用矿物质特征和机器学习区分蜂蜜的花源与产地
Classification of Honey Botanical and Geographical Sources using Mineral Profiles and Machine Learning
- 通过矿物质元素数据预处理与分类建模，实现蜂蜜来源识别
- 随机森林模型准确率达99.30%（花源）和98.01%（产地）
- 适合食品溯源、蜂蜜品质鉴定领域研究人员参考
本文提出一种基于机器学习的蜂蜜花源与地理来源分类方法，利用蜂蜜的矿物质元素组成进行识别。该方法分为预处理与分类两阶段：预处理包括缺失值处理与数据归一化；分类阶段采用多种监督学习模型，对六类花源与十三个地理来源的蜂蜜进行区分。在公开可用的蜂蜜矿物质元素数据集上测试，结果表明蜂蜜中矿物质含量具有显著区分性。其中随机森林（Random Forests, RF）表现最优，交叉验证准确率分别达到99.30%（花源分类）和98.01%（地理来源分类）。
原文摘要 · Abstract (English)
This paper proposes a machine learning-based approach for identifying honey floral and geographical sources using mineral element profiles. The proposed method comprises two steps: preprocessing and classification. The preprocessing phase involves missing-value treatment and data normalization. In the classification phase, we employ various supervised classification models for discriminating between six botanical sources and 13 geographical origins of honey. We test the classifiers' performance on a publicly available honey mineral element dataset. The dataset contains mineral element profiles of honeys from various floral and geographical origins. Results show that mineral element content in honey provides discriminative information useful for classifying honey botanical and geographical sources. Results also show that the Random Forests (RF) classifier obtains the best performance on this dataset, achieving a cross-validation accuracy of 99.30% for classifying honey botanical origins and 98.01% for classifying honey geographical origins.
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