Citation: Mubashir Hussain, Zhen Chen, Mu Lv, Jingyi Xu, Xiaohan Dong, Jingzhou Zhao, Song Li, Yan Deng, Nongyue He, Zhiyang Li, Bin Liu. Rapid and label-free classification of pathogens based on light scattering, reduced power spectral features and support vector machine[J]. Chinese Chemical Letters, ;2020, 31(12): 3163-3167. doi: 10.1016/j.cclet.2020.04.038 shu

Rapid and label-free classification of pathogens based on light scattering, reduced power spectral features and support vector machine

Mubashir Hussain ^a ,
Zhen Chen ^b ,
Mu Lv ^b ,
Jingyi Xu ^b ,
Xiaohan Dong ^b ,
Jingzhou Zhao ^a ,
Song Li ^d ,
Yan Deng ^d ,
Nongyue He ^{a, d, *,} ,
Zhiyang Li ^{c, *,} ,
Bin Liu ^{b, *,}

a.
State Key Laboratory of Bioelectronics, School of Biological Science and Medical Engineering, Southeast University, Nanjing 210096, China
b.
Key Laboratory of Clinical and Medical Engineering, Department of Biomedical Engineering, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing 211166, China
c.
Department of Clinical Laboratory, The Affiliated Drum Tower Hospital of Nanjing University Medical School, Nanjing 210008, China
d.
Hunan Key Laboratory of Biomedical Nanomaterials and Devices, Hunan University of Technology, Zhuzhou 412007, China

nyhe1958@163.com

lizhiyangcn@qq.com

liubin@njmu.edu.cn

Received Date: 9 February 2020
Revised Date: 21 April 2020
Accepted Date: 22 April 2020
Available Online: 14 May 2020

Figures(5)

