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Zhao Z, Jin Z, Wu G, Li C, Yu J. TriFNet: A triple-branch feature fusion network for pH determination by surface-enhanced Raman spectroscopy. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 312:124048. [PMID: 38387412 DOI: 10.1016/j.saa.2024.124048] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/04/2023] [Revised: 02/11/2024] [Accepted: 02/14/2024] [Indexed: 02/24/2024]
Abstract
Due to the acidic tumor microenvironment caused by metabolic changes in tumor cells, the accurate pH detection of extracellular fluid is helpful for doctors in precise tumor resection. The combination of Raman spectroscopy and deep learning provides a solution for pH detection. However, most existing studies use one-dimensional convolutional neural networks (1D-CNNs) for spectral analysis, which limits the performance due to insufficient feature extraction. In this work, we propose a 2D triple-branch feature fusion network (TriFNet) for accurate pH determination using surface-enhanced Raman spectra (SERS). Specifically, we design a triple-branch network structure by converting Raman spectra into three types of images to extensively extract complex patterns in spectra. In addition, an attention fusion module, which leverages the complementarity among features in both space and channel, is designed to obtain the valuable information, achieving further accurate pH determination. On our Raman spectral dataset containing 14,137 samples, we achieved mean absolute error (MAE) of 0.059, standard deviation of the absolute error (SD) of 0.07, root mean squared error (RMSE) of 0.092, and coefficient of determination (R2) of 0.991 on the test set. Compared with other published methods, the four metrics showed an average improvement of 47%, 39%, 43%, and 6%, respectively. In addition, visualization validates the diagnostic capability of our model to correlate with biomolecular signatures. Meanwhile, our model has robustness to different SERS chips. These results prove the potential of our method to develop an effective technology based on Raman spectroscopy for accurate pH determination to guide surgery.
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Affiliation(s)
- Zheng Zhao
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
| | - Ziyi Jin
- School of Pharmacy, Fudan University, Shanghai 201203, China
| | - Guoqing Wu
- School of Information Science and Technology, Fudan University, Shanghai 200438, China
| | - Cong Li
- School of Pharmacy, Fudan University, Shanghai 201203, China.
| | - Jinhua Yu
- School of Information Science and Technology, Fudan University, Shanghai 200438, China.
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2
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Chen J, Hu J, Xue C, Zhang Q, Li J, Wang Z, Lv J, Zhang A, Dang H, Lu D, Zou D, Cong L, Li Y, Chen GJ, Shum PP. Combined Mutual Learning Net for Raman Spectral Microbial Strain Identification. Anal Chem 2024; 96:5824-5831. [PMID: 38573047 DOI: 10.1021/acs.analchem.3c05107] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/05/2024]
Abstract
Infectious diseases pose a significant threat to global health, yet traditional microbiological identification methods suffer from drawbacks, such as high costs and long processing times. Raman spectroscopy, a label-free and noninvasive technique, provides rich chemical information and has tremendous potential in fast microbial diagnoses. Here, we propose a novel Combined Mutual Learning Net that precisely identifies microbial subspecies. It demonstrated an average identification accuracy of 87.96% in an open-access data set with thirty microbial strains, representing a 5.76% improvement. 50% of the microbial subspecies accuracies were elevated by 1% to 46%, especially for E. coli 2 improved from 31% to 77%. Furthermore, it achieved a remarkable subspecies accuracy of 92.4% in the custom-built fiber-optical tweezers Raman spectroscopy system, which collects Raman spectra at a single-cell level. This advancement demonstrates the effectiveness of this method in microbial subspecies identification, offering a promising solution for microbiology diagnosis.
