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Koyun OC, Keser RK, Şahin SO, Bulut D, Yorulmaz M, Yücesoy V, Töreyin BU. RamanFormer: A Transformer-Based Quantification Approach for Raman Mixture Components. ACS OMEGA 2024; 9:23241-23251. [PMID: 38854537 PMCID: PMC11154961 DOI: 10.1021/acsomega.3c09247] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2023] [Revised: 05/03/2024] [Accepted: 05/10/2024] [Indexed: 06/11/2024]
Abstract
Raman spectroscopy is a noninvasive technique to identify materials by their unique molecular vibrational fingerprints. However, distinguishing and quantifying components in mixtures present challenges due to overlapping spectra, especially when components share similar features. This study presents "RamanFormer", a transformer-based model designed to enhance the analysis of Raman spectroscopy data. By effectively managing sequential data and integrating self-attention mechanisms, RamanFormer identifies and quantifies components in chemical mixtures with high precision, achieving a mean absolute error of 1.4% and a root mean squared error of 1.6%, significantly outperforming traditional methods such as least squares, MLP, VGG11, and ResNet50. Tested extensively on binary and ternary mixtures under varying conditions, including noise levels with a signal-to-noise ratio of up to 10 dB, RamanFormer proves to be a robust tool, improving the reliability of material identification and broadening the application of Raman spectroscopy in fields, such as material science, forensics, and biomedical diagnostics.
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Affiliation(s)
- Onur Can Koyun
- Signal
Processing for Computational Intelligence Research Group (SP4CING),
Informatics Institute, Istanbul Technical
University, 34469 Istanbul, Turkey
| | - Reyhan Kevser Keser
- Signal
Processing for Computational Intelligence Research Group (SP4CING),
Informatics Institute, Istanbul Technical
University, 34469 Istanbul, Turkey
| | | | - Damla Bulut
- ASELSAN
Inc, Yenimahalle, 06200 Ankara, Turkey
| | | | | | - Behçet Uğur Töreyin
- Signal
Processing for Computational Intelligence Research Group (SP4CING),
Informatics Institute, Istanbul Technical
University, 34469 Istanbul, Turkey
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2
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Jiang S, Wang X, Chong Y, Huang Y, Hu W, Smith PES, Jiang J, Feng S. Spectra-Based Machine Learning for Predicting the Statistical Interaction Properties of CO Adsorbates on Surface. J Phys Chem Lett 2024; 15:2400-2404. [PMID: 38393989 DOI: 10.1021/acs.jpclett.4c00011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/25/2024]
Abstract
Theoretical analyses of small-molecule adsorption on heterogeneous catalyst surfaces often rely on simplified models of molecular adsorption with the most favorable configuration. Given that real-world experimental tests frequently entail multiple molecules interacting with the surface, there is a pressing need for a comprehensive multimolecule adsorption model to bridge the gap between theory and experiment. Using machine learning, we predict the average values of important adsorption properties from conformationally averaged, calculated infrared and Raman spectra and compare these values to those theoretically derived from the conformationally averaged ensemble. Remarkably, our approach yields excellent predictions even when faced with large and indeterminate numbers of surface molecules. These quantitative spectra-averaged property relationships provide a theoretical framework for extracting key interaction properties from the spectra of real chemical environments.
