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Chen L, Guo H, Wang C, Chen B, Sassa F, Hayashi K. Two-Dimensional SERS Sensor Array for Identifying and Visualizing the Gas Spatial Distributions of Two Distinct Odor Sources. Sensors (Basel) 2024; 24:790. [PMID: 38339509 PMCID: PMC10857130 DOI: 10.3390/s24030790] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Revised: 01/19/2024] [Accepted: 01/23/2024] [Indexed: 02/12/2024]
Abstract
The spatial distribution of gas emitted from an odor source provides valuable information regarding the composition, size, and localization of the odor source. Surface-enhanced Raman scattering (SERS) gas sensors exhibit ultra-high sensitivity, molecular specificity, rapid response, and large-area detection. In this paper, a SERS gas sensor array was developed for visualizing the spatial distribution of gas evaporated from benzaldehyde and 4-ethylbenzaldehyde odor sources. The SERS spectra of the gas were collected by scanning the sensor array using an automatic detection system. The non-negative matrix factorization algorithm was employed to extract feature and concentration information at each spot on the sensor array. A heatmap image was generated for visualizing the gas spatial distribution using concentration information. Gaussian fitting was applied to process the image for localizing the odor source. The size of the odor source was estimated using the processed image. Moreover, the spectra of benzaldehyde, 4-ethylbenzaldehyde, and their gas mixture were simultaneously detected using one SERS sensor array. The feature information was recognized using a convolutional neural network with an accuracy of 98.21%. As a result, the benzaldehyde and 4-ethylbenzaldehyde odor sources were identified and visualized. Our research findings have various potential applications, including odor source localization, environmental monitoring, and healthcare.
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Affiliation(s)
- Lin Chen
- Department of Information Science, Joint Graduate School of Mathematics for Innovation, Kyushu University, Fukuoka 819-0395, Japan
| | - Hao Guo
- Department of Electronics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan; (H.G.); (C.W.); (F.S.)
| | - Cong Wang
- Department of Electronics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan; (H.G.); (C.W.); (F.S.)
| | - Bin Chen
- Chongqing Key Laboratory of Non-Linear Circuit and Intelligent Information Processing, College of Electronic and Information Engineering, Southwest University, Chongqing 400715, China;
| | - Fumihiro Sassa
- Department of Electronics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan; (H.G.); (C.W.); (F.S.)
| | - Kenshi Hayashi
- Department of Electronics, Graduate School of Information Science and Electrical Engineering, Kyushu University, Fukuoka 819-0395, Japan; (H.G.); (C.W.); (F.S.)
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2
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Wang C, He T, Zhou H, Zhang Z, Lee C. Artificial intelligence enhanced sensors - enabling technologies to next-generation healthcare and biomedical platform. Bioelectron Med 2023; 9:17. [PMID: 37528436 PMCID: PMC10394931 DOI: 10.1186/s42234-023-00118-1] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2023] [Accepted: 06/17/2023] [Indexed: 08/03/2023] Open
Abstract
The fourth industrial revolution has led to the development and application of health monitoring sensors that are characterized by digitalization and intelligence. These sensors have extensive applications in medical care, personal health management, elderly care, sports, and other fields, providing people with more convenient and real-time health services. However, these sensors face limitations such as noise and drift, difficulty in extracting useful information from large amounts of data, and lack of feedback or control signals. The development of artificial intelligence has provided powerful tools and algorithms for data processing and analysis, enabling intelligent health monitoring, and achieving high-precision predictions and decisions. By integrating the Internet of Things, artificial intelligence, and health monitoring sensors, it becomes possible to realize a closed-loop system with the functions of real-time monitoring, data collection, online analysis, diagnosis, and treatment recommendations. This review focuses on the development of healthcare artificial sensors enhanced by intelligent technologies from the aspects of materials, device structure, system integration, and application scenarios. Specifically, this review first introduces the great advances in wearable sensors for monitoring respiration rate, heart rate, pulse, sweat, and tears; implantable sensors for cardiovascular care, nerve signal acquisition, and neurotransmitter monitoring; soft wearable electronics for precise therapy. Then, the recent advances in volatile organic compound detection are highlighted. Next, the current developments of human-machine interfaces, AI-enhanced multimode sensors, and AI-enhanced self-sustainable systems are reviewed. Last, a perspective on future directions for further research development is also provided. In summary, the fusion of artificial intelligence and artificial sensors will provide more intelligent, convenient, and secure services for next-generation healthcare and biomedical applications.
