基于激光诱导击穿光谱的微生物种类鉴别研究

引用本文: 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. 基于激光诱导击穿光谱的微生物种类鉴别研究[J]. 分析化学, 2018, 46(7): 1122-1128. doi: 10.11895/j.issn.0253-3820.171448 shu

Citation: 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. Discrimination of Microbe Species by Laser Induced Breakdown Spectroscopy[J]. Chinese Journal of Analytical Chemistry, 2018, 46(7): 1122-1128. doi: 10.11895/j.issn.0253-3820.171448 shu

基于激光诱导击穿光谱的微生物种类鉴别研究

1.
江西农业大学工学院, 南昌 330045;
2.
江西省果蔬采后处理关键技术及质量安全协同创新中心, 南昌 330045;
3.
江西省现代农业装备重点实验室, 南昌 330045
基金项目:
本文系国家自然科学基金项目（Nos.31460419，31560482）和江西省研究生创新专项资金项目（No.YC2017-S177）资助

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。

关键词:

English

Discrimination of Microbe Species by Laser Induced Breakdown Spectroscopy

1.
College of Engineering, Jiangxi Agricultural University, Nanchang 330045, China;
2.
Collaborative Innovation Center of Postharvest Key Technology and Quality Safety of Fruits and Vegetables in Jiangxi Province, Nanchang 330045, China;
3.
Jiangxi Key Laboratory of Modern Agricultural Equipment, Nanchang 330045, China
Received Date: 23 November 2017
Revised Date: 14 April 2018
Fund Project:
This work was supported by the National Natural Science Foundation of China (Nos. 31460419, 31560482) and the Graduate Innovative Special Funds Project of Jiangxi Province (No.YC2017-S177).

Abstract: 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 from 10 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 spectras

Key words:

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基于激光诱导击穿光谱的微生物种类鉴别研究

English

Discrimination of Microbe Species by Laser Induced Breakdown Spectroscopy

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通讯作者: 陈斌, bchen63@163.com

中国化学会秘书处

基于激光诱导击穿光谱的微生物种类鉴别研究

English

Discrimination of Microbe Species by Laser Induced Breakdown Spectroscopy

CCS Chemistry：嵌段共聚物自组装成 “三明治”二维有机材料，实现纳米级压力传感功能

CCS Chemistry：颜徐州、鲍哲南： 你的皮肤可能是一片SPMs

CCS Chemistry：工作高效不疲劳，只须让催化剂动起来

CCS Chemistry：静电组装导向聚合- 聚电解质纳米凝胶量化制备新策略

CCS Chemistry：金黄色葡萄球菌醛基脱氢酶是如何识别特异性底物的？

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