A quantitative analysis method assisted by image features in laser-induced breakdown spectroscopy

Jiujiang Yan, Zhongqi Hao, Ran Zhou, Yun Tang, Ping Yang, Kun Liu, Wen Zhang, Xiangyou Li, Yongfeng Lu, Xiaoyan Zeng

Research output: Contribution to journalArticlepeer-review

19 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)30-36
Number of pages7
JournalAnalytica Chimica Acta
StatePublished - Nov 15 2019
Externally publishedYes


  • Image features
  • Image quantitative analysis
  • Laser-induced breakdown spectroscopy
  • Partial least squares regression
  • Quantitative analytical performance

ASJC Scopus subject areas

  • Analytical Chemistry
  • Biochemistry
  • Environmental Chemistry
  • Spectroscopy


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