机器学习辅助的快速拉曼光谱分析:Radix Bupleuri的一个案例研究.
Fangjie Guo1, Xudong Yang1, Zhengyong Zhang2
1Quality and Safety Engineering Institute of Food and Drug, Zhejiang Gongshang University, Hangzhou, China.
Journal of the science of food and agriculture
|November 9, 2024
概括
机器学习模型使用拉曼光谱学准确预测Radix Bupleuri起源. 线性分辨分析支持向量机 (LDA-SVM) 模型显示了用于分类草本样本的高精度和稳定性.
科学领域:
- 使用先进的光谱技术和计算方法进行植物学分析.
- 集成机器学习,用于自然产品化学中的复杂数据解释.
背景情况:
- 拉迪克斯布普勒乌里因其药用特性而广泛使用,但其有效性受环境因素影响,影响成分度.
- 在Radix Bupleuri的变异性需要强大的质量控制和原产地确定方法.
研究的目的:
- 开发和评估机器学习模型,用于分类和预测Radix Bupleuri的生产区域.
- 根据拉曼光谱数据评估各种机器学习算法的性能,包括LDA,SVM,XGBoost和LightGBM.
主要方法:
- 收集了来自不同地区和品种的Radix Bupleuri样本.
- 获取实验和计算拉曼光谱用于样本分析.
- 使用线性差异分析 (LDA) 应用尺寸缩小,然后使用支持矢量机 (SVM),XGBoost和LightGBM进行分类和回归建模.
主要成果:
- 支持向量分类器在预测生产地区时,实现了高精度98%和F1得分高于0.96的高精度.
- 支持向量回归表现出良好的适配性能,R2得分高于0.90.
- 复杂的模型表现出过度拟合的趋势,影响了概括能力.
结论:
- LDA-SVM模型被证明是Radix Bupleuri分类的高性能和稳定的方法,与高性能液态染色学 (HPLC) 结果保持一致.
- 这种快速分类和回归技术显示了对其他药草的应用潜力,提高了质量控制和认证.
相关概念视频
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