使用近红外高光谱成像技术快速识别菌体物种
Kaixuan Li1, Yijun Guo1, Haosheng Zhong2
1College of Optical, Mechanical and Electrical Engineering, Zhejiang A&F University, Hangzhou 311300, China.
Sensors (Basel, Switzerland)
|September 27, 2025
概括
近红外高光谱成像 (NIR-HSI) 可以快速识别Dendrobium物种. 使用CARS的特征选择显著提高了分类准确度,有助于控制这种有价值的药用草本的质量.
科学领域:
- 农业科学 农业科学
- 分析化学 分析化学
- 生物技术是生物技术.
背景情况:
- 准确识别Dendrobium officinale*,一个有价值的中国药草,是挑战后处理.
- 目前用于区分Dendrobium物种的方法耗时且缺乏足够的准确性.
研究的目的:
- 开发一种快速而准确的分类模型,以使用近红外高光谱成像 (NIR-HSI) 来识别Dendrobium物种.
- 通过数据预处理和特征波长选择来优化模型.
主要方法:
- 用了五种Dendrobium物种进行光谱分析.
- 应用光谱预处理技术,包括规范化和光滑.
- 构建支持向量机 (SVM) 模型,并采用竞争性自适应重量取样 (CARS) 和连续投影算法 (SPA) 进行特征选择.
主要成果:
- 标准化-SVM模型实现了97%的校准和88%的预测准确度.
- CARS-Normalize-SVM模型以98%的校准率和96%的预测准确率展示了卓越的性能.
- *D. aphyllum*分类显著改善,达到100%的回忆和95.24%的F1得分.
结论:
- 超光谱成像为快速准确识别Dendrobium物种提供了一个有前途的工具.
- 优化的模型增强了对药用Dendrobium的质量控制和市场监督.
- 像CARS这样的特征选择方法对于提高模型效率和准确性至关重要.
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