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Affiliation(s)
- Chan Wang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Tianyiyi He
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Hong Zhou
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Zixuan Zhang
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore, 4 Engineering Drive 3, Singapore, 117576, Singapore.
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore, 5 Engineering Drive 1, Singapore, 117608, Singapore.
- NUS Suzhou Research Institute (NUSRI), Suzhou Industrial Park, Suzhou, 215123, China.
- NUS Graduate School for Integrative Science and Engineering, National University of Singapore, Singapore, 117456, Singapore.
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3
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Cheng N, Lou B, Wang H. Discovering the digital biomarker of hepatocellular carcinoma in serum with SERS-based biosensors and intelligence vision. Colloids Surf B Biointerfaces 2023; 226:113315. [PMID: 37086688 DOI: 10.1016/j.colsurfb.2023.113315] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2023] [Revised: 03/31/2023] [Accepted: 04/11/2023] [Indexed: 04/24/2023]
Abstract
By its many virtues, non-biomarker-reliant molecular detection has recently shown bright prospects for cancer screening but its clinical application is hindered by the shortage of measurable criteria that are analogous to biomarkers. Here, we report a digital biomarker, as a new-concept serum biomarker, of hepatocellular carcinoma (HCC) found with SERS-based biosensors and a deep neural network "digital retina" for visualizing and explicitly defining spectral fingerprints. We validate the discovered digital biomarker (a collection of 10 characteristic peaks in the serum SERS spectra) with unsupervised clustering of spectra from an independent sample batch comprised normal individuals and HCC cases; the validation results show clustering accuracies of 95.71% and 100.00%, respectively. Furthermore, we find that the digital biomarker of HCC shares a few common peaks with three clinically applied serum biomarkers, which means it could convey essential biomolecular information similar to these biomarkers. Accordingly, we present an intelligent method for early HCC detection that leverages the digital biomarker with similar traits as biomarkers. Employing the digital biomarker, we could accurately stratify HCC, hepatitis B, and normal populations with linear classifiers, exhibiting accuracies over 92% and area under the receiver operating curve values above 0.93. It is anticipated that this non-biomarker-reliant molecular detection method will facilitate mass cancer screening.
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Affiliation(s)
- Ningtao Cheng
- School of Medicine, Zhejiang University, Hangzhou, Zhejiang 310058, China.
| | - Bin Lou
- Department of Laboratory Medicine, the First Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, Zhejiang 310003, China
| | - Hongyang Wang
- International Cooperation Laboratory on Signal Transduction, Eastern Hepatobiliary Surgery Hospital, Shanghai 200438, China; National Center for Liver Cancer, Shanghai 201805, China.
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4
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Huang YH, Wei H, Santiago PJ, Thrift WJ, Ragan R, Jiang S. Sensing Antibiotics in Wastewater Using Surface-Enhanced Raman Scattering. Environ Sci Technol 2023; 57:4880-4891. [PMID: 36934344 PMCID: PMC10061928 DOI: 10.1021/acs.est.3c00027] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 01/02/2023] [Revised: 01/27/2023] [Accepted: 03/02/2023] [Indexed: 06/18/2023]
Abstract
Rapid and cost-effective detection of antibiotics in wastewater and through wastewater treatment processes is an important first step in developing effective strategies for their removal. Surface-enhanced Raman scattering (SERS) has the potential for label-free, real-time sensing of antibiotic contamination in the environment. This study reports the testing of two gold nanostructures as SERS substrates for the label-free detection of quinoline, a small-molecular-weight antibiotic that is commonly found in wastewater. The results showed that the self-assembled SERS substrate was able to quantify quinoline spiked in wastewater with a lower limit of detection (LoD) of 5.01 ppb. The SERStrate (commercially available SERS substrate with gold nanopillars) had a similar sensitivity for quinoline quantification in pure water (LoD of 1.15 ppb) but did not perform well for quinoline quantification in wastewater (LoD of 97.5 ppm) due to interferences from non-target molecules in the wastewater. Models constructed based on machine learning algorithms could improve the separation and identification of quinoline Raman spectra from those of interference molecules to some degree, but the selectivity of SERS intensification was more critical to achieve the identification and quantification of the target analyte. The results of this study are a proof-of-concept for SERS applications in label-free sensing of environmental contaminants. Further research is warranted to transform the concept into a practical technology for environmental monitoring.
