|
|
|
|
|
|
| Detection of Pb Element Composition in Irregular Copper Alloy Samples Based on Multi-Line Internal Standard Method |
| ZHU De-hua1, 2, WANG Man-cang1, 2, XU Ling-jie1, 2, CHEN Xiao-jing3, SUN Bing-tao1, 2, ZHANG Jian1, 2, LIU Wen-wen1, 2, CAO Yu1, 2, YUAN Lei-ming3, CAI Yan1* |
1. Institute of Laser and Optoelectronics Intelligent Manufacturing, Wenzhou University, Wenzhou 325000, China
2. College of Mechanical and Electrical Engineering, Wenzhou University, Wenzhou 325035, China
3. College of Mathematics, Physics and Electronic Information Engineering, Wenzhou University, Wenzhou 325035, China |
|
|
|
|
Abstract In-situ analysis or on-line detection is a major advantage of laser-induced breakdown spectroscopy (LIBS) technology. However, in the outdoor environment, people cannot uniformly pre-process samples, then it is difficult to ensure the detection accuracy when facing the various types of samples. In this paper, a multi-line calibration method is proposed to solve the above problem, that is, the calibration curve is established by calculating the intensity ratio of multiple analytical lines and the internal standard element lines, which can reduce the error caused by spectral signal fluctuation and improve linear correlation and detection accuracy. In this experiment, the lead brass alloy samples were taken as an example. The quantitative detection of Pb elements in lead brass samples with different thicknesses (the maximum variation is 2 mm) was carried out by LIBS, and the traditional calibration method and multi-line calibration method were used respectively to establish the calibration curves. The experiment found that for irregular samples, the detection accuracy of the traditional calibration method is very poor, which has no obvious linear relationship from the calibration curve. When the internal calibration method of a single line is adopted, the linear correlation of the calibration curve is greatly improved, and the adjusted determination coefficient reaches 0.724 89. While using the multi-line calibration method (considering the sum of the intensities of multiple analytical lines), it is found that the adjusted determination coefficient of the calibration curve reached 0.984 6 when five Pb lines (Pb 261.42 nm, Pb 280.20 nm, Pb 368.35 nm, Pb 405.78 nm and Pb 520.14 nm) were selected. It can be seen that this method eliminates the spectral intensity fluctuation error caused by sample irregularity and significantly improves the measurement accuracy. While increasing the number of analytical lines can further increase linear correlation, but it also increases the computational complexity, so it is important to choose the appropriate analytical lines. In addition, the multi-line calibration method can also eliminate the matrix effects and spectral interference to a certain extent, which is a simple, effective and universal data processing method. Of course, this method also has limitations (such as extremely heterogeneous sample, extremely irregular sample surface which results in the laser energy below the breakdown threshold, etc.), but by adjusting and optimizing the detection device scheme (for example, increasing laser energy, increasing the diameter of focus spot, using a long-focus lens, etc.), we can improve the advantages of this method. This research content of this paper can provide a new idea for the application of LIBS in-situ analysis and on-line detection.
|
|
Received: 2018-08-30
Accepted: 2018-12-28
|
|
|
|
Corresponding Authors:
CAI Yan
E-mail: 648670467@qq.com
|
|
[1] Awasthi S, Kumar R, Rai A K. J. Appl. Spectrosc., 2017, 84(5): 811.
[2] Hahn D W, Omenetto N. Appl. Spectrosc., 2010, 64(12): 335A.
[3] Qiao S, Ding Y, Tian D, et al. Appl. Spectrosc. Rev., 2015, 50(1): 1.
[4] Remus J J, Harmon R S, Hark R R, et al. Appl. Opt., 2012, 51(7): B65.
[5] LI Yue-sheng, LU Wei-ye, ZHAO Jing-bo, et al(李越胜，卢伟业，赵静波，等). Spectroscopy and Spectral Analysis(光谱学与光谱分析), 2017, 37(8): 2575.
[6] Roux C P M, Rakovsky J, Musset O, et al. Spectrochim. Acta, Part B, 2015, 103: 63.
[7] Zhao C, Dong D, Du X, et al. Sensors, 2016, 16(10): 1764.
[8] Moncayo S, Rosales J D, Izquierdo-Hornillos R, et al. Talanta, 2016, 158: 185.
[9] Zhang T, Xia D, Tang H, et al. Chemometrics and Intelligent Laboratory Systems, 2016, 157: 196.
[10] Meng D, Zhao N, Wang Y, et al. Spectrochim. Acta, Part B, 2017, 137: 39.
[11] Trichard F, Gilon N, Lienemann C P, et al. J. Anal. At. Spectrom., 2016, 31(3): 712.
[12] ZHAO Shu-rui, WANG Hua-li, LU Meng-ke(赵书瑞，王华丽，卢孟柯). Metallurgical Analysis(冶金分析), 2018, 38(7): 33.
[13] ZHANG Lei, MA Wei-guang, YAN Xiao-juan, et al(张 雷，马维光，闫晓娟，等). Acta Sinica Quantum Optica(量子光学学报), 2011, 17(1): 70.
