Search search
submit Submit
Publication Date
to
Vol. 178
Latest Volume
All Volumes
PIER 180 [2024] PIER 179 [2024] PIER 178 [2023] PIER 177 [2023] PIER 176 [2023] PIER 175 [2022] PIER 174 [2022] PIER 173 [2022] PIER 172 [2021] PIER 171 [2021] PIER 170 [2021] PIER 169 [2020] PIER 168 [2020] PIER 167 [2020] PIER 166 [2019] PIER 165 [2019] PIER 164 [2019] PIER 163 [2018] PIER 162 [2018] PIER 161 [2018] PIER 160 [2017] PIER 159 [2017] PIER 158 [2017] PIER 157 [2016] PIER 156 [2016] PIER 155 [2016] PIER 154 [2015] PIER 153 [2015] PIER 152 [2015] PIER 151 [2015] PIER 150 [2015] PIER 149 [2014] PIER 148 [2014] PIER 147 [2014] PIER 146 [2014] PIER 145 [2014] PIER 144 [2014] PIER 143 [2013] PIER 142 [2013] PIER 141 [2013] PIER 140 [2013] PIER 139 [2013] PIER 138 [2013] PIER 137 [2013] PIER 136 [2013] PIER 135 [2013] PIER 134 [2013] PIER 133 [2013] PIER 132 [2012] PIER 131 [2012] PIER 130 [2012] PIER 129 [2012] PIER 128 [2012] PIER 127 [2012] PIER 126 [2012] PIER 125 [2012] PIER 124 [2012] PIER 123 [2012] PIER 122 [2012] PIER 121 [2011] PIER 120 [2011] PIER 119 [2011] PIER 118 [2011] PIER 117 [2011] PIER 116 [2011] PIER 115 [2011] PIER 114 [2011] PIER 113 [2011] PIER 112 [2011] PIER 111 [2011] PIER 110 [2010] PIER 109 [2010] PIER 108 [2010] PIER 107 [2010] PIER 106 [2010] PIER 105 [2010] PIER 104 [2010] PIER 103 [2010] PIER 102 [2010] PIER 101 [2010] PIER 100 [2010] PIER 99 [2009] PIER 98 [2009] PIER 97 [2009] PIER 96 [2009] PIER 95 [2009] PIER 94 [2009] PIER 93 [2009] PIER 92 [2009] PIER 91 [2009] PIER 90 [2009] PIER 89 [2009] PIER 88 [2008] PIER 87 [2008] PIER 86 [2008] PIER 85 [2008] PIER 84 [2008] PIER 83 [2008] PIER 82 [2008] PIER 81 [2008] PIER 80 [2008] PIER 79 [2008] PIER 78 [2008] PIER 77 [2007] PIER 76 [2007] PIER 75 [2007] PIER 74 [2007] PIER 73 [2007] PIER 72 [2007] PIER 71 [2007] PIER 70 [2007] PIER 69 [2007] PIER 68 [2007] PIER 67 [2007] PIER 66 [2006] PIER 65 [2006] PIER 64 [2006] PIER 63 [2006] PIER 62 [2006] PIER 61 [2006] PIER 60 [2006] PIER 59 [2006] PIER 58 [2006] PIER 57 [2006] PIER 56 [2006] PIER 55 [2005] PIER 54 [2005] PIER 53 [2005] PIER 52 [2005] PIER 51 [2005] PIER 50 [2005] PIER 49 [2004] PIER 48 [2004] PIER 47 [2004] PIER 46 [2004] PIER 45 [2004] PIER 44 [2004] PIER 43 [2003] PIER 42 [2003] PIER 41 [2003] PIER 40 [2003] PIER 39 [2003] PIER 38 [2002] PIER 37 [2002] PIER 36 [2002] PIER 35 [2002] PIER 34 [2001] PIER 33 [2001] PIER 32 [2001] PIER 31 [2001] PIER 30 [2001] PIER 29 [2000] PIER 28 [2000] PIER 27 [2000] PIER 26 [2000] PIER 25 [2000] PIER 24 [1999] PIER 23 [1999] PIER 22 [1999] PIER 21 [1999] PIER 20 [1998] PIER 19 [1998] PIER 18 [1998] PIER 17 [1997] PIER 16 [1997] PIER 15 [1997] PIER 14 [1996] PIER 13 [1996] PIER 12 [1996] PIER 11 [1995] PIER 10 [1995] PIER 09 [1994] PIER 08 [1994] PIER 07 [1993] PIER 06 [1992] PIER 05 [1991] PIER 04 [1991] PIER 03 [1990] PIER 02 [1990] PIER 01 [1989]
All Journals
PIER PIER B PIER C PIER M PIER Letters
2023-10-01
High-Accuracy Rapid Identification and Classification of Mixed Bacteria Using Hyperspectral Transmission Microscopic Imaging and Machine Learning
By
He Zhu,

