一种双分支CNN编码模型,用于快速地缘追踪近红外光谱
Shumin Gao1, Haofan Zhang2, Zhiqing Yang3
1Institute for Complexity Science, Henan University of Technology, Zhengzhou 450001, China.
概括
一种新方法使用近红外 (NIR) 光谱和双分支深度神经网络 (双TCNet) 快速识别阿斯特拉加勒斯药草的来源. 这种技术确保了中国传统医学的质量控制.
科学领域:
- 植物医学
- 光谱学
- 计算生物学
背景情况:
- 地理来源影响药用草药的化学成分和功效.
- 传统中医药 (TCM) 的质量控制需要可靠的来源识别.
- 这种植物对生长环境很敏感.
研究的目的:
- 开发一种快速,非破坏性的方法来追踪阿斯特拉加勒斯的地理来源.
- 评估一种新的双TCNet模型的草本来源可追溯性.
主要方法:
- 使用近红外光谱 (NIR) 来收集588个阿斯特拉加勒斯样本的光谱数据.
- 开发了一个双分支深度神经网络 (双TCNet),集成了CNN和修改后的编码器.
- 一个多头注意力机制被用来融合多尺度的光谱特征.
主要成果:
- 双TCNet模型实现了高精度 (98.31%),精度 (98.39%) 和回忆 (98.32%).
- 性能超过了SVM,RF和1D-CNN等传统模型.
- 该方法证明了来自五个不同的地理区域的样本的有效分类.
结论:
- 拟议的双TCNet方法提供了一个非常准确和高效的方法,用于非破坏性地追溯阿斯特拉加勒斯的来源.
- 这种技术在确保TCM成分的质量和真实性方面具有显著的实际潜力.
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