WO2019184678A1 - 一种基于激光诱导击穿光谱技术定量分析茶叶中铅元素的方法 - Google Patents

一种基于激光诱导击穿光谱技术定量分析茶叶中铅元素的方法 Download PDF

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WO2019184678A1
WO2019184678A1 PCT/CN2019/077390 CN2019077390W WO2019184678A1 WO 2019184678 A1 WO2019184678 A1 WO 2019184678A1 CN 2019077390 W CN2019077390 W CN 2019077390W WO 2019184678 A1 WO2019184678 A1 WO 2019184678A1
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tea
lead
stochastic resonance
signal
spectral
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黄敏
张宏阳
朱启兵
郭亚
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江南大学
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    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/62—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light
    • G01N21/71—Systems in which the material investigated is excited whereby it emits light or causes a change in wavelength of the incident light thermally excited
    • G01N21/718—Laser microanalysis, i.e. with formation of sample plasma
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    • G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
    • G01N21/00—Investigating or analysing materials by the use of optical means, i.e. using sub-millimetre waves, infrared, visible or ultraviolet light
    • G01N21/84—Systems specially adapted for particular applications
    • G01N21/88—Investigating the presence of flaws or contamination
    • G01N21/94—Investigating contamination, e.g. dust

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  • the invention belongs to the field of spectral analysis, and in particular relates to a method for realizing quantitative analysis of lead in heavy metal elements in tea based on laser induced breakdown spectroscopy combined with stochastic resonance algorithm.
  • tea has rich nutritional value and health care functions. Tea is rich in amino acids, tea polyphenols, caffeine and other chemical components and a variety of trace elements, which is beneficial to the human body. With the improvement of people's quality of life, people are paying more and more attention to the quality of tea.
  • the hazard elements affecting the quality and safety of tea mainly include: heavy metals exceeding the standard, pesticide residues and organic microbial contamination.
  • the impact of environmental degradation in China will lead to harmful heavy metal ions contaminating tea, and the phenomenon of excessive heavy metal content in tea products will seriously affect the quality and safety of tea and affect the international market for tea exports. Heavy metals in tea have become a key factor restricting the quality and safety of tea.
  • LIBS Laser induced breakdown spectroscopy
  • the LIBS technology is an analytical technique that uses a laser to illuminate the surface of a measured object to generate a plasma component (qualitative analysis), concentration (quantitative analysis), and substance identification.
  • LIBS has real-time, fast, non-destructive or micro-loss detection compared to traditional spectral analysis methods.
  • the method of weak signal amplification based on stochastic resonance method is to construct the bistable system, and optimize the system parameters a and b by ant colony algorithm to ensure the maximum output signal-to-noise ratio of the system.
  • the Voigt fitting method is used to fit and correct the spectral signals of the system, and establish the calibration curve of the spectral intensity and lead concentration of tea to realize the detection of lead content in heavy metals of tea.
  • the present application provides a method for quantitatively analyzing lead elements in tea based on laser induced breakdown spectroscopy.
  • a method for quantitatively analyzing lead elements in tea based on laser induced breakdown spectroscopy comprising the following steps:
  • Breakdown spectrum detection method is used to obtain the spectral signal of the tablet-like sample, and the spectral signal data of the tablet-like sample is normalized by using the carbon element as a reference;