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Affiliation(s)
- Junfan Chen
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jiaqi Hu
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Chenlong Xue
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Qian Zhang
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou 511443, China
| | - Jingyan Li
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Ziyue Wang
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Jinqian Lv
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Aoyan Zhang
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Hong Dang
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Dan Lu
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Defeng Zou
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Longqing Cong
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Yuchao Li
- Guangdong Provincial Key Laboratory of Nanophotonic Manipulation, Institute of Nanophotonics, Jinan University, Guangzhou 511443, China
| | - Gina Jinna Chen
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
| | - Perry Ping Shum
- State Key Laboratory of Optical Fiber and Cable Manufacture Technology, Guangdong Key Laboratory of Integrated Optoelectronics Intellisense, Department of EEE, Southern University of Science and Technology, Shenzhen 518055, China
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Lu XY, Wang CY, Tang H, Qin YF, Cui L, Wang X, Liu GK, Ren B. Patch-Based Convolutional Encoder: A Deep Learning Algorithm for Spectral Classification Balancing the Local and Global Information. Anal Chem 2024. [PMID: 38324760 DOI: 10.1021/acs.analchem.3c03889] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/09/2024]
Abstract
Molecular vibrational spectroscopies, including infrared absorption and Raman scattering, provide molecular fingerprint information and are powerful tools for qualitative and quantitative analysis. They benefit from the recent development of deep-learning-based algorithms to improve the spectral, spatial, and temporal resolutions. Although a variety of deep-learning-based algorithms, including those to simultaneously extract the global and local spectral features, have been developed for spectral classification, the classification accuracy is still far from satisfactory when the difference becomes very subtle. Here, we developed a lightweight algorithm named patch-based convolutional encoder (PACE), which effectively improved the accuracy of spectral classification by extracting spectral features while balancing local and global information. The local information was captured well by segmenting the spectrum into patches with an appropriate patch size. The global information was extracted by constructing the correlation between different patches with depthwise separable convolutions. In the five open-source spectral data sets, PACE achieved a state-of-the-art performance. The more difficult the classification, the better the performance of PACE, compared with that of residual neural network (ResNet), vision transformer (ViT), and other commonly used deep learning algorithms. PACE helped improve the accuracy to 92.1% in Raman identification of pathogen-derived extracellular vesicles at different physiological states, which is much better than those of ResNet (85.1%) and ViT (86.0%). In general, the precise recognition and extraction of subtle differences offered by PACE are expected to facilitate vibrational spectroscopy to be a powerful tool toward revealing the relevant chemical reaction mechanisms in surface science or realizing the early diagnosis in life science.
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Affiliation(s)
- Xin-Yu Lu
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Chen-Yue Wang
- Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
| | - Hui Tang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
| | - Yi-Fei Qin
- Xiamen Key Laboratory of Indoor Air and Health, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Li Cui
- Xiamen Key Laboratory of Indoor Air and Health, Key Laboratory of Urban Environment and Health, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen 361021, China
- College of Resources and Environment, University of Chinese Academy of Sciences, Beijing 100049, China
| | - Xiang Wang
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, China
| | - Guo-Kun Liu
- State Key Laboratory of Marine Environmental Science, Fujian Provincial Key Laboratory for Coastal Ecology and Environmental Studies, Center for Marine Environmental Chemistry & Toxicology, College of the Environment and Ecology, Xiamen University, Xiamen, Fujian 361102, China
| | - Bin Ren
- State Key Laboratory of Physical Chemistry of Solid Surfaces, Collaborative Innovation Center of Chemistry for Energy Materials (iChEM), College of Chemistry and Chemical Engineering, Xiamen University, Xiamen 361005, China
- Institute of Artificial Intelligence, Xiamen University, Xiamen 361005, China
- Tan Kah Kee Innovation Laboratory, Xiamen 361005, China
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Kaushal S, Priyadarshi N, Garg P, Singhal NK, Lim DK. Nano-Biotechnology for Bacteria Identification and Potent Anti-bacterial Properties: A Review of Current State of the Art. NANOMATERIALS (BASEL, SWITZERLAND) 2023; 13:2529. [PMID: 37764558 PMCID: PMC10536455 DOI: 10.3390/nano13182529] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 08/10/2023] [Revised: 08/26/2023] [Accepted: 09/05/2023] [Indexed: 09/29/2023]
Abstract
Sepsis is a critical disease caused by the abrupt increase of bacteria in human blood, which subsequently causes a cytokine storm. Early identification of bacteria is critical to treating a patient with proper antibiotics to avoid sepsis. However, conventional culture-based identification takes a long time. Polymerase chain reaction (PCR) is not so successful because of the complexity and similarity in the genome sequence of some bacterial species, making it difficult to design primers and thus less suitable for rapid bacterial identification. To address these issues, several new technologies have been developed. Recent advances in nanotechnology have shown great potential for fast and accurate bacterial identification. The most promising strategy in nanotechnology involves the use of nanoparticles, which has led to the advancement of highly specific and sensitive biosensors capable of detecting and identifying bacteria even at low concentrations in very little time. The primary drawback of conventional antibiotics is the potential for antimicrobial resistance, which can lead to the development of superbacteria, making them difficult to treat. The incorporation of diverse nanomaterials and designs of nanomaterials has been utilized to kill bacteria efficiently. Nanomaterials with distinct physicochemical properties, such as optical and magnetic properties, including plasmonic and magnetic nanoparticles, have been extensively studied for their potential to efficiently kill bacteria. In this review, we are emphasizing the recent advances in nano-biotechnologies for bacterial identification and anti-bacterial properties. The basic principles of new technologies, as well as their future challenges, have been discussed.