    Ms. He Zhu

    College of Optical Science and Engineering

    Zhejiang University

    China

    Homepage

Jing Luo,

    Dr. Jing Luo

    College of Optical Science and Engineering

    Zhejiang University

    China

    Homepage

Jiaqi Liao,

    Dr. Jiaqi Liao

    College of Optical Science and Engineering

    Zhejiang University

    China

    Homepage

Sailing He

    Prof. Sailing He

    Department of Electromagnetic Theory

    Royal Institute of Technology

    Sweden

    Homepage

Progress In Electromagnetics Research, Vol. 178, 49-62, 2023
doi:10.2528/PIER23082303
Abstract
In this paper, we developed a hyperspectral transmission microscopic imaging (HTMI) system for rapid detection of pathogenic bacteria, which can realize precise identification and classification of mixed pathogenic bacteria to a single-bacterium level. The system worksin trans-illumination patterns and a self-developed dispersive hyperspectral imaging module is usedas the detection setup, providing spectral images with high SNR, and showing excellent performances with spatial resolution of 2.19 µm and spectral resolutions less than 1 nm. Hyperspectral microscopic imaging of five types of bacteria in low concentration were performed. The merging spatial-spectral profiles of individual bacteria for each species were extracted and utilized for species identification, achieving high classification accuracy of 93.6% using a simple PCA-SVM method. Species identification experiments of the mixed bacterial sampleswere further carried out, and the results demonstrate the validity and capability of the system assisted with simple machine learning methods to be used as an effective and rapid diagnostic tool for elaborate identification of mixed bacterial pathogen samples, providing guidance for the use of correct antibiotics.
Citation
He Zhu, Jing Luo, Jiaqi Liao, and Sailing He, "High-Accuracy Rapid Identification and Classification of Mixed Bacteria Using Hyperspectral Transmission Microscopic Imaging and Machine Learning," Progress In Electromagnetics Research, Vol. 178, 49-62, 2023.
doi:10.2528/PIER23082303
References

1. Ward, J. L., P. S. Azzopardi, K. L. Francis, et al. "Global, regional, and national mortality among young people aged 10{24 years, 1950{2019: A systematic analysis for the Global Burden of Disease Study 2019," Lancet, Vol. 398, 1593-1618, 2021.
doi:10.1016/S0140-6736(21)01546-4

2. Beutlich, J., J. A. Hammerl, B. Appel, et al. "Characterization of illegal food items and identification of foodborne pathogens brought into the European Union via two major German airports," International Journal of Food Microbiology, Vol. 209, 13-19, 2015.
doi:10.1016/j.ijfoodmicro.2014.10.017

3. Galanis, E., J. Parmley, W. N. De, et al. "Integrated surveillance of Salmonella along the food chain using existing data and resources in British Columbia, Canada," Food Research International, Vol. 45, No. 2, 795-801, 2012.
doi:10.1016/j.foodres.2011.04.015

4. Kumar, A., P. Ellis, Y. Arabi, et al. "Initiation of inappropriate antimicrobial therapy results in a fivefold reduction of survival in human septic shock," Chest, Vol. 136, No. 5, 1237-1248, 2009.
doi:10.1378/chest.09-0087

5. Gan, Y., C. Li, X. Peng, et al. "Fight bacteria with bacteria: Bacterial membrane vesicles as vaccines and delivery nanocarriers against bacterial infections," Nanomedicine-Nanotechnology Biology and Medicine, Vol. 35, 102398, 2021.
doi:10.1016/j.nano.2021.102398

6. Fratamico, P. M., "Comparison of culture, polymerase chain reaction (PCR), TaqMan Salmonella, and Transia Card Salmonella assays for detection of Salmonella spp. in naturally-contaminated ground chicken, ground turkey, and ground beef," Molecular and Cellular Probes, Vol. 17, No. 5, 215-221, 2003.
doi:10.1016/S0890-8508(03)00056-2

7. Bolton, F. J., E. Fritz, S. Poynton, et al. "Rapid enzyme-linked immunoassay for detection of salmonella in food and feed products: Performance testing program," Journal of Aoac International, Vol. 83, No. 2, 299-303, 2000.
doi:10.1093/jaoac/83.2.299

8. Compton, J., "Nucleic acid sequence-based amplification," Nature, Vol. 350, No. 6313, 91-92, 1991.
doi:10.1038/350091a0

9. Engelmann, I., E. K. Alidjinou, J. Ogiez, et al. "Preanalytical issues and cycle threshold values in SARS-CoV-2 real-time RT-PCR testing: Should test results include these," Acs Omega, Vol. 6, No. 10, 6528-6536, 2021.
doi:10.1021/acsomega.1c00166