  • Voigt spectrum correction the Voigt function is used to fit the amplified signal to obtain the signal line after random resonance amplification
  • the stochastic resonance system equation in the step (2) is:
  • V(x) -ax 2 /2+bx 4 /4
  • s(t) is the analog signal
  • V(x) is the potential function of the nonlinear bistable system
  • G(t) is the Gaussian white noise with the intensity D and the mean value of 0
  • ⁇ (t) is the impulse function.
  • ⁇ G(t), G(t- ⁇ )> denotes the G(t) autocorrelation function
  • ⁇ is the time delay
  • a and b are greater than 0 system parameters.
  • k 2 h ⁇ (a ⁇ (p n +k 1 /2) ⁇ b ⁇ (p n +k 1 /2) 3 +I n )
  • k 3 h ⁇ (a ⁇ (p n +k 2 /2) ⁇ b ⁇ (p n +k 2 /2) 3 +I n+1 )
  • k 4 h ⁇ (a ⁇ (p n +k 3 ) ⁇ b ⁇ (p n +k 3 ) 3 +I n+1 )
  • M is the input signal length
  • h is the integration step size
  • h 1/f s
  • f s is the sampling frequency
  • I n is the bistable input signal
  • I 0 is set to 0
  • p n is the bi-stable output signal
  • k 1 represents the slope at the point p n
  • k 2 represents the slope at p n +k 1 /2 using k 1
  • k 3 represents the slope at p n +k 2 /2 using k 2
  • k 4 represents calculated using the slope k 3 k 3 at point p n +.
  • the Voigt function in the step (3) is:
  • H G represents Gaussian broadening
  • H L represents Lorentz broadening
  • k is a linear contour scaling factor
  • the invention has the beneficial effects that the low lead content in the tea leaves causes the spectral signal to be weak and the detection is difficult, and the stochastic resonance method is an effective weak signal detection method in the background of strong noise, which converts the noise energy into signal energy, thereby improving
  • the signal-to-noise ratio (SNR) is used to achieve the amplification of weak signals.
  • the method has the advantages of simple, efficient and accurate quantitative analysis of the content of lead metal in heavy metals in tea. It can quickly and accurately quantify weak signals such as lead in tea, and improve lead in tea. Detection ability and efficiency, effectively ensure the quality and safety of tea.
  • Figure 1 is a flow chart for establishing a quantitative model of lead metal heavy metal elements.
  • Figure 2(a) shows the intensity ratio of lead element to carbon element line of tea with different concentrations of lead nitrate.
  • Figure 2(b) shows the intensity ratio of lead element to carbon element line of tea with different concentrations of lead nitrate after Voigt correction.
  • Fig. 3(a) shows the intensity ratio of lead element to carbon element line of tea with different concentrations of lead nitrate after stochastic resonance.
  • Figure 3(b) shows the intensity ratio of lead element to carbon element in different concentrations of lead nitrate after stochastic resonance combined with Voigt correction.
  • Figure 4(a) is a calibration curve of the ratio of the spectral intensity of lead to carbon and the concentration of lead.
  • Figure 4(b) shows the calibration curve of the ratio of the lead element to the carbon element and the lead concentration after Voigt correction.
  • Fig. 5(a) is a calibration curve of the spectral intensity ratio of lead element to carbon element and lead concentration after stochastic resonance.
  • Fig. 5(b) is a calibration curve of the spectral intensity ratio of lead element to carbon element and lead concentration after stochastic resonance combined with Voigt correction.
  • a method for quantitatively analyzing lead elements in tea based on laser induced breakdown spectroscopy comprising the following steps:
  • This experiment uses Longjing tea for analysis. Firstly, the equal-quality Longjing tea leaves are immersed in different concentrations of lead nitrate solution for 35 hours, the contaminated tea leaves are dried, dried at 70 ° C for about 5 hours, and then ground to uniformity. The powder was applied to the tea powder by a mechanical tableting machine at a pressure of 30 MPa for 3 minutes to prepare a sheet sample having a diameter of 15 mm, a thickness of 3 mm and a weight of 0.8 g, and obtained lead nitrate having different mass fractions (0.1%, 0.3%, 0.5%, 0.7%, 0.9%) tea samples.

