基于多来源数据的融合,确定了Gastrodia elata Blume的地理来源

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Hong Liu^1,2, Honggao Liu³, Jieqing Li¹

¹College of Agronomy and Biotechnology, Yunnan Agricultural University, Kunming, China.

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|June 27, 2024

概括

红外光谱学和机器学习准确地识别了Gastrodia elata Blume起源. 序列数据融合和ResNet模型为地理来源识别提供了强大的解决方案.

科学领域:

药理学是指药理学,即药理学是指药理学.
分析化学分析化学
计算生物学计算生物学

背景情况:

准确地确定Gastrodia elata Blume的地理来源对于其药用至关重要.
本研究探讨了用于原产地确定的先进光谱和机器学习技术.

研究的目的:

开发快速准确的方法来识别Gastrodia elata Blume的地理来源.
为了比较不同机器学习模型和数据融合策略的有效性.

主要方法:

收集了来自306个胃类动物 (Gastrodia elata) 样本的减弱总反射里叶变换红外 (ATR-FTIR) 和里叶变换近红外 (FT-NIR) 光谱.
开发了使用单个和融合光谱数据的支持矢量机 (SVM) 模型.
在特征层面的融合中使用了顺序和正交的部分最小平方差异分析 (SO-PLS-DA).
使用残余卷积神经网络 (ResNet) 模型与光谱成像.

主要成果:

功能级融合模型的准确性高于单频谱或全频谱融合模型.
与基于SVM的特征级融合相比,SO-PLS-DA提供了一个更简单的方法.
ResNet模型显示了良好的分类性能,但需要更多的数据来改进概括.

结论:

序列和正交的数据融合方法是有效的识别Gastrodia elata来源.
ResNet模型为地理来源识别提供了一个强大的替代方案,并有可能进一步优化.
这些方法有助于科学和理性地利用Gastrodia elata.

关键词:

2DCOS 图像的使用情况在ATR-FTIR中使用.这是一个FT-NIR.胃类虫是布鲁姆的动物.数据融合数据融合地理歧视是指地理上的歧视.

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