希腊"Gigantes Elefantes"豆的多元素分析和地理歧视,使用诱导合等离子体质谱和机器学习模型

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Eleni C Mazarakioti¹, Anastasios Zotos², Vassilios S Verykios³

¹Department of Food Science and Technology, University of Patras, 30131 Agrinio, Greece.

Foods (Basel, Switzerland)

|September 28, 2024

概括

这项研究使用多元元素分析和决策树算法来验证希腊巨型豆的真实性. 该方法准确地区分了来自不同地区的豆类,打击食品欺诈,确保产品的真实性.

科学领域:

农业科学农业科学
食品化学食品化学
数据科学数据科学数据科学

背景情况:

希腊大豆 (Gigantes Elefantes,Phaseolus vulgaris L.) 是希腊美食和经济的重要组成部分.
食品欺诈对消费者安全和经济稳定构成重大风险.
在全球食品工业中,认证农产品的真实性至关重要.

研究的目的:

调查多元元素分析与决策树学习相结合,以确定希腊巨型豆的起源.
区分来自卡斯托里亚和普雷斯佩斯的豆类的地理来源 (受保护的地理标志 - PGI地位).
为了打击食品欺诈,并确保希腊巨型豆的真实性.

主要方法:

使用诱导合等离子体质谱法 (ICP-MS) 对豆样进行了广泛的多元件分析.
分析包括了各种各样的元素 (例如,Ag,Al,Ca,Fe,K,Mg,Na,P,Zn).
应用决策树学习算法来根据元素配置文件对豆类进行分类.

主要成果:

决策树模型实现了高性能指标:92.86%的准确性,87.50%的灵敏性和96.88%的特异性.
元素形状有效地作为指纹来区分豆类根据其地理来源.
该模型成功地区分了来自卡斯托里亚和普雷斯佩斯的希腊巨型豆类.

结论:

多元元素分析与决策树算法相结合,是验证希腊大豆真实性的高效方法.
这种方法提供了一种可靠的工具来打击食品欺诈,并验证PGI产品的地理来源.
这些发现支持食品供应链的完整性,并保护消费者和生产者.

关键词:

在ICP-MS中,使用的是ICP-MS.决策树是一个决策树.一个基本的指纹.地理来源的地理来源.巨大的豆子机器学习是机器学习.多元元素分析是一种多元元素分析.

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