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Updated: Sep 19, 2025

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机器学习辅助的分类和假冒检测脂肪油使用通过超临界流体染色学获得的脂肪酸概况
Yue-Mei Zhao1, Zi-Ying Wang2, Zhen Liu3
1School of Pharmacy, Nanjing University of Chinese Medicine, Nanjing 210046, China.
Journal of pharmaceutical and biomedical analysis
|June 4, 2025
概括
检测脂肪油中的改对于制药安全至关重要. 本研究介绍了一种使用机器学习和SFC-MS的快速方法,以准确识别脂肪油和检测改,通过随机森林算法实现100%的准确性.
科学领域:
- 分析化学 分析化学
- 制药科学 制药科学
- 数据科学数据科学数据科学
背景情况:
- 脂肪油在药品中对于药物输送和健康益处至关重要.
- 确保脂肪油的纯度对于制药质量和患者安全至关重要.
- 目前用于检测伪造的方法可能耗时且复杂.
研究的目的:
- 开发一种快速而准确的方法来分类脂肪油.
- 用先进的分析技术检测和量化脂肪油中的改.
- 建立一个可靠的系统,以确保药品应用中脂肪油的完整性.
主要方法:
- 超临界流体染色学-质谱学 (SFC-MS) 用于分析自由脂肪酸 (FFA) 概况.
- 收集了五种常见脂肪油中14种FFA的数据.
- 监督机器学习 (ML) 模型被训练用于分类和伪造检测.
主要成果:
- 三种ML模型成功识别了五种常见的脂肪油.
- 快速确定了45种混合脂肪油的FFA成分,这些油具有不同的改水平.
- 随机森林 (RF) 算法在对所有样品进行分类和检测改方面实现了100%的准确性.
结论:
- 将ML与SFC-MS集成为快速脂肪油认证提供了一个强大的工具.
- 这种方法显著提高了检测制药级脂肪油中改的能力.
- 开发的方法为制药行业的质量控制提供了可靠的解决方案.
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