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关于通过NIR和MIR频谱数据对Polygonatum kingianum来源进行歧视的可行性研究

Yue Wang^1,2, Yuanzhong Wang²

¹College of Traditional Chinese Medicine, Yunnan University of Chinese Medicine, Kunming, China.

Journal of food science

|October 1, 2024

概括

近红外和中红外光谱学可以确定Polygonatum kingianum (P. kingianum) 的地理来源. 一个主要组件模型实现了完美的准确性,展示了一种用于追踪P. kingianum来源的实用方法.

科学领域:

分析化学分析化学
化学测量化学测量化学测量
植物认证植物认证

背景情况:

地理标志对受保护物种至关重要.
对于物种的近距离地理原产地确定存在有限的研究.
波利格纳 (Polygonatum kingianum) 是一种有价值的药用植物,具有区域差异.

研究的目的:

调查近红外和中红外光谱法用于P. kingianum起源识别.
将传统的机器学习模型与数据融合技术进行比较.
开发一个实用的模型来确定P. kingianum.的近距离地理来源.

主要方法:

从中国云南省的六个地区收集了156个P. kingianum样本.
使用近红外和中红外光谱学获得的光谱图像.
应用数据融合技术和机器学习模型,包括主要组件分析和残余卷积神经网络 (ResNet).

主要成果:

光谱图像为P. kingianum分析提供了丰富的信息.
中级数据融合主要组件模型实现了100%的灵敏度,特异性和准确性.
在特定频段的ResNet模型强调了更少变量的优势,以提高准确性.

结论:

近红外和中红外光谱是可行的,以确定P. kingianum的地理来源.
数据融合主要组件模型提供了一个高度准确和高效的方法.
这项研究为追踪P. kingianum的来源制定了一个实用的策略.

关键词:

多边形的Kingianum多边形的Kingianum.数据融合数据融合红外光谱学红外光谱学机器学习是机器学习.歧视的起源歧视的起源

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