10. Pal, S., W. Ying, E. C. Alocija, et al. "Sensitivity and specificity performance of a direct-charge transfer biosensor for detecting Bacillus cereus in selected food matrices," Biosystems Engineering, Vol. 99, No. 4, 461-468, 2008.
doi:10.1016/j.biosystemseng.2007.11.015

11. Esteban-Fernandez De Avila, B., M. Pedrero, S. Campuzano, et al. "Sensitive and rapid amperometric magnetoimmunosensor for the determination of Staphylococcus aureus," Analytical and Bioanalytical Chemistry, Vol. 403, No. 4, 917-925, 2012.
doi:10.1007/s00216-012-5738-8

12. Munoz-Berbel, X., N. Vigues, A. T. A. Jenkins, et al. "Impedimetric approach for quantifying low bacteria concentrations based on the changes produced in the electrode-solution interface during the pre-attachment stage," Biosensors & Bioelectronics, Vol. 23, No. 10, 1540-1546, 2008.
doi:10.1016/j.bios.2008.01.007

13. Luo, J., Z. Lin, X. Xing, E. Forsberg, C. Wu, X. Zhu, T. Guo, G. Wang, B. Bian, D. Wu, and S. He, "Portable 4D snapshot hyperspectral imager for fastspectral and surface morphology measurements (Invited Paper)," Progress In Electromagnetics Research, Vol. 173, 25-36, 2022.
doi:10.2528/PIER22021702

14. Guo, T., Z. Lin, X. Xu, Z. Zhang, X. Chen, N. He, G. Wang, Y. Jin, J. Evans, and S. He, "Broad-tuning, dichroic metagrating Fabry-Perot filter based on liquid crystal for spectral imaging," Progress In Electromagnetics Research, Vol. 177, 43-51, 2023.
doi:10.2528/PIER23030703

15. Pawlowski, M. E., J. G. Dwight, N. Thuc Uyen, et al. "High performance image mapping spectrometer (IMS) for snapshot hyperspectral imaging applications," Optics Express, Vol. 27, No. 2, 1597-1612, 2019.
doi:10.1364/OE.27.001597

16. Wang, T., F. Shen, H. Deng, et al. "Smartphone imaging spectrometer for egg/meat freshness monitoring," Analytical Methods, Vol. 14, No. 5, 508-517, 2022.
doi:10.1039/D1AY01726H

17. Shen, F., H. Deng, L. Yu, et al. "Open-source mobile multispectral imaging system and its applications in biological sample sensing," Spectrochimica Acta, Part A: Molecular and Biomolecular Spectroscopy, Vol. 280, 121504, 2022.
doi:10.1016/j.saa.2022.121504

18. Li, Y., F. Shen, and L. Hu, "A stare-down video-rate high-throughput hyperspectral imaging system and its applications in biological sample sensing," IEEE Sensors Journal, 2023.

19. Xu, Z., Y. Jiang, J. Ji, et al. "Classification, identification, and growth stage estimation of microalgae based on transmission hyperspectral microscopic imaging and machine learning," Optics Express, Vol. 28, No. 21, 30686-30700, 2020.
doi:10.1364/OE.406036

20. Lin, S., X. Bi, S. Zhu, et al. "Dual-type hyperspectral microscopic imaging for the identification and analysis of intestinal fungi," Biomedical Optics Express, Vol. 9, No. 9, 4496-4508, 2018.
doi:10.1364/BOE.9.004496

21. Luo, J., H. Zhang, E. Forsberg, et al. "Confocal hyperspectral microscopic imager for the detection and classification of individual microalgae," Optics Express, Vol. 29, No. 23, 37281-37301, 2021.
doi:10.1364/OE.438253

22. Maktabi, M., Y. Wichmann, H. Koehler, et al. "Tumor cell identification and classification in esophageal adenocarcinoma specimens by hyperspectral imaging," Scientific Reports, Vol. 12, No. 1, 4508, 2022.
doi:10.1038/s41598-022-07524-6

23. Zhu, S., K. Su, Y. Liu, et al. "Identification of cancerous gastric cells based on common features extracted from hyperspectral microscopic images," Biomedical Optics Express, Vol. 6, No. 4, 1135-1145, 2015.
doi:10.1364/BOE.6.001135

24. Duan, Y., J. Wang, M. Hu, et al. "Leukocyte classification based on spatial and spectral features of microscopic hyperspectral images," Optics & Laser Technology, Vol. 112, 530-538, 2019.
doi:10.1016/j.optlastec.2018.11.057

25. Wang, Q., J. Wang, M. Zhou, et al. "Spectral-spatial feature-based neural network method for acute lymphoblastic leukemia cell identification via microscopic hyperspectral imaging technology," Biomedical Optics Express, Vol. 8, 3017-3028, 2017.
doi:10.1364/BOE.8.003017