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Affiliation(s)
- Shuang Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Xijun Wang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Yuanyuan Chong
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Yan Huang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Wei Hu
- School of Chemistry and Chemical Engineering, Qilu University of Technology (Shandong Academy of Science), Jinan 250353, China
| | | | - Jun Jiang
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
| | - Shuo Feng
- Key Laboratory of Precision and Intelligent Chemistry, School of Chemistry and Materials Science, University of Science and Technology of China, Hefei 230026, China
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3
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Wu X, Du Z, Ma R, Zhang X, Yang D, Liu H, Zhang Y. Qualitative and quantitative studies of phthalates in extra virgin olive oil (EVOO) by surface-enhanced Raman spectroscopy (SERS) combined with long short term memory (LSTM) neural network. Food Chem 2024; 433:137300. [PMID: 37657163 DOI: 10.1016/j.foodchem.2023.137300] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2023] [Revised: 08/13/2023] [Accepted: 08/25/2023] [Indexed: 09/03/2023]
Abstract
Phthalates are commonly used plasticizers in the plastics industry, and have received extensive attention due to their reproductive toxicity. Since phthalates are lipophilic solutions, phthalates can easily migrate from packaging to edible oils. This study synthesized stable and sensitive Gold Nanostars as SERS substrates to conduct qualitative and quantitative analysis of two common phthalates, dibutyl phthalate and di(2-ethylhexyl) phthalate. Two ethanol standard solutions and actual oil solutions of phthalates at different concentrations (10, 5, 1, 0.5, 0.1, 0.02 mg/kg) were prepared. After dimension reduction, LSTM achieved the accuracy of 98% for pure EVOO and EVOO adulterated with different types of phthalates. In terms of quantification, LSTM demonstrates great predictive performance with Rp2 greater than 0.97 and the ratio of performance to deviation greater than 5. These results have certain guiding significance for the analysis of plasticizers in edible oil.
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Affiliation(s)
- Xijun Wu
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China
| | - Zherui Du
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China.
| | - Renqi Ma
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China.
| | - Xin Zhang
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China
| | - Daolin Yang
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China
| | - Hailong Liu
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China
| | - Yungang Zhang
- Measurement Technology & Instrumentation Key Laboratory of Hebei Province, Institute of Electrical Engineering, Yanshan University, Qinhuangdao 066004 China
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4
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Bi X, Lin L, Chen Z, Ye J. Artificial Intelligence for Surface-Enhanced Raman Spectroscopy. SMALL METHODS 2024; 8:e2301243. [PMID: 37888799 DOI: 10.1002/smtd.202301243] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 09/15/2023] [Revised: 10/11/2023] [Indexed: 10/28/2023]
Abstract
Surface-enhanced Raman spectroscopy (SERS), well acknowledged as a fingerprinting and sensitive analytical technique, has exerted high applicational value in a broad range of fields including biomedicine, environmental protection, food safety among the others. In the endless pursuit of ever-sensitive, robust, and comprehensive sensing and imaging, advancements keep emerging in the whole pipeline of SERS, from the design of SERS substrates and reporter molecules, synthetic route planning, instrument refinement, to data preprocessing and analysis methods. Artificial intelligence (AI), which is created to imitate and eventually exceed human behaviors, has exhibited its power in learning high-level representations and recognizing complicated patterns with exceptional automaticity. Therefore, facing up with the intertwining influential factors and explosive data size, AI has been increasingly leveraged in all the above-mentioned aspects in SERS, presenting elite efficiency in accelerating systematic optimization and deepening understanding about the fundamental physics and spectral data, which far transcends human labors and conventional computations. In this review, the recent progresses in SERS are summarized through the integration of AI, and new insights of the challenges and perspectives are provided in aim to better gear SERS toward the fast track.