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Abstract

本发明提供一种基于激光诱导击穿光谱技术定量分析茶叶中铅元素的方法，属于光谱分析领域。该方法通过LIBS设备对含不同铅浓度的茶叶进行初步分析，对照美国标准原子谱线库确定405.78nm处具有铅元素的谱线；然后将LIBS光谱信号数据置于随机共振双稳态系统中，建立随机共振系统方程；随后采用蚁群算法对参数进行优化，采用四阶龙格-库塔算法对优化后的随机共振系统方程进行数值仿真，并使用Voigt函数对光谱进行拟合，获取随机共振放大后的信号谱线；最后构建茶叶中铅浓度与信号谱线之间的定标曲线，从而实现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，简单快速等优点，为茶叶重金属含量分析提供一种参考方法。

Description

一种基于激光诱导击穿光谱技术定量分析茶叶中铅元素的方法 技术领域
本发明属于光谱分析领域，尤其是涉及一种基于激光诱导击穿光谱技术结合随机共振算法实现茶叶中重金属元素铅定量分析的方法。
背景技术
茶作为全球三大天然饮料之一，具有丰富营养价值和保健功能。茶叶中富含氨基酸、茶多酚、咖啡碱等化学成分及多种微量元素，对人体是有益的。随着人们生活质量的提升，人们对茶叶质量也越来越关注。目前影响茶叶质量安全的危害元素主要有：重金属超标、农药残留及有机微生物污染等。其中目前我国环境恶化影响，会导致有害重金属离子污染茶叶，出现茶叶产品的重金属含量超标现象，严重影响茶叶质量安全性，影响茶叶出口国际市场。茶叶中的重金属已成为制约茶叶质量安全的关键性因素。
激光诱导击穿光谱技术(LIBS)是一种光谱分析技术，在对样品组成及含量分析领域具有广泛的应用。LIBS技术是利用激光照射被测物体表面产生等离子体来获取物质成分(定性分析)、浓度(定量分析)和物质识别的分析技术。LIBS相比于传统的光谱分析方法，具有实时、快速、无损或微损检测等特点。
基于随机共振方法实现的微弱信号放大的方法是通过构建双稳态系统，通过蚁群算法优化系统参数a和b确保系统输出信噪比最大。
Voigt拟合方法对系统光谱信号进行拟合和修正，建立茶叶光谱强度与铅元素浓度定标曲线，实现对茶叶重金属铅元素含量检测。
发明内容
针对目前茶叶中重金属铅元素信号微弱导致检测准确度低，检测效率低等 缺点，本申请提供了一种基于激光诱导击穿光谱技术定量分析茶叶中铅元素的方法。
本发明的技术方案：
一种基于激光诱导击穿光谱技术定量分析茶叶中铅元素的方法，包括以下步骤：
(1)茶叶样品制备及光谱信号获取：
将同品种、等质量茶叶分别放入不同浓度的硝酸铅溶液中，进行铅污染处理，将污染后的茶叶干燥、研磨成均匀粉末并制成不同铅浓度茶叶的压片状样品；采用激光诱导击穿光谱检测方法获取压片状样品的光谱信号，并对压片状样品的光谱信号数据以碳元素为参考进行数据归一化处理；
(2)将归一化处理后的数据置于随机共振双稳态系统中，得到随机共振系统方程；使用蚁群算法对随机共振系统方程中的参数进行优化，并采用四阶龙格-库塔算法对优化后的随机共振系统方程进行数值仿真，获取经过随机共振后的放大信号；
(3)Voigt光谱修正：使用Voigt函数对放大信号进行拟合，获取随机共振放大后的信号谱线；
(4)建立定量分析模型：构建茶叶样品中铅浓度与信号谱线之间的定标曲线；并根据定标曲线定量分析实际茶叶样本中铅元素的含量。
所述步骤(2)中随机共振系统方程为：
Figure PCTCN2019077390-appb-000001
<G(t)>＝0,<G(t),G(t-τ)>＝2Dδ(t)
V(x)＝-ax 2/2+bx 4/4
其中，s(t)为模拟信号，V(x)为非线性双稳态系统的势函数，G(t)表示强度为D、均值为0的高斯白噪声，δ(t)为冲激函数，<G(t),G(t-τ)>表示G(t)自相关函数，τ为时间延迟，a和b为大于0系统参数。