26. Seo, Y., B. Park, A. Hinton, Jr., et al. "Identification of Staphylococcus species with hyperspectral microscope imaging and classification algorithms," Journal of Food Measurement and Characterization, Vol. 10, No. 2, 253-263, 2016.
doi:10.1007/s11694-015-9301-0

27. Seo, Y., B. Park, S. C. Yoon, et al. "Morphological image analysis for foodborne bacteria classification," Transactions of the Asabe, Vol. 61, No. 1, 5-13, 2018.
doi:10.13031/trans.11800

28. Liu, K., Z. Ke, P. Chen, et al. "Classification of two species of Gram-positive bacteria through hyperspectral microscopy coupled with machine learning," Biomedical Optics Express, Vol. 12, No. 12, 7906-7916, 2021.
doi:10.1364/BOE.445041

29. Tao, C., J. Du, Y. Tang, J. Wang, et al. "A deep-learning based system for rapid genus identification of pathogens under hyperspectral microscopic images," Cells, Vol. 11, No. 14, 2237, 2022.
doi:10.3390/cells11142237

30. Tao, C., J. Du, J. Wang, et al. "Rapid identification of infectious pathogens at the single-cell level via combining hyperspectral microscopic images and deep learning," Cells, Vol. 12, No. 3, 379, 2023.
doi:10.3390/cells12030379

31. Kang, R., B. Park, M. Eady, et al. "Classification of foodborne bacteria using hyperspectral microscope imaging technology coupled with convolutional neural networks," Applied Microbiology and Biotechnology, Vol. 104, No. 7, 3157-3166, 2020.
doi:10.1007/s00253-020-10387-4

32. Kang, R., B. Park, M. Eady, et al. "Single-cell classification of foodborne pathogens using hyperspectral microscope imaging coupled with deep learning frameworks," Sensors and Actuators B --- Chemical, Vol. 309, 127789, 2020.
doi:10.1016/j.snb.2020.127789

33. Huang, G., Z. Liu, L. Van Der Maaten, et al. "Densely connected convolutional networks," Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 4700-4708, Honolulu, HI, USA, July 21-26, 2017.

34. Farooq, S., M. Del-Valle, M. O. dos Santos, et al. "Rapid identification of breast cancer subtypes using micro-FTIR and machine learning methods," Applied Optics, Vol. 62, No. 8, C80-C87, 2023.
doi:10.1364/AO.477409

35. Farooq, S., A. Caramel-Juvino, and M. Del-Valle, "Superior machine learning method for breast cancer cell lines identification," 2022 SBFoton International Optics and Photonics Conference (SBFoton IOPC), 1-3, 2022.

36. Cho, J. H., P. J. Gemperline, and D. Walker, "Wavelength calibration method for a CCD detector and multichannel fiber-optic probes," Appl. Spectrosc., Vol. 49, No. 12, 1841-1845, 1995.
doi:10.1366/0003702953966055

37. Wold, S., K. Esbensen, and P. Geladi, "Principal component analysis," Chemom. Intell. Lab. Syst., Vol. 2, No. 1-3, 37-52, 1987.
doi:10.1016/0169-7439(87)80084-9

38. Bro, R. and A. K. Smilde, "Principal component analysis," Analytical Methods, Vol. 6, No. 9, 2812-2831, 2014.
doi:10.1039/C3AY41907J

39. Melgani, F. and L. Bruzzone, "Classification of hyperspectral remote sensing images with support vector machines," IEEE Transactions on Geoscience and Remote Sensing, Vol. 42, No. 8, 1778-1790, 2004.
doi:10.1109/TGRS.2004.831865

40. Moughal, T. A., "Hyperspectral image classification using support vector machine," Journal of Physics Conference, Vol. 439, 012042, 2013.
doi:10.1088/1742-6596/439/1/012042

41. Yang, W. and H. Song, "Spectral-spatial classification of hyperspectral image based on support vector machine," International Journal of Information Technology and Web Engineering, Vol. 16, No. 1, 56-74, 2021.
doi:10.4018/IJITWE.2021010103

42. De Oliveira, M. A. S., M. Campbell, A. M. Afify, et al. "Hyperspectral Raman microscopy can accurately differentiate single cells of different human thyroid nodules," Biomedical Optics Express, Vol. 10, No. 9, 4411-4421, 2019.
doi:10.1364/BOE.10.004411

43. Luo, J., H. Lin, A. Yang, et al. "Pulse fluorescence LIDAR system for identification and low concentration measurements of Phaeocystisglobosa cells and colonies," Optik, Vol. 270, 170003, 2022.
doi:10.1016/j.ijleo.2022.170003

Download Full Article (761) View Full Article volume
The EM Academy piers.org jpier.org Who's Who in EM
Copyright © 2022 The Electromagnetics Academy. All Rights Reserved.