激光诱导击穿光谱技术实时检测蔷薇属植物  

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

作　　者：于玮 周卓彦 孙仲谋 张兴龙 刘玉柱[1,2,3] YU Wei;ZHOU Zhuoyan;SUN Zhongmou;ZHANG Xinglong;LIU Yuzhu(Jiangsu Key Laboratory for Optoelectronic Detection of Atmosphere and Ocean,Nanjing University of Information Science&Technology,Nanjing 210044,China;Jiangsu Collaborative Innovation Center on Atmosphere Environment and Equipment Technology,Nanjing 210044,China;Jiangsu International Joint Laboratory on Meterological Photonics and Optoelectronic Detection,Nanjing University of Information Science&Technology,Nanjing 210044,China)

机构地区：[1]南京信息工程大学,江苏省大气海洋光电探测重点实验室,江苏南京210044 [2]江苏省大气环境与装备技术协同创新中心,江苏南京210044 [3]南京信息工程大学,江苏省气象光子学与光电探测国际合作联合实验室,江苏南京210044

出　　处：《量子电子学报》2022年第4期494-501,共8页Chinese Journal of Quantum Electronics

基　　金：国家自然科学基金(U1932149);江苏省研究生实践创新计划项目(SJCX22−0347);(江苏省高校“青蓝工程”人才项目)。

摘　　要：玫瑰、蔷薇和月季三种花同属于蔷薇属植物,这三种花外貌相似,极易混淆。蔷薇属植物具有重要的观赏价值、药用价值等,因此快速鉴别蔷薇属植物具有重要意义。利用激光诱导击穿光谱(LIBS)技术对三种花进行原位在线检测,可以识别三种花中的主要元素。通过植物中所含元素的差异以及同一元素对应谱线的相对强度的差异,可以辨别玫瑰、蔷薇和月季。此外,在蔷薇属植物光谱中还可以探测到CN自由基,利用LIFBASE软件模拟光谱中的CN,计算CN的振动温度和转动温度,得到的参数可以视为实验参数。在对比分析三种不同花的激光诱导击穿光谱后,选择强度差异显著的特征谱线作为变量,结合广义回归神经网络(GRNN)对花属进行预测,正确率可达93.3%。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%.

关 键 词：光谱学 检测蔷薇属植物 激光诱导击穿光谱 广义回归神经网络 

分 类 号：O433.4[机械工程—光学工程] TN249[理学—光学]