[14] Ji G, Ye P, Shi Y, et al. Sensors, 2017, 17(11): 2655. |
| [1] |
HUANG Bao-kun1*, ZHAO Qian-nan2, LIU Ye-fan2, ZHU Lin1, ZHANG Hong2, ZHANG Yun-hong3*, LIU Yan4*. In Situ Detection of Fuel Engine Exhaust Components by Raman
Integrating Sphere[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(10): 3310-3313. |
| [2] |
YAN Zhong-wei1, 2, 3, TIAN Xi2, 3, ZHANG Yi-fei2, 3, LI Lian-jie2, 3, LIU San-qing1, 2, 3, HUANG Wen-qian2, 3*. Online Detection of Soluble Solids Content in Different Parts of
Watermelons Based on Full Transmission Near Infrared
Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(06): 1800-1808. |
| [3] |
XU Ming-kun, LIN Jia-xiang, ZHANG Xiao-lin, LI Zhen-yin, WANG Ya-ming, LIU Chun-tai, SHEN Chang-yu, SHAO Chun-guang*. In Situ Detection of Structural Evolution of Isotropic Polypropylene Crystals During Uniaxial Stretching by Two-Dimensional X-Ray Diffraction Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(05): 1426-1433. |
| [4] |
TIAN Xi1, 2, 3, CHEN Li-ping2, 3, WANG Qing-yan2, 3, LI Jiang-bo2, 3, YANG Yi2, 3, FAN Shu-xiang2, 3, HUANG Wen-qian2, 3*. Optimization of Online Determination Model for Sugar in a Whole Apple
Using Full Transmittance Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(06): 1907-1914. |
| [5] |
LI Qing-bo1, WEI Yuan1, CUI Hou-xin2, FENG Hao2, LANG Jia-ye2. Quantitative Analysis of TOC in Water Quality Based on UV-Vis Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2022, 42(02): 376-380. |
| [6] |
HAO Yong1, WANG Qi-ming1, ZHANG Shu-min2. Study on Online Detection Method of “Yali” Pear Black Heart Disease Based on Vis-Near Infrared Spectroscopy and AdaBoost Integrated Model[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(09): 2764-2769. |
| [7] |
LIU Juan1, LIU Yu-zhu2, CHU Chen-xi3, BU Ling-bing1*, ZHANG Yang4. In Situ Online Detection of Lignite and Soot by Laser-Induced Breakdown Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(03): 954-960. |
| [8] |
ZHANG Yun-gang1, LIU Huang-tao1, GAO Qiang2, ZHU Zhi-feng2, LI Bo2, WANG Yong-da1. In-Situ Detection of SF6 Decomposition Products Based on Femtosecond Laser-Guided High-Voltage Discharge[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 414-418. |
| [9] |
LIU Yan-yan1, TAO Ning-ping1, 2, 3, WANG Xi-chang1, 2, 3, LU Ying1, 2, 3*，XU Chang-hua1, 2, 3*. Integrated Detection of Foodborne Pathogens by In-Situ Infrared Spectroscopy Based on ZnSe Film Transmission Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2021, 41(02): 419-424. |
| [10] |
KONG Dan-dan, HAN Tong-shuai, GE Qing, CHEN Wen-liang, LIU Rong, LI Chen-xi, XU Ke-xin*. Verification of Signal Extraction Capability of Near-Infrared Non-Invasive Blood Glucose Detection System[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(11): 3438-3442. |
| [11] |
ZHOU Peng, LI Min-zan*, YANG Wei, JI Rong-hua, MENG Chao. Development of Vehicle-Mounted in-situ Soil Parameters Detector Based on NIR Diffuse Reflection[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(09): 2856-2861. |
| [12] |
CHEN Dong-jie1, 2, JIANG Pei-hong1, 2, GUO Feng-jun1, 2, ZHANG Yu-hua1, 2*, ZHANG Chang-feng1, 2. Effects of Prediction Model of Kolar Pear Based on NIR Diffuse Transmission under Different Moving Speed on Online[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1839-1845. |
| [13] |
DING Ji-gang1, HAN Dong-hai1, LI Yong-yu1*, PENG Yan-kun1, WANG Qi1, HAN Xi2. Simultaneous Non-Destructive On-Line Detection of Potato Black-Heart Disease and Starch Content Based on Visible/Near Infrared Diffuse Transmission Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(06): 1909-1915. |
| [14] |
LI Qing-bo1, HE Lin-qian1, CUI Hou-xin2, HAO Long-teng2, SUN Dong-sheng2. Determination of Nitrite Nitrogen Concentration in Surface Water Based on UV-Vis Spectral Analysis Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1127-1131. |
| [15] |
LIU Yan-de, XU Hai, SUN Xu-dong, JIANG Xiao-gang, RAO Yu, ZHANG Yu. Development of Multi-Cultivar Universal Model for Soluble Solid Content of Apple Online Using Near Infrared Spectroscopy[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(03): 922-928. |
|
|
|
|
