|
|
|
|
|
|
| Spectral Features Analysis of Multi-Wavelength Transmission Spectra of Pathogenic Bacterial Microbes in Water |
| GAN Ting-ting1, 2, ZHAO Nan-jing1, 2*, HU Yu-xia1, 2，3, YU Hui-juan1, 2，3, DUAN Jing-bo1, 2, LIU Jian-guo1, 2, LIU Wen-qing1, 2 |
1. Key Laboratory of Environmental Optics and Technology, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei 230031, China
2. Key Laboratory of Optical Monitoring Technology for Environment, Anhui Province, Hefei 230031, China
3. University of Science and Technology of China, Hefei 230026, China |
|
|
|
|
Abstract Multi-wavelength transmission spectrum can reflect the unique information on cell size, shape, internal structure and chemical composition of a sample, so multi-wavelength transmission spectroscopy is a powerful technique for real-time and in-site detection and identification of cells. And the development of multi-wavelength transmission spectroscopy for the rapid and effective detection of bacterial microbes in water will be of great significance to the control of microbial contamination in water and the protection of the safety and health of water quality. In this paper, in order to develop the method of rapidly and accurately detecting the pathogenic bacterial microbes in water based on multi-wavelength transmission spectroscopy, the multi-wavelength transmission spectra in the range of 200～900 nm of various pathogenic bacterial microbes such as Klebsiella pneumoniae, Salmonella typhimurium, Staphylococcus aureus and Escherichia coli were obtained adopting UV-Vis spectrophotometer for the first time, respectively. And the spectral features of different bacteria and the same bacteria with different concentrations were compared and analyzed. The results demonstrate that for different bacteria, when the concentration changes, the spectral shapes in the range of 400～900 nm are consistent, and the optical density value at 400, 450, 500 and 550 nm respectively has a very good linear relationship with bacterial concentration. But in the range of 200～400 nm, the spectral shapes change with the change of bacterial concentration, and the optical density value at 200, 258, 300 and 350 nm respectively has a very good quadratic polynomial relationship with bacterial concentration. According to Mie scattering theory, Levenberg-Marquardt nonlinear least square method was adopted to calculate the scattering spectra and absorption spectra based on the measured transmission spectra of different bacteria. And the spectral features of normalized scattering spectra and absorption spectra of different bacteria were compared and analyzed. The results demonstrate that for the four types of bacteria, all the characteristic spectral peaks of scattering spectra are at 245 nm. But for different bacteria, the optical density values at characteristic peak are obviously different, which arises from the differences in cell size and shape of different bacteria. Furthermore, all the characteristic spectral peaks of absorption spectra of the four types of bacteria are at 260 nm. But for different bacteria, the spectral absorption band between 240～400 nm and spectral intensity at 260 nm are obvious different, which are attributed to the difference in content of chemical composition such as nucleic acid and protein in different bacteria. This study indicates that for the different bacteria and the same bacteria with different concentrations, the spectral features of multi-wavelength transmission spectra, calculated scattering spectra and absorption spectra are obviously different. And various parameters of bacteria can be obtained by the interpretation of multi-wavelength transmission spectra. So multi-wavelength transmission spectroscopy can be used to rapidly and effectively detect pathogenic bacterial microbes in water. This study provides an important basis for the development of rapid and on-line monitoring instrument of bacteria in water.
|
|
Received: 2017-03-13
Accepted: 2017-08-22
|
|
|
|
Corresponding Authors:
ZHAO Nan-jing
E-mail: njzhao@aiofm.ac.cn
|
|
[1] Diduch M, Polkowska Z, Namiesnik J. Food Control, 2016, 61: 188.
[2] Tsuji H, Nomoto K. Annals of Nutrition and Metabolism, 2017, 71: 4.
[3] Palla M, Cristani C, Giovannetti M, et al. International Journal of Food Microbiology, 2017, 250: 19.
[4] Petra A, Brigitte S, Wolfgang B, et al. Critical Care Medicine, 2015: 1492.
[5] Nguyen T T, Trinh K T L, Yoon W J, et al. Sensors and Actuators B: Chemical, 2017, 242: 1.
[6] Park E, Lee C, Bisesi M, et al. Journal of Water and Health, 2014, 12(1): 13.
[7] Yue W W, Bai C J. Journal of Food Safety, 2013, 33(1): 1.
[8] Aleskerova L E, Alenina K A, Efremenko E N, et al. Microbiology, 2014, 83: 315.
[9] Yu Huijuan, Duan Jingbo, Gan Tingting, et al. Spectroscopy and Spectral Analysis, 2017, 37(9): 2667.
[10] Hu Y X, Zhao N J, Gan T T, et al. Acta Optica Sinica, 2017, 37(8): 0830001-1.
[11] Smith J M, Huffman D E, Acosta D, et al. Journal of Biomedical Optics, 2012, 17: 107002-1.
[12] Serebrennikova Y M, Patel J, Garcia-Rubio L H. Applied Optics, 2010, 49: 180.