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Affiliation(s)
- Shimayali Kaushal
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea;
| | - Nitesh Priyadarshi
- National Agri-Food Biotechnology Institute (NABI), Sector-81, Mohali 140306, India; (N.P.); (P.G.)
| | - Priyanka Garg
- National Agri-Food Biotechnology Institute (NABI), Sector-81, Mohali 140306, India; (N.P.); (P.G.)
| | - Nitin Kumar Singhal
- National Agri-Food Biotechnology Institute (NABI), Sector-81, Mohali 140306, India; (N.P.); (P.G.)
| | - Dong-Kwon Lim
- KU-KIST Graduate School of Converging Science and Technology, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea;
- Department of Integrative Energy Engineering, College of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Republic of Korea
- Brain Science Institute, Korea Institute of Science and Technology (KIST), 5, Hwarang-ro 14-gil, Seongbuk-gu, Seoul 02792, Republic of Korea
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Lyu JW, Zhang XD, Tang JW, Zhao YH, Liu SL, Zhao Y, Zhang N, Wang D, Ye L, Chen XL, Wang L, Gu B. Rapid Prediction of Multidrug-Resistant Klebsiella pneumoniae through Deep Learning Analysis of SERS Spectra. Microbiol Spectr 2023; 11:e0412622. [PMID: 36877048 PMCID: PMC10100812 DOI: 10.1128/spectrum.04126-22] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/12/2022] [Accepted: 01/20/2023] [Indexed: 03/07/2023] Open
Abstract
Klebsiella pneumoniae is listed by the WHO as a priority pathogen of extreme importance that can cause serious consequences in clinical settings. Due to its increasing multidrug resistance all over the world, K. pneumoniae has the potential to cause extremely difficult-to-treat infections. Therefore, rapid and accurate identification of multidrug-resistant K. pneumoniae in clinical diagnosis is important for its prevention and infection control. However, the limitations of conventional and molecular methods significantly hindered the timely diagnosis of the pathogen. As a label-free, noninvasive, and low-cost method, surface-enhanced Raman scattering (SERS) spectroscopy has been extensively studied for its application potentials in the diagnosis of microbial pathogens. In this study, we isolated and cultured 121 K. pneumoniae strains from clinical samples with different drug resistance profiles, which included polymyxin-resistant K. pneumoniae (PRKP; n = 21), carbapenem-resistant K. pneumoniae, (CRKP; n = 50), and carbapenem-sensitive K. pneumoniae (CSKP; n = 50). For each strain, a total of 64 SERS spectra were generated for the enhancement of data reproducibility, which were then computationally analyzed via the convolutional neural network (CNN). According to the results, the deep learning model CNN plus attention mechanism could achieve a prediction accuracy as high as 99.46%, with robustness score of 5-fold cross-validation at 98.87%. Taken together, our results confirmed the accuracy and robustness of SERS spectroscopy in the prediction of drug resistance of K. pneumoniae strains with the assistance of deep learning algorithms, which successfully discriminated and predicted PRKP, CRKP, and CSKP strains. IMPORTANCE This study focuses on the simultaneous discrimination and prediction of Klebsiella pneumoniae strains with carbapenem-sensitive, carbapenem-resistant, and polymyxin-resistant phenotypes. The implementation of CNN plus an attention mechanism makes the highest prediction accuracy at 99.46%, which confirms the diagnostic potential of the combination of SERS spectroscopy with the deep learning algorithm for antibacterial susceptibility testing in clinical settings.
