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Updated: May 22, 2025

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菲律宾罗布斯塔咖啡 (C. canephora) 的地理起源差异化,使用基于X射线光的元素分析,化学测量和机器学习

Krizzia Rae S Gines¹, Emmanuel V Garcia², Rosario S Sagum³

¹The Graduate School, University of Santo Tomas, España Boulevard, Manila 1015, Philippines; Food and Water Institute, De La Salle University, 2401 Taft Avenue, Manila 0922, Philippines.

|March 14, 2025

概括

验证单一来源咖啡的真实性至关重要. 能量分散式X射线光 (EDXRF) 元素分析与机器学习相结合,准确地确定了罗布斯塔咖啡的起源,提高了可追溯性.

科学领域:

农业科学农业科学
分析化学分析化学
数据科学数据科学数据科学

背景情况:

对于高价值作物的真实性和可追溯性的需求日益增加,例如单一来源的咖啡.
需要可靠的方法来防止欺诈和核实地理来源.
支持菲律宾咖啡行业提供强有力的验证系统的重要性.

研究的目的:

探索一种快速,具有成本效益的方法来验证罗布斯塔咖啡的地理来源.
评估能量分散式X射线光 (EDXRF) 元素分析的有效性,结合化学测量和机器学习.
为开发菲律宾咖啡行业的真实性和可追溯性系统提供基准数据.

主要方法:

在43个绿色罗布斯塔咖啡样本中分析了十个元素度 (K,P,Ca,S,Cl,Fe,Cu,Mn,Sr,Zn).
主要组件分析 (PCA) 用于模式识别的应用.
使用线性差异分析 (LDA) 和随机森林 (RF) 进行分类和准确性评估.

主要成果:

主要成分分析 (PCA) 证明了咖啡样本根据原产地进行了明显的聚类.
线性差别分析 (LDA) 在地理分类中实现了79%的准确性.
随机森林 (RF) 将分类准确度提高到84%,显示出卓越的性能.

结论:

能量分散式X射线光 (EDXRF) 元素分析是区分罗布斯塔咖啡来源的可行方法.
化学测量和机器学习技术显著提高了地理分类的准确性.
这项研究为基于XRF的原产地验证提供了概念证明,支持菲律宾咖啡行业的可追溯性努力.

关键词:

化学测量化学测量化学测量食品的真实性食品的真实性食品的可追溯性食品的可追溯性地理来源地理来源机器学习是机器学习.菲律宾的咖啡是菲律宾的咖啡.一个X射线的光效应.

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