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Affiliation(s)
- Yen-Hsiang Huang
- Department
of Civil and Environmental Engineering, University of California, Irvine, Irvine, California 92697, United States
| | - Hong Wei
- Department
of Materials Science and Engineering, University
of California, Irvine, Irvine, California 92697, United States
| | - Peter J. Santiago
- Department
of Materials Science and Engineering, University
of California, Irvine, Irvine, California 92697, United States
| | - William John Thrift
- Department
of Materials Science and Engineering, University
of California, Irvine, Irvine, California 92697, United States
| | - Regina Ragan
- Department
of Materials Science and Engineering, University
of California, Irvine, Irvine, California 92697, United States
| | - Sunny Jiang
- Department
of Civil and Environmental Engineering, University of California, Irvine, Irvine, California 92697, United States
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5
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Dos Santos DP, Sena MM, Almeida MR, Mazali IO, Olivieri AC, Villa JEL. Unraveling surface-enhanced Raman spectroscopy results through chemometrics and machine learning: principles, progress, and trends. Anal Bioanal Chem 2023;:1-22. [PMID: 36864313 DOI: 10.1007/s00216-023-04620-y] [Citation(s) in RCA: 8] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2022] [Revised: 02/02/2023] [Accepted: 02/20/2023] [Indexed: 03/04/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS) has gained increasing attention because it provides rich chemical information and high sensitivity, being applicable in many scientific fields including medical diagnosis, forensic analysis, food control, and microbiology. Although SERS is often limited by the lack of selectivity in the analysis of samples with complex matrices, the use of multivariate statistics and mathematical tools has been demonstrated to be an efficient strategy to circumvent this issue. Importantly, since the rapid development of artificial intelligence has been promoting the implementation of a wide variety of advanced multivariate methods in SERS, a discussion about the extent of their synergy and possible standardization becomes necessary. This critical review comprises the principles, advantages, and limitations of coupling SERS with chemometrics and machine learning for both qualitative and quantitative analytical applications. Recent advances and trends in combining SERS with uncommonly used but powerful data analysis tools are also discussed. Finally, a section on benchmarking and tips for selecting the suitable chemometric/machine learning method is included. We believe this will help to move SERS from an alternative detection strategy to a general analytical technique for real-life applications.
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6
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Park S, Wahab A, Kim M, Khan S. Self-supervised learning for inter-laboratory variation minimization in surface-enhanced Raman scattering spectroscopy. Analyst 2023; 148:1473-1482. [PMID: 36861467 DOI: 10.1039/d2an01569b] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/12/2023]
Abstract
Surface-enhanced Raman scattering (SERS) spectroscopy is still considered poorly reproducible despite its numerous advantages and is not a sufficiently robust analytical technique for routine implementation outside of academia. In this article, we present a self-supervised deep learning-based information fusion technique to minimize the variance in the SERS measurements of multiple laboratories for the same target analyte. In particular, a variation minimization model, coined the minimum-variance network (MVNet), is designed. Moreover, a linear regression model is trained using the output of the proposed MVNet. The proposed model showed improved performance in predicting the concentration of the unseen target analyte. The linear regression model trained on the output of the proposed model was evaluated by several well-known metrics, such as root mean square error of prediction (RMSEP), BIAS, standard error of prediction (SEP), and coefficient of determination (R2). The leave-one-lab-out cross-validation (LOLABO-CV) results indicate that the MVNet also minimizes the variance of completely unseen laboratory datasets while improving the reproducibility and linear fit of the regression model. The Python implementation of MVNet and the code for the analysis can be found on the GitHub page https://github.com/psychemistz/MVNet.
