TY - JOUR
T1 - A quantitative analysis method assisted by image features in laser-induced breakdown spectroscopy
AU - Yan, Jiujiang
AU - Hao, Zhongqi
AU - Zhou, Ran
AU - Tang, Yun
AU - Yang, Ping
AU - Liu, Kun
AU - Zhang, Wen
AU - Li, Xiangyou
AU - Lu, Yongfeng
AU - Zeng, Xiaoyan
N1 - Funding Information:
This research was financially supported by the National Natural Science Foundation of China (No. 11874167 ), Innovation Fund of WNLO , the Graduates' Innovation Fund of Huazhong University of Science and Technology (No. 2019ygscxcy038 ), and the Open Project Program of Wuhan National Laboratory for Optoelectronics (NO. 2018WNLOKF002) .
Publisher Copyright:
© 2019 Elsevier B.V.
PY - 2019/11/15
Y1 - 2019/11/15
N2 - The determination accuracy of alloying elements in high alloy steel is generally poor in laser-induced breakdown spectroscopy (LIBS) due to their matrix effect. To solve this problem, an image quantitative analysis (IQA) method was proposed and verified by determining nickel (Ni) in 17 stainless steel samples in this work. The results showed that the coefficient of determination (R2) was increased from 0.9833 of a conventional spectrum quantitative analysis (SQA) method to 0.9996 of the IQA method, and the average relative error of cross-validation (ARECV) and root mean squared error of cross-validation (RMSECV) were decreased from 56.80% and 1.0818 wt% to 15.93% and 0.9866 wt%, respectively. Besides, the determinations of chromium (Cr) and silicon (Si) demonstrated the generalization ability of the IQA. This study provides an effective approach to improving the quantitative performance of LIBS through the combination of image processing and computer vision technology.
AB - The determination accuracy of alloying elements in high alloy steel is generally poor in laser-induced breakdown spectroscopy (LIBS) due to their matrix effect. To solve this problem, an image quantitative analysis (IQA) method was proposed and verified by determining nickel (Ni) in 17 stainless steel samples in this work. The results showed that the coefficient of determination (R2) was increased from 0.9833 of a conventional spectrum quantitative analysis (SQA) method to 0.9996 of the IQA method, and the average relative error of cross-validation (ARECV) and root mean squared error of cross-validation (RMSECV) were decreased from 56.80% and 1.0818 wt% to 15.93% and 0.9866 wt%, respectively. Besides, the determinations of chromium (Cr) and silicon (Si) demonstrated the generalization ability of the IQA. This study provides an effective approach to improving the quantitative performance of LIBS through the combination of image processing and computer vision technology.
KW - Image features
KW - Image quantitative analysis
KW - Laser-induced breakdown spectroscopy
KW - Partial least squares regression
KW - Quantitative analytical performance
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U2 - 10.1016/j.aca.2019.07.058
DO - 10.1016/j.aca.2019.07.058
M3 - Article
C2 - 31472710
AN - SCOPUS:85071352621
SN - 0003-2670
VL - 1082
SP - 30
EP - 36
JO - Analytica Chimica Acta
JF - Analytica Chimica Acta
ER -