Journal
FOOD CHEMISTRY
Volume 429, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.foodchem.2023.136793
Keywords
Olive oil; Chemical composition; Cultivar classification; Authenticity; Artificial intelligent models; Machine learning
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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.
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.
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