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基于激光诱导击穿光谱的微生物种类鉴别研究
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  • 英文篇名:Discrimination of Microbe Species by Laser Induced Breakdown Spectroscopy
  • 作者:饶刚福 ; 黄林 ; 刘木华 ; 陈添兵 ; 陈金印 ; 罗子奕 ; 许方豪 ; 杨晖 ; 何秀文 ; 周华茂 ; 林金龙 ; 姚明印
  • 英文作者:RAO Gang-Fu;HUANG Lin;LIU Mu-Hua;CHEN Tian-Bing;CHEN Jin-Yin;LUO Zi-Yi;XU Fang-Hao;YANG Hui;HE Xiu-Wen;ZHOU Hua-Mao;LIN Jin-Long;YAO Ming-Yin;College of Engineering,Jiangxi Agricultural University;Jiangxi Key Laboratory of Modern Agricultural Equipment;Collaborative Innovation Center of Postharvest Key Technology and Quality Safety of Fruits andVegetables in Jiangxi Province;
  • 关键词:微生物 ; 快速鉴别 ; 等离子羽 ; 激光诱导击穿光谱 ; 随机森林 ; 主成分分析
  • 英文关键词:Microbes;;Rapid identification;;Plasma plume;;Laser induced breakdown spectroscopy;;Random forest;;Principal component analysis
  • 中文刊名:FXHX
  • 英文刊名:Chinese Journal of Analytical Chemistry
  • 机构:江西农业大学工学院;江西省现代农业装备重点实验室;江西省果蔬采后处理关键技术及质量安全协同创新中心;
  • 出版日期:2018-07-12
  • 出版单位:分析化学
  • 年:2018
  • 期:v.46
  • 基金:国家自然科学基金项目(Nos.31460419,31560482);; 江西省研究生创新专项资金项目(No.YC2017-S177)资助~~
  • 语种:中文;
  • 页:FXHX201807021
  • 页数:7
  • CN:07
  • ISSN:22-1125/O6
  • 分类号:138-144
摘要
建立了激光诱导击穿光谱(Laser induced breakdown spectroscopy,LIBS)全光学诊断方法,对微生物种类进行快速鉴别。制取10种微生物样品,优选滤纸为富集载体,采集等离子体羽时间演变形貌图及LIBS光谱指纹图分析了鉴别微生物种类的可行性;运用九点平滑(Nine smooth,9SM)、多元散射校正(Multiple scatter correction,MSC)对波长范围200~420 nm和560~680 nm微生物LIBS全谱数据进行了预处理;分析比较了主成分分析(Principal component analysis,PCA)、随机森林结合主成分分析(Random forest combined with principal component analysis,PCA-RF)两种方法对微生物种类的鉴别结果。结果表明,运用一定的数据预处理方法,采用PCA-RF算法对10类微生物种类鉴别,训练集总准确率为99.6%,预测集总准确率为96.7%,说明选择合适的LIBS光谱预处理及模型构建方法,对微生物种类的快速准确鉴别具有可行性。
        Laser induced breakdown spectroscopy(LIBS) was proposed to rapidly discriminate microbe species. Ten species of microbes were prepared in lab. Filter papers were selected as substrate for enriching bacteria and enhancing the quality of LIBS. The images of plasma were collected by ICCD camera and LIBS spectra were obtained by spectrometers. The results displayed that the images and spectra were different from10 bacteria. It was demonstrated that this method was feasible to discriminate bacteria species by analyzing image and/or spectroscopy. Furthermore,nine smooth and multiple scattering correction(MSC) were utilized to preprocess the LIBS full-spectrum data in the wavelength range of 200-420 nm and 560-680 nm. And principal component analysis(PCA) and PCA-RF(Random forest) were compared to validate the accuracy of discrimination. The investigation showed that the PCA-RF model coupled with suitable methods in preprocessing data could identify bacteria. The accuracy was 99.6% for ten species of microbes by evaluating LIBS spectra in training set,and 96. 7% in predicting set. This report indicated that it is feasible to differentiate bacteria species by analyzing LIBS spectra.
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