Chinese Journal of Quantum Electronics ›› 2022, Vol. 39 ›› Issue (4): 494-501.doi: 10.3969/j.issn.1007-5461.2022.04.003

• Spectroscopy • Previous Articles     Next Articles

Real-time detection of the genus Rosa L. using LIBS technology

YU Wei1, ZHOU Zhuoyan1, SUN Zhongmou1, ZHANG Xinglong1, LIU Yuzhu1;2;3*   

  1. ( 1 Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean, Nanjing University of Information Science & Technology, Nanjing 210044, China; 2 Jiangsu Collaborative Innovation Center on Atmosphere Environment and Equipment Technology, Nanjing 210044, China; 3 Jiangsu International Joint Laboratory on Meterological Photonics and Optoelectronic Detection, Nanjing University of Information Science & Technology, Nanjing 210044, China )
  • Received:2021-04-26 Revised:2021-06-11 Published:2022-07-28 Online:2022-07-28

Abstract: Rosa rugosa Thunb., Rosa sp. and Rosa chinensis Jacq. all belong to the genus Rosa L. The three kinds of flowers are similar in appearance and easily confused. The genus Rosa L. has important ornamental value and medicinal value, so it is of great significance to quickly identify the genus Rosa L. Laser induced breakdown spectroscopy (LIBS) is used for in situ detection of the main elements in the three flowers in this work. In addition, CN can also be recognized in the spectrum of the genus Rosa L, so the CN in the spectrum is simulated by using LIFBASE, and the vibration temperature and rotation temperature of CN are calculated. Then the parameters obtained can be regarded as experimental parameters. By comparing and analyzing the laser induced breakdown spectra of the three different kinds of flowers, the characteristic spectral lines with significant difference in intensity are selected as variables to predict flower genus, and combined with the general regression neural network (GRNN), the prediction accuracy could reach 93.3%.

Key words: spectroscopy, detection of the genus Rosa L., laserinduced breakdown spectroscopy, general regression neural network

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