Low-cost smartphone-based LIBS combined with deep learning image processing for accurate lithology recognition.

Journal: Chemical communications (Cambridge, England)
Published Date: Jul 20, 2021

Abstract

A low-cost and multi-channel smartphone-based spectrometer was developed for LIBS. As the CMOS detector is two-dimensional, simultaneous multichannel detection was achieved by coupling a linear array of fibres for light collection. Thus, besides the atomic information, the spectral images containing the propagation and spatial distribution characters of a laser induced plasma plume could be recorded. With these additional features, accurate rock type prediction was achieved by processing the raw data directly through a deep learning model.

Authors

  • Xu Wang
    Weldon School of Biomedical Engineering, Purdue University, West Lafayette, IN 47907.
  • Sha Chen
    Research Centre of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, Chengdu, China 610065, P. R. China. yduan@scu.edu.cn.
  • Mengfan Wu
    College of Life Sciences, Key Laboratory of Bio-resource and Eco-environment, Ministry of Education, Sichuan University, Chengdu, China 610065, P. R. China.
  • Ruiqin Zheng
    Research Centre of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, Chengdu, China 610065, P. R. China. yduan@scu.edu.cn.
  • Zhuo Liu
    Department of Urology, Tongji Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Zhongjun Zhao
    College of Chemical Engineering, Sichuan University, Chengdu, China 610065, P. R. China. tompson_2006@163.com.
  • Yixiang Duan
    Research Centre of Analytical Instrumentation, School of Mechanical Engineering, Sichuan University, Chengdu, China 610065, P. R. China. yduan@scu.edu.cn.

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