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Affiliation(s)
- Seongyong Park
- Asan Medical Center, University of Ulsan, College of Medicine, Department of Anesthesiology and Pain Medicine, 88 Olympic-ro 43-gil, Songpa-Gu, Seoul, 05505, South Korea
| | - Abdul Wahab
- Department of Mathematics, Nazarbayev University, 53 Kabanbay Batyr Avenue, Astana, 010000, Kazakhstan
| | - Minseok Kim
- Department of Mechanical System Engineering, Kumoh National Institute of Technology, 61, Daehak-ro, Gumi, 39177, Gyeongsangbuk-do, South Korea.,Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, 61, Daehak-ro, Gumi, 39177, Gyeongsangbuk-do, South Korea
| | - Shujaat Khan
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology, Daejeon 34141, South Korea. .,Siemens Healthineers, 755 College Rd E, Princeton, 08540, NJ, USA
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7
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Abstract
Plasmonic nanomaterials have outstanding optoelectronic properties potentially enabling the next generation of catalysts, sensors, lasers and photothermal devices. Owing to optical and electron techniques, modern nanoplasmonics research generates large datasets characterizing features across length scales. Furthermore, optimizing syntheses leading to specific nanostructures requires time-consuming multiparametric approaches. These complex datasets and trial-and-error practices make nanoplasmonics research ripe for the application of machine learning (ML) and advanced data processing methods. ML algorithms capture relationships between synthesis, structure and performance in a way that far exceeds conventional simulation and theory approaches, enabling effective performance optimization. For example, neural networks can tailor the nanostructure morphology to target desired properties, identify synthetic conditions and extract quantitative information from complex data. Here we discuss the nascent field of ML for nanoplasmonics, describe the opportunities and limitations of ML in nanoplasmonic research, and conclude that ML is potentially transformative, especially if the community curates and shares its big data.
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Affiliation(s)
- Jean-Francois Masson
- Département de chimie, Quebec Center for Advanced Materials, Regroupement québécois sur les matériaux de pointe, and Centre interdisciplinaire de recherche sur le cerveau et l'apprentissage, Université de Montréal, Montréal, Quebec, Canada.
| | - John S Biggins
- Engineering Department, University of Cambridge, Cambridge, UK.
| | - Emilie Ringe
- Department of Material Science and Metallurgy, University of Cambridge, Cambridge, UK.
- Department of Earth Science, University of Cambridge, Cambridge, UK.
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8
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Zhou H, Xu L, Ren Z, Zhu J, Lee C. Machine learning-augmented surface-enhanced spectroscopy toward next-generation molecular diagnostics. Nanoscale Adv 2023; 5:538-570. [PMID: 36756499 PMCID: PMC9890940 DOI: 10.1039/d2na00608a] [Citation(s) in RCA: 15] [Impact Index Per Article: 15.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/07/2022] [Accepted: 11/06/2022] [Indexed: 06/17/2023]
Abstract
The world today is witnessing the significant role and huge demand for molecular detection and screening in healthcare and medical diagnosis, especially during the outbreak of COVID-19. Surface-enhanced spectroscopy techniques, including Surface-Enhanced Raman Scattering (SERS) and Infrared Absorption (SEIRA), provide lattice and molecular vibrational fingerprint information which is directly linked to the molecular constituents, chemical bonds, and configuration. These properties make them an unambiguous, nondestructive, and label-free toolkit for molecular diagnostics and screening. However, new issues in molecular diagnostics, such as increasing molecular species, faster spread of viruses, and higher requirements for detection accuracy and sensitivity, have brought great challenges to detection technology. Advancements in artificial intelligence and machine learning (ML) techniques show promising potential in empowering SERS and SEIRA with rapid analysis and automatic data processing to jointly tackle the challenge. This review introduces the combination of ML and SERS/SEIRA by investigating how ML algorithms can be beneficial to SERS/SEIRA, discussing the general process of combining ML and SEIRA/SERS, highlighting the molecular diagnostics and screening applications based on ML-combined SEIRA/SERS, and providing perspectives on the future development of ML-integrated SEIRA/SERS. In general, this review offers comprehensive knowledge about the recent advances and the future outlook regarding ML-integrated SEIRA/SERS for molecular diagnostics and screening.
