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基于橄榄油化学成分的机器学习对橄榄品种的分类。

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

机构信息

Institute of Olive Tree, Subtropical Crops and Viticulture, Hellenic Agricultural Organization-DEMETER, 24100 Kalamata, Greece.

Department of Pharmacy, University of Patras, Rio Patras 26504 Patras, Greece.

出版信息

Food Chem. 2023 Dec 15;429:136793. doi: 10.1016/j.foodchem.2023.136793. Epub 2023 Jul 3.

DOI:10.1016/j.foodchem.2023.136793
PMID:37535989
Abstract

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.

摘要

特级初榨橄榄油的可追溯性和真实性是重要的质量指标,目前正针对开发方法以确保橄榄油产地相关问题进行详尽的研究。本研究的目的是开发一种能够基于橄榄油化学成分进行橄榄品种识别的分类模型。为了实现我们的目标,在连续两年的作物季节中,从希腊西部和意大利南部沿海地区的不同地点采集了 385 个两个希腊品种和三个意大利品种的橄榄样本,并对其化学特性进行了分析。主成分分析显示,在作物年份内或之间,橄榄品种之间存在分化趋势。基于 XGBoost 机器学习算法的人工智能模型在对混合样本中的五个橄榄品种进行分类方面表现出了很高的性能。

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