基于三段动态值联合优化战略的mRMR-PCA-LGBM模型,用于通过中红外光谱学识别Cornus officinalis的原产地
Bing Liu1,2, Hua Yi1, Chaoning Li2
1Public Foundational Courses Department, Nanjing University of Industry Technology Nanjing 210023 China Liub1@niit.edu.cn.
RSC advances
|November 24, 2025
概括
这项研究引入了一个新的框架,可以使用光谱数据快速追踪Cornus officinalis的起源. 该方法能够高精度地有效地识别药物原料的来源,确保质量和安全.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 药理学是指药理学,它是指药理学.
背景情况:
- 中国药用材料的来源对于它们的有效性和安全性至关重要.
- 像Cornus officinalis这样的药用植物的快速和准确的可追溯性对于质量控制至关重要.
研究的目的:
- 开发和验证使用光谱数据准确识别Cornus officinalis的原产地框架.
- 建立一个有效的技术途径,用于验证中国药用材料的真实性和市场监督.
主要方法:
- 提出了一个由三个部分组成的动态值联合优化框架.
- 使用最小冗余最大相关性 (mRMR) 算法进行光谱分类和频段选择.
- 贝叶斯优化用于光谱带和LightGBM超参数的联合优化.
- 主要组件分析 (PCA) 用于减小维度.
- 开发和评估了mRMR-PCA-LightGBM模型.
主要成果:
- 最优化的模型保留了34个关键光谱频段,删除了345个频段.
- 减小尺寸导致38个主要组件,最终输入72个特征.
- mRMR-PCA-LightGBM模型实现了90.9%的准确性,0.91 F1得分,0.90 科恩卡帕和0.90 马修斯相关系数.
- 接收器运行特征 - 曲线下的面积超过0.95对于所有11个来源.
- 性能明显优于五个控制模型.
结论:
- 拟议的框架使得Cornus officinalis的高度准确和可靠的来源识别成为可能.
- 该方法有效地捕获特定来源的光谱信息,同时最大限度地减少噪音.
- 这种方法为验证中国药用材料的真实性提供了实用和有效的解决方案.
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