首页> 外国专利> LASER-INDUCED BREAKDOWN SPECTROSCOPY TECHNOLOGY BASED QUANTITATIVE ANALYSIS METHOD FOR LEAD ELEMENTS IN TEA

LASER-INDUCED BREAKDOWN SPECTROSCOPY TECHNOLOGY BASED QUANTITATIVE ANALYSIS METHOD FOR LEAD ELEMENTS IN TEA

机译:基于激光的击穿光谱技术对茶叶中铅元素的定量分析方法

摘要

Provided is a laser-induced breakdown spectroscopy technology based quantitative analysis method for lead elements in tea, which falls within the field of spectral analysis. The method comprises: preliminarily analyzing tea having different lead concentrations by means of an LIBS device, and determining a spectral line with lead elements at the position of 405.78 nm according to the American standard atomic spectral line library; then, placing LIBS spectral signal data into a stochastic resonance bi-stable system, and establishing a stochastic resonance system equation; subsequently, optimizing parameters by means of an ant colony algorithm, performing numerical simulation on an optimized stochastic resonance system equation by means of a four-order Runge-Kutta algorithm, and fitting spectra by means of a Voigt function to acquire a signal spectral line after stochastic resonance amplification; and finally, constructing a calibration curve between the lead concentrations of the tea and the signal spectral line to realize the quantitative analysis of lead elements in a practical tea sample. The present invention has the advantages of high discrimination accuracy, simplicity, rapidness, etc., and a reference method is provided for heavy metal content analysis of tea.
机译:提供了一种基于激光诱导击穿光谱技术的茶叶中铅元素定量分析方法,属于光谱分析领域。该方法包括:通过LIBS装置对铅含量不同的茶进行初步分析,并根据美国标准原子光谱线库确定铅元素在405.78nm位置处的光谱线。然后,将LIBS频谱信号数据放入随机共振双稳态系统中,建立随机共振系统方程。随后,通过蚁群算法优化参数,通过四阶Runge-Kutta算法对优化的随机共振系统方程进行数值模拟,并通过Voigt函数拟合光谱以获取信号谱线。随机共振放大;最后,在茶叶中铅浓度与信号谱线之间建立校正曲线,以实现对实际茶叶样品中铅元素的定量分析。本发明具有判别准确度高,简便,快速等优点,提供了一种茶叶重金属含量分析的参考方法。

著录项

  • 公开/公告号WO2019184678A1

    专利类型

  • 公开/公告日2019-10-03

    原文格式PDF

  • 申请/专利权人 JIANGNAN UNIVERSITY;

    申请/专利号WO2019CN77390

  • 申请日2019-03-08

  • 分类号G01N21/71;

  • 国家 WO

  • 入库时间 2022-08-21 11:53:03

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