基于HPLC的Cinnamomum cassia根据地理原产地进行化学分类
Bui Thi Lan Phuong1,2, Hoang Trung Tung3, Do Khanh Linh3
1National Institute of Pharmaceutical Technology, Hanoi University of Pharmacy, Hanoi, Vietnam.
Biomedical chromatography : BMC
|November 30, 2025
概括
根据产地对肉进行分类对于质量至关重要. 机器学习模型,特别是使用HPLC数据和主要组件分析,在区分肉的地理来源方面实现了100%的准确性.
科学领域:
- 分析化学 分析化学
- 计算化学的计算化学
- 食品科学 食品科学 食品科学
背景情况:
- 肉的质量与其地理来源密切相关.
- 根据地区对肉进行准确的分类对于贸易和质量控制至关重要.
研究的目的:
- 根据其地理来源对肉样品进行分类.
- 评估各种机器学习模型在肉分类中的有效性.
- 探索数据预处理技术,以提高分类准确度.
主要方法:
- 针对库马林,肉酸和肉甲度的有针对性的分析.
- 使用高性能液态染色学 (HPLC) 染色图进行非定向分析.
- 机器学习算法的应用:XGBoost,支持向量机,随机森林,投票,堆叠和卷积神经网络.
- 使用SHapley添加式扩展 (SHAP) 来确定特征的重要性.
- 使用主要组件分析 (PCA) 来减少维度.
主要成果:
- 使用XGBoost的有针对性的分析实现了76.47%的准确性,这表明仅使用三个关键化合物的局限性.
- 非定向的HPLC分析显著改善了分类,投票和堆叠模型的准确性达到94.18%.
- 结合PCA和机器学习模型,PCA可以100%准确地对肉来源进行分类.
结论:
- 非定向的HPLC数据和先进的机器学习的结合提供了一个强大的方法来区分肉来源.
- 主要成分分析是提高肉分类模型性能的一种高效方法.
- 本研究提出了一种有效和准确的方法来验证肉的地理来源.
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