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Affiliation(s)
- Hong Zhou
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
| | - Liangge Xu
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
- National Key Laboratory of Special Environment Composite Technology, Harbin Institute of Technology Harbin 150001 China
| | - Zhihao Ren
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
| | - Jiaqi Zhu
- National Key Laboratory of Special Environment Composite Technology, Harbin Institute of Technology Harbin 150001 China
| | - Chengkuo Lee
- Department of Electrical and Computer Engineering, National University of Singapore Singapore 117583
- Center for Intelligent Sensors and MEMS (CISM), National University of Singapore Singapore 117608
- NUS Suzhou Research Institute (NUSRI) Suzhou 215123 China
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9
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Negm A, Howlader MMR, Belyakov I, Bakr M, Ali S, Irannejad M, Yavuz M. Materials Perspectives of Integrated Plasmonic Biosensors. Materials (Basel) 2022; 15:7289. [PMID: 36295354 PMCID: PMC9611134 DOI: 10.3390/ma15207289] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/26/2022] [Revised: 10/02/2022] [Accepted: 10/13/2022] [Indexed: 06/16/2023]
Abstract
With the growing need for portable, compact, low-cost, and efficient biosensors, plasmonic materials hold the promise to meet this need owing to their label-free sensitivity and deep light-matter interaction that can go beyond the diffraction limit of light. In this review, we shed light on the main physical aspects of plasmonic interactions, highlight mainstream and future plasmonic materials including their merits and shortcomings, describe the backbone substrates for building plasmonic biosensors, and conclude with a brief discussion of the factors affecting plasmonic biosensing mechanisms. To do so, we first observe that 2D materials such as graphene and transition metal dichalcogenides play a major role in enhancing the sensitivity of nanoparticle-based plasmonic biosensors. Then, we identify that titanium nitride is a promising candidate for integrated applications with performance comparable to that of gold. Our study highlights the emerging role of polymer substrates in the design of future wearable and point-of-care devices. Finally, we summarize some technical and economic challenges that should be addressed for the mass adoption of plasmonic biosensors. We believe this review will be a guide in advancing the implementation of plasmonics-based integrated biosensors.
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Affiliation(s)
- Ayman Negm
- Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada
- Department of Electronics and Communications Engineering, Cairo University, Giza 12613, Egypt
| | - Matiar M. R. Howlader
- Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Ilya Belyakov
- Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, ON N2L 3G1, Canada
| | - Mohamed Bakr
- Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada
| | - Shirook Ali
- Department of Electrical and Computer Engineering, McMaster University, Hamilton, ON L8S 4K1, Canada
- School of Mechanical and Electrical Engineering Technology, Sheridan College, Brampton, ON L6Y 5H9, Canada
| | | | - Mustafa Yavuz
- Waterloo Institute for Nanotechnology, University of Waterloo, Waterloo, ON N2L 3G1, Canada
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10
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Park S, Lee J, Khan S, Wahab A, Kim M. Machine Learning-Based Heavy Metal Ion Detection Using Surface-Enhanced Raman Spectroscopy. Sensors (Basel) 2022; 22:596. [PMID: 35062556 PMCID: PMC8778908 DOI: 10.3390/s22020596] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/22/2021] [Revised: 12/27/2021] [Accepted: 01/10/2022] [Indexed: 02/06/2023]
Abstract
Surface-Enhanced Raman Spectroscopy (SERS) is often used for heavy metal ion detection. However, large variations in signal strength, spectral profile, and nonlinearity of measurements often cause problems that produce varying results. It raises concerns about the reproducibility of the results. Consequently, the manual classification of the SERS spectrum requires carefully controlled experimentation that further hinders the large-scale adaptation. Recent advances in machine learning offer decent opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are missing. Towards this end, we provide the SERS spectral benchmark dataset of lead(II) nitride (Pb(NO3)2) for a heavy metal ion detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. The proposed model can successfully identify the Pb(NO3)2 molecule from SERS measurements of independent test experiments. In particular, the proposed model shows an 84.6% balanced accuracy for the cross-batch testing task.
