基于集成的电化学语言和机器学习的叶面菌体分类器
Yaqi Huang1, Yueyu Wang1, Fulin Zhu1
1School of Mechanical Engineering, Nanjing University of Science and Technology, Nanjing 210094, P. R. China.
ACS sensors
|February 4, 2025
概括
一个新的智能电子舌头 (SET) 平台使用电化学指纹快速识别Dendrobium植物品种. 这项技术通过确保植物来源的真实性来增强传统中医药的发展.
科学领域:
- 分析化学 分析化学
- 生物传感技术的技术
- 传统中国医药 传统中国医药
背景情况:
- 植物采购显著影响草药的安全性和有效性,阻碍了传统中医 (TCM) 产业的增长.
- 准确和快速识别植物资源对于TCM开发和质量控制至关重要.
研究的目的:
- 开发一个便携式,智能和集成的平台,用于准确识别Dendrobium红杉亚种和来源.
- 为了建立一个多功能解决方案,对各种药用植物的点-of-need分类.
主要方法:
- 一种智能电子舌头 (SET) 设备的创新,该设备具有用于叶子提取的空心微针阵列和用于电化学读取的五电极芯片.
- 在自我组装的单层上利用差分脉冲电压测量来生成Dendrobium亚型的高维电化学指纹.
- 采用机器学习算法进行植物品种的自动认证和分类.
主要成果:
- 在SET平台上,成功实现了8个Dendrobium品种的自动认证.
- 在分类不同Dendrobium亚型时,证明了高达95%的分辨准确度.
- 该设备集成了小型化,叶面提取,按需抽样和电化学分析.
结论:
- 开发的智能电子舌头 (SET) 提供了一种快速,方便和准确的方法来识别Dendrobium植物品种.
- 这种生物传感装置为在需要时分类药用植物提供了多功能解决方案,支持TCM行业.
- 这项技术有望通过可靠的植物认证来确保草药的质量和安全.
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