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Sun Y, Tian Y, Zhang Y, Yu M, Su X, Wang Q, Guo J, Lu Y, Ren L. A double-branch convolutional neural network model for species identification based on multi-modal data. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2024; 318:124454. [PMID: 38788500 DOI: 10.1016/j.saa.2024.124454] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 11/02/2023] [Revised: 04/15/2024] [Accepted: 05/10/2024] [Indexed: 05/26/2024]
Abstract
For species identification analysis, methods based on deep learning are becoming prevalent due to their data-driven and task-oriented nature. The most commonly used convolutional neural network (CNN) model has been well applied in Raman spectra recognition. However, when faced with similar molecules or functional groups, the features of overlapping peaks and weak peaks may not be fully extracted using the CNN model, which can potentially hinder accurate species identification. Based on these practical challenges, the fusion of multi-modal data can effectively meet the comprehensive and accurate analysis of actual samples when compared with single-modal data. In this study, we propose a double-branch CNN model by integrating Raman and image multi-modal data, named SI-DBNet. In addition, we have developed a one-dimensional convolutional neural network combining dilated convolutions and efficient channel attention mechanisms for spectral branching. The effectiveness of the model has been demonstrated using the Grad-CAM method to visualize the key regions concerned by the model. When compared to single-modal and multi-modal classification methods, our SI-DBNet model achieved superior performance with a classification accuracy of 98.8%. The proposed method provided a new reference for species identification based on multi-modal data fusion.
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Affiliation(s)
- Yuxin Sun
- College of Computer Science and Technology, Qingdao University, Qingdao 266071, China; College of Physics and Opto-electronic Engineering, Ocean University of China, Qingdao 266100, China
| | - Ye Tian
- College of Physics and Opto-electronic Engineering, Ocean University of China, Qingdao 266100, China
| | - Yiyi Zhang
- College of Physics and Opto-electronic Engineering, Ocean University of China, Qingdao 266100, China
| | - Mengting Yu
- College of Physics and Opto-electronic Engineering, Ocean University of China, Qingdao 266100, China
| | - Xiaoquan Su
- College of Computer Science and Technology, Qingdao University, Qingdao 266071, China
| | - Qi Wang
- College of Physics and Opto-electronic Engineering, Ocean University of China, Qingdao 266100, China
| | - Jinjia Guo
- College of Physics and Opto-electronic Engineering, Ocean University of China, Qingdao 266100, China
| | - Yuan Lu
- College of Physics and Opto-electronic Engineering, Ocean University of China, Qingdao 266100, China
| | - Lihui Ren
- College of Computer Science and Technology, Qingdao University, Qingdao 266071, China; Single-Cell Center, Qingdao Institute of BioEnergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao 266101, China.
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Guselnikova O, Trelin A, Kang Y, Postnikov P, Kobashi M, Suzuki A, Shrestha LK, Henzie J, Yamauchi Y. Pretreatment-free SERS sensing of microplastics using a self-attention-based neural network on hierarchically porous Ag foams. Nat Commun 2024; 15:4351. [PMID: 38806498 PMCID: PMC11133413 DOI: 10.1038/s41467-024-48148-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2023] [Accepted: 04/21/2024] [Indexed: 05/30/2024] Open
Abstract
Low-cost detection systems are needed for the identification of microplastics (MPs) in environmental samples. However, their rapid identification is hindered by the need for complex isolation and pre-treatment methods. This study describes a comprehensive sensing platform to identify MPs in environmental samples without requiring independent separation or pre-treatment protocols. It leverages the physicochemical properties of macroporous-mesoporous silver (Ag) substrates templated with self-assembled polymeric micelles to concurrently separate and analyze multiple MP targets using surface-enhanced Raman spectroscopy (SERS). The hydrophobic layer on Ag aids in stabilizing the nanostructures in the environment and mitigates biofouling. To monitor complex samples with multiple MPs and to demultiplex numerous overlapping patterns, we develop a neural network (NN) algorithm called SpecATNet that employs a self-attention mechanism to resolve the complex dependencies and patterns in SERS data to identify six common types of MPs: polystyrene, polyethylene, polymethylmethacrylate, polytetrafluoroethylene, nylon, and polyethylene terephthalate. SpecATNet uses multi-label classification to analyze multi-component mixtures even in the presence of various interference agents. The combination of macroporous-mesoporous Ag substrates and self-attention-based NN technology holds potential to enable field monitoring of MPs by generating rich datasets that machines can interpret and analyze.
