Classification of olive cultivars by machine learning based on olive oil chemical composition

Extra virgin olive oil traceability and authenticity are important quality indicators, and are currently the subject of exhaustive research, for developing methods to secure olive oil origin-related issues. The aim of this study was the development of a classification model capable of olive cultivar identification based on olive oil chemical composition. To achieve our aim, 385 samples of two Greek and three Italian olive cultivars were collected during two successive crop years from different locations in the coastline part of western Greece and southern Italy and analyzed for their chemical characteristics. Principal Component Analysis showed trends of differentiation among olive cultivars within or between the crop years. Artificial intelligence model of the XGBoost machine learning algorithm showed high performance in classifying the five olive cultivars from the pooled samples.

Automated summary

The aim of this study was to develop a classification model based on the chemical composition of olive oil for the identification of olive cultivars. Analysis of 385 samples from two Greek and three Italian cultivars showed differentiation trends among cultivars within or between crop years. The XGBoost machine learning algorithm showed high performance in classifying the five olive cultivars from the pooled samples.