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Affiliation(s)
- Seongyong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea; (S.P.); (S.K.)
| | - Jaeseok Lee
- Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea;
- Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
| | - Shujaat Khan
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea; (S.P.); (S.K.)
| | - Abdul Wahab
- Department of Mathematics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan;
| | - Minseok Kim
- Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea;
- Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
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11
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Guo J, Liu Y, Ju H, Lu G. From lab to field: Surface-enhanced Raman scattering-based sensing strategies for on-site analysis. Trends Analyt Chem 2022; 146:116488. [DOI: 10.1016/j.trac.2021.116488] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
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12
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Park S, Lee J, Khan S, Wahab A, Kim M. SERSNet: Surface-Enhanced Raman Spectroscopy Based Biomolecule Detection Using Deep Neural Network. Biosensors (Basel) 2021; 11:bios11120490. [PMID: 34940246 PMCID: PMC8699110 DOI: 10.3390/bios11120490] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 10/15/2021] [Revised: 11/25/2021] [Accepted: 11/29/2021] [Indexed: 05/10/2023]
Abstract
Surface-Enhanced Raman Spectroscopy (SERS)-based biomolecule detection has been a challenge due to large variations in signal intensity, spectral profile, and nonlinearity. Recent advances in machine learning offer great opportunities to address these issues. However, well-documented procedures for model development and evaluation, as well as benchmark datasets, are lacking. Towards this end, we provide the SERS spectral benchmark dataset of Rhodamine 6G (R6G) for a molecule detection task and evaluate the classification performance of several machine learning models. We also perform a comparative study to find the best combination between the preprocessing methods and the machine learning models. Our best model, coined as the SERSNet, robustly identifies R6G molecule with excellent independent test performance. In particular, SERSNet shows 95.9% balanced accuracy for the cross-batch testing task.
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Affiliation(s)
- Seongyong Park
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea; (S.P.); (S.K.)
| | - Jaeseok Lee
- Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea;
- Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
| | - Shujaat Khan
- Department of Bio and Brain Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon 34141, Korea; (S.P.); (S.K.)
| | - Abdul Wahab
- Department of Mathematics, Nazarbayev University, Nur-Sultan 010000, Kazakhstan;
| | - Minseok Kim
- Department of Mechanical System Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea;
- Department of Aeronautics, Mechanical and Electronic Convergence Engineering, Kumoh National Institute of Technology, Gumi 39177, Korea
- Correspondence:
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13
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Chen L, Guo H, Sassa F, Chen B, Hayashi K. SERS Gas Sensors Based on Multiple Polymer Films with High Design Flexibility for Gas Recognition. Sensors (Basel) 2021; 21:5546. [PMID: 34450988 DOI: 10.3390/s21165546] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/19/2021] [Revised: 08/11/2021] [Accepted: 08/15/2021] [Indexed: 11/17/2022]
Abstract
The Surface-Enhanced Raman Scattering (SERS) technique is utilized to fabricate sensors for gas detection due to its rapid detection speed and high sensitivity. However, gases with similar molecular structures are difficult to directly discriminate using SERS gas sensors because there are characteristic peak overlaps in the Raman spectra. Here, we proposed a multiple SERS gas sensor matrix via a spin-coating functional polymer to enhance the gas recognition capability. Poly (acrylic acid) (PAA), Poly (methyl methacrylate) (PMMA) and Polydimethylsiloxane (PDMS) were employed to fabricate the polymer film. The high design flexibility of the two-layer film was realized by the layer-by-layer method with 2 one-layer films. The SERS gas sensor coated by different polymer films showed a distinct affinity to target gases. The principle component analysis (PCA) algorithm was used for the further clustering of gas molecules. Three target gases, phenethyl alcohol, acetophenone and anethole, were perfectly discriminated, as the characteristic variables in the response matrix constructed by the combination of gas responses obtained 3 one-layer and 3 two-layer film-coated sensors. This research provides a new SERS sensing approach for recognizing gases with similar molecular structures.
