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智能物种识别 Fritillaria spp. 的智能物种识别. 基于多光谱特征融合的基础上

Yuchen Tang¹, Xuting Guo¹, Jianqiang Zhao²

¹College of Pharmaceutical Engineering of Traditional Chinese Medicine, Tianjin University of Traditional Chinese Medicine, Tianjin 301617, PR China; Tianjin Key Laboratory of Intelligent and Green Pharmaceuticals for Traditional Chinese Medicine, Tianjin 301617, PR China.

Spectrochimica acta. Part A, Molecular and biomolecular spectroscopy

|November 20, 2025

概括

使用便携式光谱和机器学习快速验证Fritillaria物种的真实性. 数据融合显著提高了识别准确度,为这种有价值的药用植物提供了快速,非破坏性的方法.

科学领域:

分析化学分析化学
频谱学是一种光谱学.
化学测量化学测量化学测量

背景情况:

弗里蒂拉里亚菌种类. 它们是有价值的药用植物,具有多种化学特征.
精确的认证是非常重要的,因为属性和药用价值的变化.
传统的识别方法耗时且缺乏精度.

研究的目的:

评估快速 Fritillaria spp. 的可行性. 使用便携式光谱仪进行识别.
开发和比较机器学习模型进行分类.
调查数据融合对识别准确性的影响.

主要方法:

使用便携式近红外 (NIR),中红外 (MIR) 和拉曼光谱.
开发了使用K-近邻 (KNN),随机森林 (RF) 和支持向量机 (SVM) 的分类模型.
在预处理和特征选择后应用低,中,高级数据融合策略.

主要成果:

数据融合显著提高了Fritillaria spp.的分类准确性.
使用堆叠的高水平融合实现了最高的准确性 (90.41%),F1得分为0.906,卡帕为0.861,AUC为0.989.
综合光谱方法在快速和非破坏性分析方面被证明是有效的.

结论:

便携式光谱学与机器学习相结合,为Fritillaria spp.提供了快速而准确的方法. 认证. 认证. 认证. 认证. 认证. 认证. 认证.
数据融合策略是最大化光谱识别性能的关键.
这种方法提高了Fritillaria研究和质量控制的分析能力.

关键词:

数据融合数据融合弗里蒂拉里亚菌种类.中红外光谱学中红外光谱学.便携式近红外光谱学便携式近红外光谱学拉曼光谱法拉曼光谱法

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