The rapid identification of pathogens is crucial in controlling the food quality and safety. The proposed system for the rapid and label-free identification of pathogens is based on the principle of laser scattering from the bacterial microbes. The clinical prototype consists of three parts: the laser beam, photodetectors, and the data acquisition system. The bacterial testing sample was mixed with 10 mL distilled water and placed inside the machine chamber. When the bacterial microbes pass by the laser beam, the scattering of light occurs due to variation in size, shape, and morphology. Due to this reason, different types of pathogens show their unique light scattering patterns. The photo-detectors were arranged at the surroundings of the sample at different angles to collect the scattered light. The photodetectors convert the scattered light intensity into a voltage waveform. The waveform features were acquired by using the power spectral characteristics, and the dimensionality of extracted features was reduced by applying minimal-redundancy-maximal-relevance criterion (mRMR). A support vector machine (SVM) classifier was developed by training the selected power spectral features for the classification of three different bacterial microbes. The resulting average identification accuracies of E. faecalis, E. coli and S. aureus were 99%, 87%, and 94%, respectively. The overall experimental results yield a higher accuracy of 93.6%, indicating that the proposed device has the potential for label-free identification of pathogens with simplicity, rapidity, and cost-effectiveness.
- Pathogens identification,
- Laser light scattering,
- Features reduction,
- Support vector machines,
- Waveform features extraction
1. [1]
  X.W. Wang, L. Zhang, L.Q, et al., Appl. Microbiol. Biotechnol. 76(2007) 225-233. doi: 10.1007/s00253-007-0993-x
2. [2]
  A.K. Jha, H.P. Bais, J.M. Vivanco, Infect. Immun. 73(2005) 464-475. doi: 10.1128/IAI.73.1.464-475.2005
3. [3]
  S. Wang, Y. Zhang, L. Zhang, et al., Chin. Chem. Lett. 29(2018) 1513-1516. doi: 10.1016/j.cclet.2018.08.002
4. [4]
  J.B. Kaper, J.P. Nataro, H.L.T. Mobley, Nat. Rev. Microbiol. 2(2004) 123-140. doi: 10.1038/nrmicro818
5. [5]
  Z. Hong, D. Chen, W. Zhou, et al., Nanosci. Nanotechnol. Lett. 10(2018) 1606-1612. doi: 10.1166/nnl.2018.2809
6. [6]
  J.R. Johnson, T.A. Russo, J. Lab, Clin. Med. 139(2002) 155-162. doi: 10.1067/mlc.2002.121550
7. [7]
  R.A. Weinstein, M.J.M. Bonten, D.J. Austin, M. Lipsitch, Clin. Infect. Dis. 33(2001) 1739-1746. doi: 10.1086/323761
8. [8]
  S. Yang, R.E. Rothman, Lancet Infect. Dis. 4(2004) 337-348. doi: 10.1016/S1473-3099(04)01044-8
9. [9]
  Y. Yamamoto, Clin. Diagn. Lab. Immunol. 9(2002) 508-514. doi: 10.1128/CDLI.9.3.508-514.2002
10. [10]
  D. Zhou, J. Luo, T. Sun, et al., Nanosci. Nanotechnol. Lett. 11(2019) 136-142. doi: 10.1166/nnl.2019.2861
11. [11]
  P. Dakrong, H. Yusuke, Nano-Micro Lett. 9(2017) 113-121.
12. [12]
  W. Li, A. Nie, Q. Li, et al., Mater. Express 9(2019) 484-491. doi: 10.1166/mex.2019.1511
13. [13]
  J. Deng, L. Fu, R. Wang, et al., J. Thorac. Dis. 6(2014) 539-544. doi: 10.3978/j.issn.2072-1439.2014.02.20
14. [14]
  L.M. Schlecht, B.M. Peters, B.P. Krom, et al., Microbiology 161(2015) 168-181. doi: 10.1099/mic.0.083485-0
15. [15]
  L. Wang, Q. Lin, J. Chen, et al., Nanosci. Nanotechnol. Lett.11(2019) 998-1003. doi: 10.1166/nnl.2019.2970
16. [16]
  L. Huang, D. Zhang, L. Jiao, et al., Chin. Chem. Lett. 29(2018) 1853-1856. doi: 10.1016/j.cclet.2018.11.028
17. [17]
  D.L. Haavig, K.R. Hollen, A.E. Debruin, et al., J. AOAC Int.100(2017) 1836-1847. doi: 10.5740/jaoacint.17-0097
18. [18]
  M. Hussain, M. Lv, J. Xu, et al., IEEE International Symposium on Medical Measurements and Applications, Istanbul, Turkey, 2019.
