用矿物质成分差异，98.37%准确识别蜂蜜掺假
A Machine Learning Approach for Honey Adulteration Detection using Mineral Element Profiles
- 通过分析蜂蜜中矿物质元素含量差异进行分类
- 随机森林模型达到98.37%交叉验证准确率
- 适合食品检测与质量控制领域研究人员
本文旨在构建一种基于机器学习的蜂蜜掺假检测系统，利用蜂蜜的矿物元素组成特征。系统分为预处理和分类两阶段：预处理包括缺失值处理与归一化；分类阶段采用逻辑回归、决策树和随机森林三种监督学习模型，区分真蜂蜜与掺假蜂蜜。使用公开数据集（包含真蜂蜜、糖浆及掺假蜂蜜的矿物元素测量值）评估模型性能。实验结果表明，蜂蜜中矿物元素含量可提供强区分性信息，其中随机森林分类器表现最优，交叉验证准确率达98.37%。
原文摘要 · Abstract (English)
This paper aims to develop a Machine Learning (ML)-based system for detecting honey adulteration utilizing honey mineral element profiles. The proposed system comprises two phases: preprocessing and classification. The preprocessing phase involves the treatment of missing-value attributes and normalization. In the classifica-tion phase, we use three supervised ML models: logistic regression, decision tree, and random forest, to dis-criminate between authentic and adulterated honey. To evaluate the performance of the ML models, we use a public dataset comprising measurements of mineral element content of authentic honey, sugar syrups, and adul-terated honey. Experimental findings show that mineral element content in honey provides robust discriminative information for detecting honey adulteration. Results also demonstrate that the random forest-based classifier outperforms other classifiers on this dataset, achieving the highest cross-validation accuracy of 98.37%.
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