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
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