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Affiliation(s)
- Olga Guselnikova
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
- Research School of Chemistry and Applied Biomedical Sciences, Tomsk Polytechnic University, Tomsk, Russian Federation.
| | - Andrii Trelin
- Department of Solid-State Engineering, University of Chemistry and Technology, Prague, Czech Republic
| | - Yunqing Kang
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan
| | - Pavel Postnikov
- Research School of Chemistry and Applied Biomedical Sciences, Tomsk Polytechnic University, Tomsk, Russian Federation
- Department of Solid-State Engineering, University of Chemistry and Technology, Prague, Czech Republic
| | - Makoto Kobashi
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan
| | - Asuka Suzuki
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan
| | - Lok Kumar Shrestha
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan
- Department of Materials Science, Institute of Pure and Applied Sciences, University of Tsukuba, Tsukuba, Ibaraki, Japan
| | - Joel Henzie
- National Institute for Materials Science (NIMS), Tsukuba, Ibaraki, Japan.
| | - Yusuke Yamauchi
- Department of Materials Process Engineering, Graduate School of Engineering, Nagoya University, Furo-cho, Chikusa-ku, Nagoya, Japan.
- Australian Institute for Bioengineering and Nanotechnology (AIBN), The University of Queensland, Brisbane, QLD, Australia.
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Ren L, Li S, Ye W, Lv Q, Sun Y, Zhou X, Lian S, Lv J, Wang S, Guo J, Tian Y, Zheng R, Lu Y. Tracking organic matrix in the seashell by elemental mapping under laser-induced breakdown spectroscopy. Talanta 2024; 271:125658. [PMID: 38219325 DOI: 10.1016/j.talanta.2024.125658] [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: 10/22/2023] [Revised: 01/05/2024] [Accepted: 01/08/2024] [Indexed: 01/16/2024]
Abstract
As a biogenic calcium carbonate, the seashell plays a crucial role in marine environmental studies. In these studies, it is essential to investigate the composition of the seashell. In this study, we used laser-induced breakdown spectroscopy (LIBS) to analyze the elemental composition of cultured scallop-shell (Patinopecten yessoensis), with a specific focus on examining the organic elements (C, N, O, H) to track the shell organic matrix (SOM). Our findings indicate that the seashell organic layer can be accurately identified by referencing the strong emission of nitrogen or the low signal of calcium. To further confirm the presence of this layer, we employed fluorescence spectroscopy, Raman spectroscopy and FTIR spectroscopy. Correlation analysis revealed a strong connection between LIBS emissions (H, O, CC) and seashell organics, as well as demonstrated the presence of organics in metallic emissions (Si, Ba). However, when we conducted elemental mapping on the shell cross-section, the distribution similarity was observed between the elements N, Ba, and Sr. Based on the correlation of organics and the distribution similarity, it is concluded that barium is an element associated with the SOM. These results highlight the potential of LIBS for organic analysis, which can complement traditional seashell analysis.