所述步骤(2)中采用四阶龙格-库塔算法进行数值仿真的表达式为：
Figure PCTCN2019077390-appb-000002
k 2＝h×(a×(p n+k 1/2)-b×(p n+k 1/2) 3+I n)
k 3＝h×(a×(p n+k 2/2)-b×(p n+k 2/2) 3+I n+1)
k 4＝h×(a×(p n+k 3)-b×(p n+k 3) 3+I n+1)
Figure PCTCN2019077390-appb-000003
其中，M为输入信号长度，h为积分步长，h＝1/f s，f s为采样频率，I n为双稳态输入信号，I 0设置为0，p n为双稳态输出信号，k 1代表p n点处斜率，k 2代表利用k 1求得p n+k 1/2处的斜率，k 3代表利用k 2求得p n+k 2/2处斜率，k 4代表利用k 3求得p n+k 3点处的斜率。
所述步骤(3)中所述Voigt函数为：
Figure PCTCN2019077390-appb-000004
其中，其中H G代表高斯展宽，H L代表洛伦兹展宽，k为线性轮廓比例因子。
本发明的有益效果：茶叶中铅元素含量低导致其光谱信号微弱导致检测困难，随机共振方法是一种有效的强噪声背景下微弱信号检测方法，该方法使得噪声能量转化为信号能量，从而提高信噪比，实现微弱信号的放大；本方法具有简单、高效、准确定量分析茶叶中重金属铅元素含量的优点，可以对茶叶中重金属元素铅等微弱信号进行快速准确定量分析，提高茶叶中铅元素检测能力与效率，有效保证茶叶的质量与安全。
附图说明
图1为建立茶叶重金属元素铅定量模型流程图。
图2(a)为不同硝酸铅浓度茶叶铅元素与碳元素谱线强度比值。
图2(b)为Voigt修正后不同硝酸铅浓度茶叶铅元素与碳元素谱线强度比值。
图3(a)为随机共振后不同硝酸铅浓度茶叶铅元素与碳元素谱线强度比值。
图3(b)为随机共振结合Voigt修正后不同硝酸铅浓度茶叶铅元素与碳元素谱线强度比值
图4(a)为铅元素与碳元素光谱强度比值与铅浓度的定标曲线。
图4(b)为Voigt修正后铅元素与碳元素光谱强比值与铅浓度的定标曲线。
图5(a)为随机共振后铅元素与碳元素光谱强度比值与铅浓度的定标曲线。
图5(b)为随机共振结合Voigt修正后铅元素与碳元素光谱强度比值与铅浓度的定标曲线。
具体实施方式
下面参照附图具体说明发明的一个实施例。
一种基于激光诱导击穿光谱技术定量分析茶叶中铅元素的方法：包括以下步骤：
(1)本实验采用龙井茶叶进行分析，首先将等质量龙井茶叶分别浸泡在不同浓度的硝酸铅溶液中35小时，将污染茶叶进行干燥，在70摄氏度下干燥约5个小时，然后研磨成均匀粉末，使用机械压片机对茶叶粉末施加30Mpa压力，持续3分钟制成直径15mm、厚3mm、重0.8g的片状样品，得到含不同质量分数硝酸铅(0.1％,0.3％,0.5％,0.7％,0.9％)的茶叶样品。
通过一系列预实验，调节影响LIBS光谱分析设备ICCD探测延时时间、门宽和激光能量等参数进行优化。最佳实验条件下获取LIBS光谱数据。以碳元素 作为参考，对铅元素(405.78nm)谱峰进行定量分析。原始的光谱及修正后光谱如图2(a)和图2(b)所示。
(2)将归一化处理后320nm-340nm的光谱信号数据置于双稳态随机共振系统，得到随机共振系统方程；使用蚁群算法对随机共振系统进行参数a和b优化，使信号的输出信噪比最大。采用四阶龙格-库塔算法对不同铅浓度茶叶LIBS光谱经过随机共振后输出信号进行数值仿真，获取经过随机共振后的放大信号。
(3)使用Voigt函数对放大信号进行拟合，获取随机共振放大后的信号谱线；如图3(a)和图3(b)所示。
(4)建立定量分析模型：构建茶叶样品中铅浓度和随机共振放大信号谱线之间的定标曲线。原始光谱信号建立定标曲线如图4(a)和图4(b)所示，随机共振后信号建立定标曲线如图5(a)和图5(b)所示。比较表明随机共振后茶叶定标曲线稳定性和准确性有所提升，定标曲线线性相关性为0.9983，相对均方根误差0.011％。

