使用高光谱成像和机器学习进行Ganoderma lucidum组成的非破坏性分析
Jing Ran1, Hui Xu1, Zhilong Wang1
1Northeast Asia Institute of Traditional Chinese Medicine, Changchun University of Chinese Medicine, Changchun, Jilin, China.
Frontiers in chemistry
|March 13, 2025
概括
这项研究引入了一种使用高光谱成像和机器学习的快速,非侵入性方法,以准确预测Ganoderma lucidum中的多糖和厄戈斯特水平,确保药用的质量.
科学领域:
- 分析化学 分析化学
- 计算生物学 计算生物学
- 菌类学 菌类学是指菌类学.
背景情况:
- (Ganoderma lucidum) 是一种有价值的药用.
- 传统的质量评估方法复杂且昂贵.
- 需要进行快速,非破坏性的质量测试.
研究的目的:
- 开发一种快速,非侵入性的方法来评估Ganoderma lucidum的质量.
- 通过使用高光谱成像和机器学习来预测多糖和厄戈斯特醇含量.
主要方法:
- 收集了可见近红外和短波红外范围内的超光谱图像.
- 通过HPLC测量埃尔戈斯特醇,通过硫酸法测量多糖.
- 测试了feedforward神经网络,极端学习机器和决策树模型.
主要成果:
- 极端学习机器模型,通过遗传算法和投票进行优化,显示出出色的预测性能.
- 实现了多糖的R2值为0.96和埃尔戈斯特的0.97.
- 证明了模型能够准确量化关键化合物的能力.
结论:
- 超光谱成像与机器学习相结合,为Ganoderma lucidum质量评估提供了一种新的方法.
- 这种方法是非破坏性的和高效的.
- 为传统化学分析提供了可行的替代方案.
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