19. [19]
  M. Hussain, M. Lv, X. Dong, et al., J. Nanosci. Nanotechnol. 20(2020) 4047-4056. doi: 10.1166/jnn.2020.17491
20. [20]
  R.N. Khushaba, M. Takruri, J.V. Miro, S. Kodagoda, Neural Netw. 55(2014) 42-58. doi: 10.1016/j.neunet.2014.03.010
21. [21]
  R.N. Khushaba, L. Shi, S. Kodagoda, IEEE International Symposium on Communications & Information Technologies, Gold Coast, Australia, 2012.
22. [22]
  A.H. Al-Timemy, R.N. Khushaba, G. Bugmann, J. Escudero, IEEE T. Neur. Sys.Reh. 24(2016) 650-661. doi: 10.1109/TNSRE.2015.2445634
23. [23]
  R.N. Khushaba, A. Al-Ani, A. Al-Jumaily, Expert Syst. Appl. 38(2011) 11515-11526.
24. [24]
  P. Hanchuan, L. Fuhui, C. Ding, IEEE Trans. Pattern Anal. Mach. Intell. 27(2005) 1226-1238.
25. [25]
  S. Raj, K.C. Ray, IEEE T. Instrum. Meas. 66(2017) 470-478. doi: 10.1109/TIM.2016.2642758
26. [26]
  M.A. Aslam, C. Xue, K. Wang, et al., Nano Biomed. Eng. 12(2020) 1-3.
27. [27]
  W. Yan, K. Wang, H. Xu, et al., Nano-Micro Lett. 11(2019) 7.
28. [28]
  C.C. Chang, C.J. Lin, ACM T. Intel. Syst. Tec. 2(2011) 27.
29. [29]
  N. Goudarzi, M. Goodarzi, M.A. Chamjangali, et al., Chin. Chem. Lett. 24(2013) 904-908.
1. [1]
  Jie Ren , Hao Zong , Yaqun Han , Tianyi Liu , Shufen Zhang , Qiang Xu , Suli Wu . Visual identification of silver ornament by the structural color based on Mie scattering of ZnO spheres. Chinese Chemical Letters, 2024, 35(9): 109350-. doi: 10.1016/j.cclet.2023.109350
2. [2]
  Doudou Liu , Weiwei Guo , Guoliang Mei , Youpeng Dan , Rong Yang , Chao Huang , Yanling Zhai , Xiaoquan Lu . Application of catalyst Cu-t-ZrO₂ based on the electronic metal-support interaction in electrocatalytic nitrate reduction. Chinese Chemical Letters, 2025, 36(8): 110578-. doi: 10.1016/j.cclet.2024.110578
3. [3]
  Ying Hou , Zhen Liu , Xiaoyan Liu , Zhiwei Sun , Zenan Wang , Hong Liu , Weijia Zhou . Laser constructed vacancy-rich TiO_2-x/Ti microfiber via enhanced interfacial charge transfer for operando extraction-SERS sensing. Chinese Chemical Letters, 2024, 35(9): 109634-. doi: 10.1016/j.cclet.2024.109634
4. [4]
  Jin Long , Xingqun Zheng , Bin Wang , Chenzhong Wu , Qingmei Wang , Lishan Peng . Improving the electrocatalytic performances of Pt-based catalysts for oxygen reduction reaction via strong interactions with single-CoN₄-rich carbon support. Chinese Chemical Letters, 2024, 35(5): 109354-. doi: 10.1016/j.cclet.2023.109354
5. [5]
  Gaowa Xing , Yuting Shang , Xiaorui Wang , Zengnan Wu , Qiang Zhang , Jiebing Ai , Qiaosheng Pu , Ling Lin . A microfluidic biosensor for multiplex immunoassay of foodborne pathogens agitated by programmed audio signals. Chinese Chemical Letters, 2024, 35(10): 109491-. doi: 10.1016/j.cclet.2024.109491
6. [6]
  Ziruo Zhou , Wenyu Guo , Tingyu Yang , Dandan Zheng , Yuanxing Fang , Xiahui Lin , Yidong Hou , Guigang Zhang , Sibo Wang . Defect and nanostructure engineering of polymeric carbon nitride for visible-light-driven CO2 reduction. Chinese Journal of Structural Chemistry, 2024, 43(3): 100245-100245. doi: 10.1016/j.cjsc.2024.100245
7. [7]
  Zhenfei Tang , Yunwu Zhang , Zhiyuan Yang , Haifeng Yuan , Tong Wu , Yue Li , Guixiang Zhang , Xingzhi Wang , Bin Chang , Dehui Sun , Hong Liu , Lili Zhao , Weijia Zhou . Iron-doping regulated light absorption and active sites in LiTaO₃ single crystal for photocatalytic nitrogen reduction. Chinese Chemical Letters, 2025, 36(3): 110107-. doi: 10.1016/j.cclet.2024.110107
8. [8]
  Chaohui Zheng , Jing Xi , Shiyi Long , Tianpei He , Rui Zhao , Xinyuan Luo , Na Chen , Quan Yuan . Persistent luminescence encoding for rapid and accurate oral-derived bacteria identification. Chinese Chemical Letters, 2025, 36(1): 110223-. doi: 10.1016/j.cclet.2024.110223
