基于激光诱导击穿光谱的微生物种类鉴别研究
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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.
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.
引文
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2 Zieliński B,Plichta A,Misztal K,Spurek P,Brzychczy-Woch M,Ochońska D.Plos One,2017,12(9):e0184554
3 WANG Jiu-Yue,ZHAO Nan-Jing,DUAN Jing-Bo,MENG De-Shuo,FANG Li,YANG Rui-Fang,XIAO Xue,YIN GaoFang,MA Ming-Jun,LIU Jian-Guo,LIU Wen-Qing.Spectroscopy and Spectral Analysis,2017,37(2):333-337王久悦,赵南京,段静波,孟德硕,方丽,杨瑞芳,肖雪,殷高方,马明俊,刘建国,刘文清.光谱学与光谱分析,2017,37(2):333-337
4 Uusitalo S,Popov A,Ryabchikov Y V,Bibikova O,Alakomi H,Juvonen R,Kontturi V,Siitonen S,Kabashin A,Meglinski I,Hiltunen J,Laitila A.J.Food Engineer.,2017,212:47-54
5 YANG Chun,JIA Yun-Hai,WANG Hui,LI Dong-Ling,QU Hua-Yang,SHEN Xue-Jing,CHEN Ji-Wen.Chinese J.Anal.Chem.,2018,46(2):265-272杨春,贾云海,王辉,李冬玲,屈华阳,沈学静,陈吉文.分析化学,2018,46(2):265-272
6 Yang P,Zhu Y N,Yang X Y,Li J M,Tang SS,Hao Z Q,Guo L B,Li X Y,Zeng X Y,Lu Y F.J.Cereal Sci.,2018,80:111-118
7 ZHU Yi-Ning,YANG Ping,YANG Xin-Yan,LI Jia-Ming,HAO Zhong-Qi,LI Qiu-Shi,GUO Lian-Bo,LI Xiang-You,ZENG Xiao-Yan,LU Yong-Feng.Chinese J.Anal.Chem.,2017,45(3):336-341朱毅宁,杨平,杨新艳,李嘉铭,郝中骐,李秋实,郭连波,李祥友,曾晓雁,陆永枫.分析化学,2017,45(3):336-341
8 Velioglu H M,Sezer B,Bilge G,Baytur S E,Boyaci I H.Meat Sci.,2018,138:28-33
9 WANG Qian-Qian,ZHAO Yu,LU Xiao-Dang,PENG Zhong,LIU Li.Spectroscopy and Spectral Analysis,2017,37(8):2430-2434王茜蒨,赵宇,卢小刚,彭中,刘莉.光谱学与光谱分析,2017,37(8):2430-2434
10 Liu X N,Ma Q,Liu SS,Shi X Y,Zhang Q,Wu Z S,Qiao Y J.Spectrochim.Acta A,2015,151(5):547-552
11 Beldjilali S A,Axente E,Belasri A,Baba-Hamed T,Hermann J.J.Appl.Spectrom.,2017,84(3):472-477
12 Ponce L V,Flores T,Alvira F C,Bilmes G M,Sosa-Salda1a M.Appl.Optics,2016,55(2):254-258
13 Yongcheng J,Jiang H,Benchi J,Dong L.J.Appl.Spectrom.,2017,84(1):103-108
14 Shukla N,Bharti A S,Srivastava S,Uttam K N.Natl.Acad.Sci.Lett.,2017,40(1):47-51
15 Bhatt C R,Alfarraj B,Ghany C T,Yueh F Y,Singh J P.Appl.Spectrom.,2017,71(4):686-698
16 Campbell K R,Wozniak N R,Colgan J P,Judge E J,Barefield II J E,Kilcrease D P,Wilkerson M P,Ken R.Spectrochim.Acta B,2017,134:91-97
17 Williams A N,Phongikaroon S.Appl.Spectrom.,2017,71(4):744-749
18 Fambro L A,Vandenbos D D,Rosenberg M B,Dockery C R.Appl.Spectrom.,2017,71(4):699-708
19 Mohaidat Q,Palchaudhuri S,Rehse S J.Appl.Spectrom.,2011,65(4):386-392
20 Manzoor S,Moncayo S,Navarro-Villoslada F,Ayala J A,Izquierdo-Hornillos R,de Villena F J,Manuel Caceres J O.Talanta,2014,121(3):65-70
21 WANG Jing-Ge,FU Hong-Bo,NI Zhi-Bo,HE Wen-Gan,CHEN Xing-Long,DONG Feng-Zhong.Spectroscopy and Spectral Analysis,2016,36(3):817-822王静鸽,付洪波,倪志波,贺文干,陈兴龙,董凤忠.光谱学与光谱分析,2016,36(3):817-822