Achaia, which is also located in the geographic region of Peloponnese. Subsequently, the PDO Kalamata olive oil authentication process is achieved by adopting the potentiality of certain statistical machine learning models. Specifically, in this paper, a random tree classification model to authenticate PDO Kalamata olive oil when it is blended with olive oil from Aigialeia. Concretely, the adopted classification model authenticates the quality of the PDO Kalamata olive oil variety based on synchronous excitation-emission fluorescence (SyEE) spectroscopy applied to certain olive oil data samples. Experiments performed evaluate the efficiency of the adopted random tree statistical learning classifier. Intuitively, the observed results promise to define the originality and authentication of the PDO Kalamata extra virgin olive oil by exploiting its unique quality characteristics.