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Affiliation(s)
- Xinyuan Bi
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Li Lin
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Zhou Chen
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
| | - Jian Ye
- State Key Laboratory of Systems Medicine for Cancer, School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200030, P. R. China
- Institute of Medical Robotics, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
- Shanghai Key Laboratory of Gynecologic Oncology, Ren Ji Hospital, School of Medicine, Shanghai Jiao Tong University, Shanghai, 200127, P. R. China
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Wei H, Smith JP. Modernized Machine Learning Approach to Illuminate Enzyme Immobilization for Biocatalysis. ACS CENTRAL SCIENCE 2023; 9:1913-1926. [PMID: 37901174 PMCID: PMC10604017 DOI: 10.1021/acscentsci.3c00757] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/21/2023] [Indexed: 10/31/2023]
Abstract
Biocatalysis is an established technology with significant application in the pharmaceutical industry. Immobilization of enzymes offers significant benefits for commercial and practical purposes to enhance the stability and recyclability of biocatalysts. Determination of the spatial and chemical distributions of immobilized enzymes on solid support materials is essential for an optimal catalytic performance. However, current analytical methodologies often fall short of rapidly identifying and characterizing immobilized enzyme systems. Herein, we present a new analytical methodology that combines non-negative matrix factorization (NMF)-an unsupervised machine learning tool-with Raman hyperspectral imaging to simultaneously resolve the spatial and spectral characteristics of all individual species involved in enzyme immobilization. Our novel approach facilitates the determination of the optimal NMF model using new data-driven, quantitative selection criteria that fully resolve all chemical species present, offering a robust methodology for analyzing immobilized enzymes. Specifically, we demonstrate the ability of NMF with Raman hyperspectral imaging to resolve the spatial and spectral profiles of an engineered pantothenate kinase immobilized on two different commercial microporous resins. Our results demonstrate that this approach can accurately identify and spatially resolve all species within this enzyme immobilization process. To the best of our knowledge, this is the first report of NMF within hyperspectral imaging for enzyme immobilization analysis, and as such, our methodology can now provide a new powerful tool to streamline biocatalytic process development within the pharmaceutical industry.
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Affiliation(s)
- Hong Wei
- Process Research & Development,
MRL, Merck & Co., Inc., West Point, Pennsylvania 19486, United States
| | - Joseph P. Smith
- Process Research & Development,
MRL, Merck & Co., Inc., West Point, Pennsylvania 19486, United States
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Atta S, Li JQ, Vo-Dinh T. Multiplex SERS detection of polycyclic aromatic hydrocarbon (PAH) pollutants in water samples using gold nanostars and machine learning analysis. Analyst 2023; 148:5105-5116. [PMID: 37671999 DOI: 10.1039/d3an00636k] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/07/2023]
Abstract
Polycyclic aromatic hydrocarbons (PAHs) have attracted a lot of environmental concern because of their carcinogenic and mutagenic properties, and the fact they can easily contaminate natural resources such as drinking water and river water. This study presents a simple and sensitive point-of-care SERS detection of PAHs combined with machine learning algorithms to predict the PAH content more precisely and accurately in real-life samples such as drinking water and river water. We first synthesized multibranched sharp-spiked surfactant-free gold nanostars (GNSs) that can generate strong surface-enhanced Raman scattering (SERS) signals, which were further coated with cetyltrimethylammonium bromide (CTAB) for long-term stability of the GNSs as well as to trap PAHs. We utilized CTAB-capped GNSs for solution-based 'mix and detect' SERS sensing of various PAHs including pyrene (PY), nitro-pyrene (NP), anthracene (ANT), benzo[a]pyrene (BAP), and triphenylene (TP) spiked in drinking water and river water using a portable Raman module. Very low limits of detection (LOD) were achieved in the nanomolar range for the PAHs investigated. More importantly, the detected SERS signal was reproducible for over 90 days after synthesis. Furthermore, we analyzed the SERS data using artificial intelligence (AI) with machine learning algorithms based on the convolutional neural network (CNN) model in order to discriminate the PAHs in samples more precisely and accurately. Using a CNN classification model, we achieved a high prediction accuracy of 90% in the nanomolar detection range and an f1 score (harmonic mean of precision and recall) of 94%, and using a CNN regression model, achieved an RMSEconc = 1.07 × 10-1 μM. Overall, our SERS platform can be effectively and efficiently used for the accurate detection of PAHs in real-life samples, thus opening up a new, sensitive, selective, and practical approach for point-of-need SERS diagnosis of small molecules in complex practical environments.
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Affiliation(s)
- Supriya Atta
- Fitzpatrick Institute for Photonics, Duke University, Durham, NC 27708, USA.
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Joy Qiaoyi Li
- Fitzpatrick Institute for Photonics, Duke University, Durham, NC 27708, USA.
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
| | - Tuan Vo-Dinh
- Fitzpatrick Institute for Photonics, Duke University, Durham, NC 27708, USA.
