使用福里埃变换近红外光谱学,结合机器学习技术和特征减小分析,对Amomum tsaoko种子进行地理认证
Yinggang Zheng1, Songping Lan2, Haoran Hu3
1Shanghai Key Laboratory of Anesthesiology and Brain Functional Modulation, Clinical Research Center for Anesthesiology and Perioperative Medicine, Translational Research Institute of Brain and Brain-Like Intelligence, Shanghai Fourth People's Hospital, School of Medicine, Tongji University, Shanghai, China.
Frontiers in plant science
|February 9, 2026
概括
这项研究使用近红外光谱学 (NIRS) 和机器学习 (ML) 来验证Amomun tsaoko种子的真实性. 最优的方法达到96.97%的准确性,使得地理原产地快速验证.
科学领域:
- 分析化学 分析化学
- 化学测量 化学测量 化学测量
- 植物科学 植物科学
背景情况:
- 来自东亚/东南亚的香料Amomun tsaoko在中国云南省有着重要的生产.
- 在A. tsaoko的遗传变异导致了区域品种,需要原产地认证.
- 地理来源认证对于A. tsaoko的质量控制和商业价值至关重要.
研究的目的:
- 为了验证来自特定地区的A. tsaoko种子的地理来源.
- 开发一种快速而准确的A. tsaoko地理认证方法.
主要方法:
- 采用近红外光谱 (NIRS) 来获取光谱数据.
- 机器学习 (ML) 算法,包括多层感知器 (MLP) 和Catboost,用于数据分析.
- 福利埃变换近红外光谱法 (FT-NIR) 与MLP相结合被调查为主要的身份验证策略.
主要成果:
- 结合FT-NIR和MLP方法,FT-NIR显示出最佳的性能,达到96.97%的准确性.
- 使用Catboost模型识别了用于身份验证的关键光谱范围.
- 该研究成功地根据其地理来源区分了A. tsaoko种子.
结论:
- 对NIRS原始数据的预处理对于有效的身份验证至关重要.
- 机器学习技术为快速和特定的植物地理认证提供了一种可行的方法.
- 这种方法可用于确保A. tsaoko. 的质量和可追溯性.
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