Classification of olive cultivars by machine learning based on olive oil chemical composition.
Artificial intelligent models
Authenticity
Chemical composition
Cultivar classification
Machine learning
Olive oil
Journal
Food chemistry
ISSN: 1873-7072
Titre abrégé: Food Chem
Pays: England
ID NLM: 7702639
Informations de publication
Date de publication:
15 Dec 2023
15 Dec 2023
Historique:
received:
30
03
2023
revised:
15
06
2023
accepted:
01
07
2023
medline:
24
8
2023
pubmed:
3
8
2023
entrez:
3
8
2023
Statut:
ppublish
Résumé
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.
Identifiants
pubmed: 37535989
pii: S0308-8146(23)01411-5
doi: 10.1016/j.foodchem.2023.136793
pii:
doi:
Substances chimiques
Olive Oil
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
136793Informations de copyright
Copyright © 2023 Elsevier Ltd. All rights reserved.
Déclaration de conflit d'intérêts
Declaration of Competing Interest 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.