- Department of Biomedical Engineering, Duke University, Durham, NC 27708, USA
- Department of Chemistry, Duke University, Durham, NC 27708, USA
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Tseng YM, Chen KL, Chao PH, Han YY, Huang NT. Deep Learning-Assisted Surface-Enhanced Raman Scattering for Rapid Bacterial Identification. ACS APPLIED MATERIALS & INTERFACES 2023. [PMID: 37216401 DOI: 10.1021/acsami.3c03212] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/24/2023]
Abstract
Bloodstream infection (BSI) is characterized by the presence of viable microorganisms in the bloodstream and may induce systemic immune responses. Early and appropriate antibiotic usage is crucial to effectively treating BSI. However, conventional culture-based microbiological diagnostics are time-consuming and cannot provide timely bacterial identification for subsequent antimicrobial susceptibility test (AST) and clinical decision-making. To address this issue, modern microbiological diagnostics have been developed, such as surface-enhanced Raman scattering (SERS), which is a sensitive, label-free, and quick bacterial detection method measuring specific bacterial metabolites. In this study, we aim to integrate a new deep learning (DL) method, Vision Transformer (ViT), with bacterial SERS spectral analysis to build the SERS-DL model for rapid identification of Gram type, species, and resistant strains. To demonstrate the feasibility of our approach, we used 11,774 SERS spectra obtained directly from eight common bacterial species in clinical blood samples without artificial introduction as the training dataset for the SERS-DL model. Our results showed that ViT achieved excellent identification accuracy of 99.30% for Gram type and 97.56% for species. Moreover, we employed transfer learning by using the Gram-positive species identifier as a pre-trained model to perform the antibiotic-resistant strain task. The identification accuracy of methicillin-resistant and -susceptible Staphylococcus aureus (MRSA and MSSA) can reach 98.5% with only 200-dataset requirement. In summary, our SERS-DL model has great potential to provide a quick clinical reference to determine the bacterial Gram type, species, and even resistant strains, which can guide early antibiotic usage in BSI.
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Affiliation(s)
- Yi-Ming Tseng
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, 106319
| | - Ko-Lun Chen
- Institute of Atomic and Molecular Sciences, Academia Sinica, Taipei, Taiwan, 106319
| | - Po-Hsuan Chao
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, 106319
| | - Yin-Yi Han
- Department of Anesthesiology, National Taiwan University Hospital, Taipei, Taiwan, 100229
| | - Nien-Tsu Huang
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan, 106319
- Department of Electrical Engineering, National Taiwan University, Taipei, Taiwan, 106319
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8
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Highly sensitive gold nanoparticles-modified silver nanorod arrays for determination of methyl viologen. Mikrochim Acta 2022; 189:479. [DOI: 10.1007/s00604-022-05590-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2022] [Accepted: 11/23/2022] [Indexed: 11/29/2022]
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9
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Boateng D, Hu C, Dai Y, Chu K, Du J, Smith ZJ. Multicomponent Raman spectral regression using complete and incomplete models and convolutional neural networks. Analyst 2022; 147:4607-4615. [DOI: 10.1039/d2an00984f] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/21/2022]
Abstract
A critical study of CNN networks for Raman regression problems is presented. In evaluating performance on models where spectral information is missing, CNN performs as well as state-of-the-art methods, without the need for spectral pre-processing.
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Affiliation(s)
- Derrick Boateng
- National Engineering Research Center of Speech and Language Information Processing, Department of Electronic Engineering and Information Science, University of Science and Technology of China, China
| | - Chuanzhen Hu
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, China
| | - Yichuan Dai
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, China
| | - Kaiqin Chu
- Suzhou Institute for Advanced Research, University of Science and Technology of China, China
| | - Jun Du
- National Engineering Research Center of Speech and Language Information Processing, Department of Electronic Engineering and Information Science, University of Science and Technology of China, China
| | - Zachary J. Smith
- Key Laboratory of Precision Scientific Instrumentation of Anhui Higher Education Institutes, Department of Precision Machinery and Precision Instrumentation, University of Science and Technology of China, China
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