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Affiliation(s)
- Lihui Ren
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, 266100, PR China; Single-Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, PR China
| | - Shoujie Li
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, 266100, PR China
| | - Wangquan Ye
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, 266100, PR China
| | - Qi Lv
- Single-Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, PR China
| | - Yuxin Sun
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, 266100, PR China; Single-Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, PR China
| | - Xuan Zhou
- Single-Cell Center, Qingdao Institute of Bioenergy and Bioprocess Technology, Chinese Academy of Sciences, Qingdao, 266101, PR China
| | - Shanshan Lian
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, PR China
| | - Jia Lv
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, PR China
| | - Shi Wang
- MOE Key Laboratory of Marine Genetics and Breeding, College of Marine Life Sciences, Ocean University of China, Qingdao, 266003, PR China
| | - Jinjia Guo
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, 266100, PR China
| | - Ye Tian
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, 266100, PR China
| | - Ronger Zheng
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, 266100, PR China
| | - Yuan Lu
- College of Physics and Optoelectronic Engineering, Ocean University of China, Qingdao, 266100, PR China.
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Lim J, Shin G, Shin D. Fast Detection and Classification of Microplastics below 10 μm Using CNN with Raman Spectroscopy. Anal Chem 2024; 96:6819-6825. [PMID: 38625095 DOI: 10.1021/acs.analchem.4c00823] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 04/17/2024]
Abstract
In light of the growing awareness regarding the ubiquitous presence of microplastics (MPs) in our environment, recent efforts have been made to integrate Artificial Intelligence (AI) technology into MP detection. Among spectroscopic techniques, Raman spectroscopy is preferred for the detection of MP particles measuring less than 10 μm, as it overcomes the diffraction limitations encountered in Fourier transform infrared (FTIR). However, Raman spectroscopy's inherent limitation is its low scattering cross section, which often results in prolonged data collection times during practical sample measurements. In this study, we implemented a convolutional neural network (CNN) model alongside a tailored data interpolation strategy to expedite data collection for MP particles within the 1-10 μm range. Remarkably, we achieved the classification of plastic types for individual particles with a mere 0.4 s of exposure time, reaching an approximate confidence level of 85.47(±5.00)%. We postulate that the result significantly accelerates the aggregation of microplastic distribution data in diverse scenarios, contributing to the development of a comprehensive global microplastic map.
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Affiliation(s)
- Jeonghyun Lim
- Department of Chemistry and Chemical Engineering, Inha University, Incheon 22212, Republic of Korea
| | - Gogyun Shin
- Department of Chemistry and Chemical Engineering, Inha University, Incheon 22212, Republic of Korea
| | - Dongha Shin
- Department of Chemistry and Chemical Engineering, Inha University, Incheon 22212, Republic of Korea
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Cai Y, Yao Z, Cheng X, He Y, Li S, Pan J. Deep metric learning framework combined with Gramian angular difference field image generation for Raman spectra classification based on a handheld Raman spectrometer. SPECTROCHIMICA ACTA. PART A, MOLECULAR AND BIOMOLECULAR SPECTROSCOPY 2023; 303:123085. [PMID: 37454497 DOI: 10.1016/j.saa.2023.123085] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/02/2023] [Revised: 06/16/2023] [Accepted: 06/26/2023] [Indexed: 07/18/2023]
Abstract
Rapid identification of unknown material samples using portable or handheld Raman spectroscopy detection equipment is becoming a common analytical tool. However, the design and implementation of a set of Raman spectroscopy-based devices for substance identification must include spectral sampling of standard reference substance samples, resolution matching between different devices, and the training process of the corresponding classification models. The process of selecting a suitable classification model is frequently time-consuming, and when the number of classes of substances to be recognised increases dramatically, recognition accuracy decreases dramatically. In this paper, we propose a fast classification method for Raman spectra based on deep metric learning networks combined with the Gramian angular difference field (GADF) image generation approach. First, we uniformly convert Raman spectra acquired at different resolutions into GADF images of the same resolution, addressing spectral dimension disparities induced by resolution differences in different Raman spectroscopy detection devices. Second, a network capable of implementing nonlinear distance measurements between GADF images of different classes of substances is designed based on a deep metric learning approach. The Raman spectra of 450 different mineral classes obtained from the RRUFF database were converted into GADF images and used to train this deep metric learning network. Finally, the trained network can be installed on an embedded computing platform and used in conjunction with portable or handheld Raman spectroscopic detection sensors to perform material identification tasks at various scales. A series of experiments demonstrate that our trained deep metric learning network outperforms existing mainstream machine learning models on classification tasks of different sizes. For the two tasks of Raman spectral classification of natural minerals of 260 classes and Raman spectral classification of pathogenic bacteria of 8 classes with significant noise, our suggested model achieved 98.05% and 90.13% classification accuracy, respectively. Finally, we also deployed the model in a handheld Raman spectrometer and conducted identification experiments on 350 samples of chemical substances attributed to 32 classes, achieving a classification accuracy of 99.14%. These results demonstrate that our method can greatly improve the efficiency of developing Raman spectroscopy-based substance detection devices and can be widely used in tasks of unknown substance identification.
