Abstract：Laser Induced Breakdown Spectroscopy (LIBS) analysis with solid target is greatly affected by the physical morphology and chemical properties of the sample surface. Therefore, the analysis of matrix effect is greatly significant for the study of LIBS online detection. In view of the fact that the current research objects of LIBS are mostly flat samples, in this work, LIBS was used for the quantitative analysis of iron filings with different granularity. The nine kinds of iron filings used in the experiment were loose powder, granules or strips. In order to avoid splashing during the laser ablation of iron filings, the samples were stuck to the double-sided tape for fixing before the experiment. The 1 064 nm laser with the energy of 35 mJ was used as the ablation source. And the delay and gate width of detector were 1 and 10 μs. In order to evaluate the influence of the matrix effect caused by the different particle size of the samples on the LIBS spectrum, firstly, the series of samples were classified by principal component analysis (PCA). The results showed that the four samples in powder form were separated, that is, the matrix effect caused by the difference in particle size was the main reason for the difference in the spectral signals of the sample. Secondly, the elemental characteristic line of Fe Ⅰ 330.635 nm in the C5 sample before and after grinding was taken as the research object. It was found by comparing the intensity and relative standard deviation (RSD) that the smaller the particle size, the stronger the line intensity and the better the stability. In order to correct the interference of the matrix effect on LIBS spectrum, two methods of sample grinding pretreatment and spectral data pretreatment were used. For the sample grinding pretreatment, the slender strip samples of C3 and C5 were grinded in the experiment, and the intensity and stability of the lines were greatly improved after grinding. For the spectral data pretreatment, the intensity normalization, multiple scattering correction (MSC) and the combination of them were studied respectively. The three spectral pretreatments all significantly improved the stability of the line. The quantitative results of Cu element were evaluated and compared by support vector machine (SVM) method. It was found that the calibration results obtained by the grinding samples combined with intensity normalization and MSC pretreatment were the best. Finally, the predicted relative error (RE) of the Cu element in S1 and S2 samples was reduced to 1.745% and 1.857%, respectively, and the root mean square error of prediction (RMSEP) was 0.020. This study can provide a certain method basis and reference for LIBS detection of the samples with irregular surface.
龚停停，田 野，陈 倩，薛博洋，黄甫臻，王林涛，李 颖. 不同颗粒度铁屑LIBS光谱的基体效应及定量分析研究[J]. 光谱学与光谱分析, 2020, 40(04): 1207-1213.
GONG Ting-ting, TIAN Ye, CHEN Qian, XUE Bo-yang, HUANG Fu-zhen, WANG Lin-tao, LI Ying. Matrix Effect and Quantitative Analysis of Iron Filings with Different Particle Size Based on LIBS. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2020, 40(04): 1207-1213.
[1] Wang Z, Yuan T B, Hou Z, et al. Frontiers of Physics, 2014, 9(4): 419.
[2] Lawrence-Snyder M, Scaffidi J P, Pearman W F, et al. Spectrochimica Acta Part B, 2014, 99: 172.
[3] Tian Y, Xue B, Zheng R, et al. Appl. Phys. Lett., 2016, 109(6): 061104.
[4] Guo J, Lu Y, Cheng K, et al. Applied Optics, 2017, 56(29): 8196.
[5] Metzinger A, Kovács-Széles É, Almási I, et al. Applied Spectroscopy, 2014, 68(7): 789.
[6] Meng D, Zhao N, Wang Y, et al. Spectrochimica Acta Part B, 2017, 137: 39.
[7] Tian Y, Yan C, Zhang T, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2017, 135: 91.
[8] Markiewicz-Keszycka M, Cama-Moncunill X, Casado-Gavalda M P, et al. Trends in Food Science & Technology, 2017, 65: 80.
[9] Sun L, Yu H, Cong Z, et al. Spectrochimica Acta Part B, 2018, 142: 29.
[10] Fu H, Dong F, Wang H, et al. Applied Spectrscopy, 2017, 71(8): 1982.
[11] Lazic V, De Ninno A. Spectrochimica Acta Part B: Atomic Spectroscopy, 2017, 137: 28.
[12] Tian Y, Cheung H C, Zheng R, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2016, 124: 16.
[13] Hou Z, Wang Z, Yuan T, et al. Journal of Analytical Atomic Spectrometry, 2016, 31(3): 722.
[14] Bhatt C R, Jain J C, Goueguel C L, et al. Applied Spectroscopy, 2018, 72: 114.
[15] Zhang T, Wu S, Dong J, et al. Journal of Analytical Atomic Spectrometry, 2015, 30(2): 368.
[16] Takahashi T, Thornton B. Spectrochimica Acta Part B: Atomic Spectroscopy, 2017, 138: 31.
[17] Carvalho A A C, Leme F O, Luz M S, et al. Journal of Analytical Atomic Spectrometry, 2018, 33(7): 1243.
[18] Peng J, He Y, Ye L, et al. Analytical Chemistry, 2017, 89(14): 7593.
[19] Guezenoc J, Bassel L, Gallet-Budynek A, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2017, 134: 6.
[20] Li W, Lu J, Dong M, et al. Energy & Fuels, 2017, 32(1): 24.
[21] Moncayo S, Manzoor S, Rosales J D, et al. Food Chemistry, 2017, 232: 322.
[22] Chu Y W, Tang S S, Ma S X, et al. Optics Express, 2018, 26(8): 10119.
[23] ZHANG Tian-long, WU Shan, TANG Hong-sheng, et al(张天龙, 吴 珊, 汤宏胜, 等). Chinese Journal of Analytical Chemistry(分析化学), 2015, 43(6): 939.
[24] Tian Y, Wang Z, Han X, et al. Spectrochimica Acta Part B: Atomic Spectroscopy, 2014, 102: 52.