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Affiliation(s)
- Jing-Wen Lyu
- Department of Laboratory Medicine, School of Medical Technology, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xue Di Zhang
- Department of Laboratory Medicine, School of Medical Technology, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Laboratory Medicine, The Affiliated Xuzhou Infectious Diseases Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Jia-Wei Tang
- Department of Intelligent Medical Engineering, School of Medical Informatics and Engineering, Xuzhou Medical University, Jiangsu Province, Xuzhou, China
| | - Yun-Hu Zhao
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Su-Ling Liu
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Yue Zhao
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Ni Zhang
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Dan Wang
- Laboratory Medicine, The Second Affiliated Hospital of Xuzhou Medical University, Xuzhou, Jiangsu Province, China
| | - Long Ye
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Xiao-Li Chen
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
| | - Liang Wang
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
- School of Medical and Health Sciences, Edith Cowan University, Joondalup, Western Australia, Australia
| | - Bing Gu
- Department of Laboratory Medicine, School of Medical Technology, Xuzhou Medical University, Xuzhou, Jiangsu Province, China
- Laboratory Medicine, Guangdong Provincial People’s Hospital (Guangdong Academy of Medical Sciences), Southern Medical University, Guangzhou, Guangdong Province, China
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Al-Shaebi Z, Uysal Ciloglu F, Nasser M, Aydin O. Highly Accurate Identification of Bacteria's Antibiotic Resistance Based on Raman Spectroscopy and U-Net Deep Learning Algorithms. ACS OMEGA 2022; 7:29443-29451. [PMID: 36033656 PMCID: PMC9404519 DOI: 10.1021/acsomega.2c03856] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/20/2022] [Accepted: 08/02/2022] [Indexed: 06/15/2023]
Abstract
Bacterial pathogens especially antibiotic-resistant ones are a public health concern worldwide. To oppose the morbidity and mortality associated with them, it is critical to select an appropriate antibiotic by performing a rapid bacterial diagnosis. Using a combination of Raman spectroscopy and deep learning algorithms to identify bacteria is a rapid and reliable method. Nevertheless, due to the loss of information during training a model, some deep learning algorithms suffer from low accuracy. Herein, we modify the U-Net architecture to fit our purpose of classifying the one-dimensional Raman spectra. The proposed U-Net model provides highly accurate identification of the 30 isolates of bacteria and yeast, empiric treatment groups, and antimicrobial resistance, thanks to its capability to concatenate and copy important features from the encoder layers to the decoder layers, thereby decreasing the data loss. The accuracies of the model for the 30-isolate level, empiric treatment level, and antimicrobial resistance level tasks are 86.3, 97.84, and 95%, respectively. The proposed deep learning model has a high potential for not only bacterial identification but also for other diagnostic purposes in the biomedical field.
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Affiliation(s)
- Zakarya Al-Shaebi
- Department
of Biomedical Engineering, Erciyes University, 38039 Kayseri, Turkey
- NanoThera
Lab, Drug Application and Research Center (ERFARMA), Erciyes University, 38039 Kayseri, Turkey
| | - Fatma Uysal Ciloglu
- Department
of Biomedical Engineering, Erciyes University, 38039 Kayseri, Turkey
- NanoThera
Lab, Drug Application and Research Center (ERFARMA), Erciyes University, 38039 Kayseri, Turkey
| | - Mohammed Nasser
- Department
of Geomatics Engineering, Erciyes University, 38039 Kayseri, Turkey
| | - Omer Aydin
- Department
of Biomedical Engineering, Erciyes University, 38039 Kayseri, Turkey
- NanoThera
Lab, Drug Application and Research Center (ERFARMA), Erciyes University, 38039 Kayseri, Turkey
- Clinical
Engineering Research and Implementation Center, (ERKAM), Erciyes University, 38030 Kayseri, Turkey
- Nanotechnology
Research and Application Center (ERNAM), Erciyes University, 38039 Kayseri, Turkey
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Zhou B, Tong YK, Zhang R, Ye A. RamanNet: a lightweight convolutional neural network for bacterial identification based on Raman spectra. RSC Adv 2022; 12:26463-26469. [PMID: 36275115 PMCID: PMC9478993 DOI: 10.1039/d2ra03722j] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/16/2022] [Accepted: 08/25/2022] [Indexed: 11/21/2022] Open
Abstract
Raman spectroscopy combined convolutional neural network (CNN) enables rapid and accurate identification of the species of bacteria. However, the existing CNN requires a complex hyperparameters model design. Herein, we propose a new simple network architecture with less hyperparameter design and low computation cost, RamanNet, for rapid and accurate identifying of bacteria at the species level based on its Raman spectra. We verified that compared with the previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset and PKU-bacterial Raman spectral datasets, but using only about 1/45 and 1/297 network parameters, respectively. RamanNet achieved an average isolate-level accuracy of 84.7 ± 0.3%, antibiotic treatment identification accuracy of 97.1 ± 0.3%, and distinguished accuracy of 81.6 ± 0.9% for methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) on the Bacteria-ID dataset, respectively. Moreover, it achieved an average accuracy of 96.04% on the PKU-bacterial dataset. The RamanNet model benefited from fewer model parameters that can be quickly trained even using CPU. Therefore, our method has the potential to rapidly and accurately identify bacterial species based on their Raman spectra and can be easily extended to other classification tasks based on Raman spectra. We propose a novel CNN model named RamanNet for rapid and accurate identification of bacteria at the species-level based on Raman spectra. Compared to previous CNN methods, the RamanNet reached comparable results on the Bacteria-ID Raman spectral dataset.![]()
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Affiliation(s)
- Bo Zhou
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Yu-Kai Tong
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
| | - Ru Zhang
- School of Science, Beijing University of Posts and Telecommunications, Beijing 100876, China
| | - Anpei Ye
- Key Laboratory for the Physics and Chemistry of Nanodevices, School of Electronics, Peking University, Beijing 100871, China
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