9. [9]
  Li Li , Fanpeng Chen , Bohang Zhao , Yifu Yu . Understanding of the structural evolution of catalysts and identification of active species during CO₂ conversion. Chinese Chemical Letters, 2024, 35(4): 109240-. doi: 10.1016/j.cclet.2023.109240
10. [10]
  Huanyu Liu , Gang Yu , Ruoyao Guo , Hao Qi , Jiayin Zheng , Tong Jin , Zifeng Zhao , Zuqiang Bian , Zhiwei Liu . Direct identification of energy transfer mechanism in Ce^Ⅲ-Mn^Ⅱ system by constructing molecular heteronuclear complexes. Chinese Chemical Letters, 2025, 36(2): 110296-. doi: 10.1016/j.cclet.2024.110296
11. [11]
  Ting Huang , Xiaohua Zhu , Meiling Liu , Haitao Li , Youyu Zhang , Yang Liu , Shouzhuo Yao . Bioinspired interface-mediated multichannel sensor array for rapid and robust identification of bacteria. Chinese Chemical Letters, 2025, 36(8): 110530-. doi: 10.1016/j.cclet.2024.110530
12. [12]
  Xinyi Zhao , Yuai Duan , Zihan Liu , Hua Geng , Yaping Li , Zhongfeng Li , Tianyu Han . Mapping sweat pores for biometric identification based on a donor-acceptor hydrophilic fluorescent probe. Chinese Chemical Letters, 2025, 36(8): 110617-. doi: 10.1016/j.cclet.2024.110617
13. [13]
  Tian Cao , Xuyin Ding , Qiwen Peng , Min Zhang , Guoyue Shi . Intelligent laser-induced graphene sensor for multiplex probing catechol isomers. Chinese Chemical Letters, 2024, 35(7): 109238-. doi: 10.1016/j.cclet.2023.109238
14. [14]
  Zhi Wang , Lingpeng Yan , Yelin Hao , Jingxia Zheng , Yongzhen Yang , Xuguang Liu . Highly efficient and photothermally stable CDs@ZIF-8 for laser illumination. Chinese Chemical Letters, 2024, 35(10): 109430-. doi: 10.1016/j.cclet.2023.109430
15. [15]
  Ce Liang , Qiuhui Sun , Adel Al-Salihy , Mengxin Chen , Ping Xu . Recent advances in crystal phase induced surface-enhanced Raman scattering. Chinese Chemical Letters, 2024, 35(9): 109306-. doi: 10.1016/j.cclet.2023.109306
16. [16]
  Zhenxing Li , Yue Ding , Xinxin Tuo , Jinhong Hu , Taihong Zhang , Xiang Zhou , Liwei Liu , Song Yang . Structure-based drug repurposing targeting pathogenic virus superfamily 1 helicase: An integrated multi-computational screening and bioactivity identification strategy. Chinese Chemical Letters, 2025, 36(9): 110737-. doi: 10.1016/j.cclet.2024.110737
17. [17]
  Jing Chen , Peisi Xie , Pengfei Wu , Yu He , Zian Lin , Zongwei Cai . MALDI coupled with laser-postionization and trapped ion mobility spectrometry contribute to the enhanced detection of lipids in cancer cell spheroids. Chinese Chemical Letters, 2024, 35(4): 108895-. doi: 10.1016/j.cclet.2023.108895
18. [18]
  Chengde Wang , Liping Huang , Shanshan Wang , Lihao Wu , Yi Wang , Jun Dong . A distinction of gliomas at cellular and tissue level by surface-enhanced Raman scattering spectroscopy. Chinese Chemical Letters, 2024, 35(5): 109383-. doi: 10.1016/j.cclet.2023.109383
19. [19]
  Cheng Guo , Xiaoxiao Zhang , Xiujuan Hong , Yiqiu Hu , Lingna Mao , Kezhi Jiang . Graphene as adsorbent for highly efficient extraction of modified nucleosides in urine prior to liquid chromatography-tandem mass spectrometry analysis. Chinese Chemical Letters, 2024, 35(4): 108867-. doi: 10.1016/j.cclet.2023.108867
20. [20]
  Shu Tian , Wenxin Huang , Junrui Hu , Huiling Wang , Zhipeng Zhang , Liying Xu , Junrong Li , Yao Sun . Exploring the frontiers of plant health: Harnessing NIR fluorescence and surface-enhanced Raman scattering modalities for innovative detection. Chinese Chemical Letters, 2025, 36(3): 110336-. doi: 10.1016/j.cclet.2024.110336

Get Citation

PDF

XML

Metrics

PDF Downloads(5)
Abstract views(1436)
HTML views(176)

通讯作者: 陈斌, bchen63@163.com

1.
沈阳化工大学材料科学与工程学院沈阳 110142