用激光诱导分解光谱检测的坑-频谱特征融合方法
Rongqin Chen1, Xiaolong Li1, Weijiao Li2
1College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China.
Foods (Basel, Switzerland)
|April 13, 2024
概括
这项研究引入了一种新的火山口频谱特征融合方法,以准确检测Panax notoginseng中的. 这种技术减少了信号不确定性,提高了草药和农产品的安全性.
科学领域:
- 分析化学 分析化学
- 频谱学是一种光谱学.
- 环境科学 环境科学
背景情况:
- 帕纳克斯诺托金生是一种有价值的草药,具有药用和食用途.
- 在P.notoginseng中积累的会给环境和健康带来风险.
- 准确检测对于确保产品安全至关重要.
研究的目的:
- 开发一种快速而准确的方法来检测P.notoginseng.中的 (Cd).
- 为了减少激光诱导分解光谱 (LIBS) 中的信号不确定性,以提高量化.
- 为验证一种用于有毒金属检测的新火山口频谱特征聚变方法.
主要方法:
- 采用激光诱导分解光谱 (LIBS) 进行Cd的快速检测.
- 开发了一种火山口频谱特征融合方法,包括除火山口形态补偿和特征峰值比率校正 (CPRC).
- 使用多重线性回归 (MLR) 和最小平方支持向量机 (LSSVM) 模型进行数据分析.
主要成果:
- 坑道形态补偿减少了RMSEP从7.0233降至5.4043μg/g.
- 在CPRC预处理中,RMSEP达到3.4980μg/g,检测极限为1.92μg/g.
- 使用LSSVM的火山口频谱特征融合方法产生了最低的RMSEP,为2.8556μg/g.
结论:
- 坑频谱特征融合方法有效降低了P.notoginseng中Cd检测的信号不确定性.
- 这种方法为确保农产品的质量和安全提供了一个有希望的方法.
- 该技术有可能在各种矩阵中检测其他有毒金属.
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