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Affiliation(s)
- Yaoyi Cai
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410083, PR China; Xiangji Haidun Technology Co., Ltd., Changsha, Hunan 410199, PR China
| | - Zekai Yao
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410083, PR China
| | - Xi Cheng
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410083, PR China
| | - Yixuan He
- State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410083, PR China
| | - Shiwen Li
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410083, PR China
| | - Jiaji Pan
- College of Engineering and Design, Hunan Normal University, Changsha, Hunan 410083, PR China; State Key Laboratory of Developmental Biology of Freshwater Fish, College of Life Sciences, Hunan Normal University, Changsha, Hunan 410083, PR China.
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Luo Y, Su W, Xu D, Wang Z, Wu H, Chen B, Wu J. Component identification for the SERS spectra of microplastics mixture with convolutional neural network. THE SCIENCE OF THE TOTAL ENVIRONMENT 2023; 895:165138. [PMID: 37379925 DOI: 10.1016/j.scitotenv.2023.165138] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/07/2023] [Revised: 06/16/2023] [Accepted: 06/24/2023] [Indexed: 06/30/2023]
Abstract
With the increasing interest in microplastics (MPs) pollutants, relevant detection technologies are also developing. In MPs analysis, vibrational spectroscopy represented by surface-enhanced Raman spectroscopy (SERS) is widely used because they can provide unique fingerprint characteristics of chemical components. However, it is still a challenge to separate various chemical components from the SERS spectra of MPs mixture. In this study, it is innovatively proposed to combine the convolutional neural networks (CNN) model to simultaneously identify and analyze each component in the SERS spectra of six common MPs mixture. Different from the traditional method, which requires a series of spectral preprocessing such as baseline correction, smoothing and filtering, the average identification accuracy of MP components is as high as 99.54 % after the unpreprocessed spectral data is trained by CNN, which is better than other classical algorithms such as support vector machine (SVM), principal component analysis linear discriminant analysis (PCA-LDA), partial least squares discriminant analysis (PLS-DA), Random Forest (RF), and K Near Neighbor (KNN), with or without spectral preprocessing. The high accuracy shows that CNN can be used to quickly identify MPs mixture with unpreprocessed SERS spectra data.
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Affiliation(s)
- Yinlong Luo
- College of Science, Hohai University, Changzhou 213022, China
| | - Wei Su
- College of Science, Hohai University, Changzhou 213022, China.
| | - Dewen Xu
- College of Science, Hohai University, Changzhou 213022, China
| | - Zhenfeng Wang
- College of Science, Hohai University, Changzhou 213022, China
| | - Hong Wu
- School of Science, Nanjing University of Posts and Telecommunications, Nanjing 210003, China
| | - Bingyan Chen
- College of Science, Hohai University, Changzhou 213022, China
| | - Jian Wu
- College of Advanced Interdisciplinary Studies, National University of Defense Technology, Changsha 410003, China
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