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Schackart KE, Yoon JY. Machine Learning Enhances the Performance of Bioreceptor-Free Biosensors. Sensors (Basel) 2021; 21:5519. [PMID: 34450960 PMCID: PMC8401027 DOI: 10.3390/s21165519] [Citation(s) in RCA: 21] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/09/2021] [Accepted: 08/13/2021] [Indexed: 01/06/2023]
Abstract
Since their inception, biosensors have frequently employed simple regression models to calculate analyte composition based on the biosensor's signal magnitude. Traditionally, bioreceptors provide excellent sensitivity and specificity to the biosensor. Increasingly, however, bioreceptor-free biosensors have been developed for a wide range of applications. Without a bioreceptor, maintaining strong specificity and a low limit of detection have become the major challenge. Machine learning (ML) has been introduced to improve the performance of these biosensors, effectively replacing the bioreceptor with modeling to gain specificity. Here, we present how ML has been used to enhance the performance of these bioreceptor-free biosensors. Particularly, we discuss how ML has been used for imaging, Enose and Etongue, and surface-enhanced Raman spectroscopy (SERS) biosensors. Notably, principal component analysis (PCA) combined with support vector machine (SVM) and various artificial neural network (ANN) algorithms have shown outstanding performance in a variety of tasks. We anticipate that ML will continue to improve the performance of bioreceptor-free biosensors, especially with the prospects of sharing trained models and cloud computing for mobile computation. To facilitate this, the biosensing community would benefit from increased contributions to open-access data repositories for biosensor data.
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Affiliation(s)
- Kenneth E. Schackart
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, USA;
| | - Jeong-Yeol Yoon
- Department of Biosystems Engineering, The University of Arizona, Tucson, AZ 85721, USA;
- Department of Biomedical Engineering, The University of Arizona, Tucson, AZ 85721, USA
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Xu Y, Hassan MM, Sharma AS, Li H, Chen Q. Recent advancement in nano-optical strategies for detection of pathogenic bacteria and their metabolites in food safety. Crit Rev Food Sci Nutr 2021; 63:486-504. [PMID: 34281447 DOI: 10.1080/10408398.2021.1950117] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/14/2023]
Abstract
Pathogenic bacteria and their metabolites are the leading risk factor in food safety and are one of the major threats to human health because of the capability of triggering diseases with high morbidity and mortality. Nano-optical sensors for bacteria sensing have been greatly explored with the emergence of nanotechnology and artificial intelligence. In addition, with the rapid development of cross fusion technology, other technologies integrated nano-optical sensors show great potential in bacterial and their metabolites sensing. This review focus on nano-optical strategies for bacteria and their metabolites sensing in the field of food safety; based on surface-enhanced Raman scattering (SERS), fluorescence, and colorimetric biosensors, and their integration with the microfluidic platform, electrochemical platform, and nucleic acid amplification platform in the recent three years. Compared with the traditional techniques, nano optical-based sensors have greatly improved the sensitivity with reduced detection time and cost. However, challenges remain for the simple fabrication of biosensors and their practical application in complex matrices. Thus, bringing out improvements or novelty in the pretreatment methods will be a trend in the upcoming future.
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Affiliation(s)
- Yi Xu
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
| | - Md Mehedi Hassan
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
| | - Arumugam Selva Sharma
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
| | - Huanhuan Li
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
| | - Quansheng Chen
- School of Food and Biological Engineering, Jiangsu University, Zhenjiang, People's Republic of China
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Thrift WJ, Ronaghi S, Samad M, Wei H, Nguyen DG, Cabuslay AS, Groome CE, Santiago PJ, Baldi P, Hochbaum AI, Ragan R. Deep Learning Analysis of Vibrational Spectra of Bacterial Lysate for Rapid Antimicrobial Susceptibility Testing. ACS Nano 2020; 14:15336-15348. [PMID: 33095005 DOI: 10.1021/acsnano.0c05693] [Citation(s) in RCA: 50] [Impact Index Per Article: 12.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
Rapid antimicrobial susceptibility testing (AST) is an integral tool to mitigate the unnecessary use of powerful and broad-spectrum antibiotics that leads to the proliferation of multi-drug-resistant bacteria. Using a sensor platform composed of surface-enhanced Raman scattering (SERS) sensors with control of nanogap chemistry and machine learning algorithms for analysis of complex spectral data, bacteria metabolic profiles post antibiotic exposure are correlated with susceptibility. Deep neural network models are able to discriminate the responses of Escherichia coli and Pseudomonas aeruginosa to antibiotics from untreated cells in SERS data in 10 min after antibiotic exposure with greater than 99% accuracy. Deep learning analysis is also able to differentiate responses from untreated cells with antibiotic dosages up to 10-fold lower than the minimum inhibitory concentration observed in conventional growth assays. In addition, analysis of SERS data using a generative model, a variational autoencoder, identifies spectral features in the P. aeruginosa lysate data associated with antibiotic efficacy. From this insight, a combinatorial dataset of metabolites is selected to extend the latent space of the variational autoencoder. This culture-free dataset dramatically improves classification accuracy to select effective antibiotic treatment in 30 min. Unsupervised Bayesian Gaussian mixture analysis achieves 99.3% accuracy in discriminating between susceptible versus resistant to antibiotic cultures in SERS using the extended latent space. Discriminative and generative models rapidly provide high classification accuracy with small sets of labeled data, which enormously reduces the amount of time needed to validate phenotypic AST with conventional growth assays. Thus, this work outlines a promising approach toward practical rapid AST.