Claims (5)

  1. 一种基于激光诱导击穿光谱技术定量分析茶叶中铅元素的方法，其特征在于，包括以下步骤：
    (1)茶叶样品制备及光谱信号获取：
    将同品种、等质量茶叶分别放入不同浓度的硝酸铅溶液中，进行铅污染处理，将污染后的茶叶干燥、研磨成均匀粉末并制成不同铅浓度茶叶的压片状样品；采用激光诱导击穿光谱检测方法获取压片状样品的光谱信号，并对压片状样品的光谱信号数据以碳元素为参考进行数据归一化处理；
    (2)将归一化处理后的数据置于随机共振双稳态系统中，得到随机共振系统方程；使用蚁群算法对随机共振系统方程中的参数进行优化，并采用四阶龙格-库塔算法对优化后的随机共振系统方程进行数值仿真，获取经过随机共振后的放大信号；
    (3)Voigt光谱修正：使用Voigt函数对放大的信号进行拟合，获取随机共振放大后的信号谱线；
    (4)建立定量分析模型：构建茶叶样品中铅浓度与信号谱线之间的定标曲线；并根据定标曲线定量分析实际茶叶样本中铅元素的含量。
  2. 根据权利要求1所述的方法，其特征在于，所述步骤(2)中随机共振系统方程为：
    Figure PCTCN2019077390-appb-100001
    <G(t)>＝0,<G(t),G(t-τ)>＝2Dδ(t)
    V(x)＝-ax 2/2+bx 4/4
    其中，s(t)为模拟信号，V(x)为非线性双稳态系统的势函数，G(t)表示强度为D、均值为0的高斯白噪声，δ(t)为冲激函数，<G(t),G(t-τ)>表示G(t)自相关函数，τ为时间延迟，a和b为大于0系统参数。
  3. 根据权利要求1或2所述的方法，其特征在于，所述步骤(2)中采用四阶龙格-库塔算法进行数值仿真的表达式为：
    Figure PCTCN2019077390-appb-100002
    k 2＝h×(a×(p n+k 1/2)-b×(p n+k 1/2) 3+I n)
    k 3＝h×(a×(p n+k 2/2)-b×(p n+k 2/2) 3+I n+1)
    k 4＝h×(a×(p n+k 3)-b×(p n+k 3) 3+I n+1)
    Figure PCTCN2019077390-appb-100003
    其中，M为输入信号长度，h为积分步长，h＝1/f s，f s为采样频率，I n为双稳态输入信号，I 0设置为0，p n为双稳态输出信号，k 1代表p n点处斜率，k 2代表利用k 1求得p n+k 1/2处的斜率，k 3代表利用k 2求得p n+k 2/2处斜率，k 4代表利用k 3求得p n+k 3点处的斜率。
  4. 根据权利要求1或2所述的方法，其特征在于，所述步骤(3)中所述Voigt函数为：
    Figure PCTCN2019077390-appb-100004
    其中，H G代表高斯展宽，H L代表洛伦兹展宽，k为线性轮廓比例因子。
  5. 根据权利要求3所述的方法，其特征在于，所述步骤(3)中所述Voigt函数为：
    Figure PCTCN2019077390-appb-100005
    其中，H G代表高斯展宽，H L代表洛伦兹展宽，k为线性轮廓比例因子。
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