[13] Martens-Habbena W, Sass H. Applied and Environmental Microbiology, 2006, 72: 87.
[14] Lavrinenko I A, Vashanov G A, Ruban M K. Journal of Applied Spectroscopy, 2014, 80: 899.
[15] Migovich M I, Kelman V A. Optics and Spectroscopy, 2014, 117: 194. |
| [1] |
WANG Hong-peng, WAN Xiong*. Distinguishing Extra Virgin Olive Oil Based on the Correlation Coefficient of Visible Absorption Spectrum of Vegetable Oil[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(09): 2814-2819. |
| [2] |
JIA Li-ge1, 2，ZHANG Bo1*，WEI Huai-dong1, 3. The Spectral Characteristic Variation Analysis of Three Typical Desert Plants in Growing Season[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(09): 2881-2887. |
| [3] |
ZHAO Xu-ting1, 2, 3, ZHANG Shu-juan1, LI Bin2, 3, 4*, LI Yin-kun5. Study on Moisture Content of Soybean Canopy Leaves under Drought Stress Using Terahertz Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2350-2354. |
| [4] |
FENG Cai-ping1, SUI Xiao-feng1, CHEN Chong2, GUO Hui-yuan2, WANG Peng-jie2*. Spectroscopic Analysis of Effect of Sodium Citrate on the Properties of Transglutaminase Goat’s Milk Gels[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2505-2510. |
| [5] |
ZHANG Yu1, 2, LI Jie-qing1, LI Tao3, LIU Hong-gao1*, WANG Yuan-zhong2*. Study on the Geographical Traceability of Boletus Tomentipes Using Multi-Spectra Data Fusion[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2529-2535. |
| [6] |
FU Chen-fei1, 2, LUO Li-qiang1, 2*. Quantitative Analysis, Distribution and Speciation of Elements as Pb, As, Cd in Human Hair from Inhabitants in a Lead-Zinc Mining Area[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(08): 2606-2611. |
| [7] |
HUANG Fei1, 2, YANG Duo3, MENG Lin1, 2，LI Yong-li2, YAN Ying-can2, LIU Kai-jun2, ZHANG Bao-yun1. Influence of Doping Co Amount on the Crystal Structure Characteristics and Light Absorption Properties of Nano-Micrometer Pyrite Prepared by Thermal Sulfide Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2004-2009. |
| [8] |
LI Zheng-hui1，3, YAO Shun-chun1，3*, LU Wei-ye2, ZHU Xiao-rui1，3, ZOU Li-chang1，3, LI Yue-sheng2, LU Zhi-min1，3. Study on Temperature Correction Method of CO2 Measurement by TDLAS[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2048-2053. |
| [9] |
YANG Ke-ming, ZHANG Wei, WANG Xiao-feng, SUN Tong-tong, CHENG Long. Differentiation and Level Monitoring of Corn Leaf Stressed by Cu and Pb Derived from Spatial Spectrum[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2200-2208. |
| [10] |
TIAN Yuan-sheng1, ZHANG Yue1, SUN Wen-yi1, 2*, MU Xing-min1, 2, GAO Peng1, 2, ZHAO Guang-ju1, 2. Spectral Characteristics of Biological Soil Crusts under the Different Types in the Water-Wind Erosion Crisscross Region on the Loess Plateau[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2215-2220. |
| [11] |
XIE Jun1, ZHAO Ya-nan1, CHEN Xuan-jing1, WANG Ke1, XU Chun-li1, LI Dan-ping1, ZHANG Yue-qiang1, 2, WANG Ding-yong1, SHI Xiao-jun1, 2*. Effect on Soil DOM Content and Structure Characteristics in Different Soil Layers by Long-Term Fertilizations[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(07): 2250-2255. |
| [12] |
ZHONG Qian1, 2, 3, WU Qiong2, 3, LIAO Zong-ting1, 2, 3*, ZHOU Zheng-yu1, 2, 3. Vibrational Spectral Characteristics of Ensignia Actinolite Jade from Guangxi, China[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1786-1792. |
| [13] |
ZHANG Yu-feng1, LI Ming1, DAI Jing-min2, SHAO Zhu-feng1, WU Yuan-qing1. Measuring Method of the Spectral Absorptivity for Solar Selective Absorption Coatings at High Temperature[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(06): 1814-1818. |
| [14] |
SHI Dong-chen，HUA Deng-xin*，LEI Ning, GAO Fei, WANG Li, YAN Qing, ZHOU Yi. Research of Solar-Blind Ultraviolet Raman Lidar for Water Vapor Measurement Technology[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1430-1436. |
| [15] |
MIAO Quan-long1, DAI Lei1, LI Bai-cheng1, ZHAO Tian-qi1, LIANG Kun1, 2, YANG Ru1, 2, HAN De-jun1,2*. Time-Resolved Raman Scattering Measurement Based on SiPM and TCMPC Method[J]. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2018, 38(05): 1444-1450. |
|
|
|
|