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Affiliation(s)
- William John Thrift
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
| | - Sasha Ronaghi
- Sage Hill School, Newport Coast, California 92657, United States
| | - Muntaha Samad
- Department of Computer Science, University of California, Irvine, California 92697, United States
| | - Hong Wei
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
| | - Dean Gia Nguyen
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, California 92697, United States
| | | | - Chloe E Groome
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
| | - Peter Joseph Santiago
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
| | - Pierre Baldi
- Department of Computer Science, University of California, Irvine, California 92697, United States
| | - Allon I Hochbaum
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
- Department of Chemistry, University of California, Irvine, California 92617, United States
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, California 92697, United States
- Department of Molecular Biology and Biochemistry, University of California, Irvine, California 92697, United States
| | - Regina Ragan
- Department of Materials Science and Engineering, University of California, Irvine, California 92697, United States
- Department of Chemical and Biomolecular Engineering, University of California, Irvine, California 92697, United States
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Shin H, Seo D, Choi Y. Extracellular Vesicle Identification Using Label-Free Surface-Enhanced Raman Spectroscopy: Detection and Signal Analysis Strategies. Molecules 2020; 25:E5209. [PMID: 33182340 PMCID: PMC7664897 DOI: 10.3390/molecules25215209] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2020] [Revised: 10/19/2020] [Accepted: 11/05/2020] [Indexed: 02/07/2023] Open
Abstract
Extracellular vesicles (EVs) have been widely investigated as promising biomarkers for the liquid biopsy of diseases, owing to their countless roles in biological systems. Furthermore, with the notable progress of exosome research, the use of label-free surface-enhanced Raman spectroscopy (SERS) to identify and distinguish disease-related EVs has emerged. Even in the absence of specific markers for disease-related EVs, label-free SERS enables the identification of unique patterns of disease-related EVs through their molecular fingerprints. In this review, we describe label-free SERS approaches for disease-related EV pattern identification in terms of substrate design and signal analysis strategies. We first describe the general characteristics of EVs and their SERS signals. We then present recent works on applied plasmonic nanostructures to sensitively detect EVs and notable methods to interpret complex spectral data. This review also discusses current challenges and future prospects of label-free SERS-based disease-related EV pattern identification.
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Affiliation(s)
- Hyunku Shin
- Department of Bio-convergence Engineering, Korea University, Seoul 02841, Korea; (H.S.); (D.S.)
| | - Dongkwon Seo
- Department of Bio-convergence Engineering, Korea University, Seoul 02841, Korea; (H.S.); (D.S.)
| | - Yeonho Choi
- Department of Bio-convergence Engineering, Korea University, Seoul 02841, Korea; (H.S.); (D.S.)
- School of Biomedical Engineering, Korea University, Seoul 02841, Korea
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Lussier F, Thibault V, Charron B, Wallace GQ, Masson JF. Deep learning and artificial intelligence methods for Raman and surface-enhanced Raman scattering. Trends Analyt Chem 2020. [DOI: 10.1016/j.trac.2019.115796] [Citation(s) in RCA: 157] [Impact Index Per Article: 39.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/16/2022]
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