General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning
Raman spectroscopy is a powerful tool for identifying substances, yet accurately analyzing mixtures remains challenging due to overlapping spectra. This study aimed to develop a deep learning-based framework to improve the identification of components in mixtures using Raman spectroscopy. We propose...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/14/22/10245 |
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| author | Yilong Zhang Tianke Wang Kang Du Peng Chen Haixia Wang Haohao Sun |
| author_facet | Yilong Zhang Tianke Wang Kang Du Peng Chen Haixia Wang Haohao Sun |
| author_sort | Yilong Zhang |
| collection | DOAJ |
| description | Raman spectroscopy is a powerful tool for identifying substances, yet accurately analyzing mixtures remains challenging due to overlapping spectra. This study aimed to develop a deep learning-based framework to improve the identification of components in mixtures using Raman spectroscopy. We propose a three-branch feature fusion network that leverages spectral pairwise comparison and a multi-head self-attention mechanism to capture both local and global spectral features. To address limited data availability, traditional data augmentation techniques were combined with deep convolutional generative adversarial networks (DCGAN) to expand the dataset. Our framework significantly outperformed existing Raman spectroscopy-based methods in both qualitative and quantitative analyses. The model demonstrated superior accuracy compared to U-Net and ResNext, achieving higher detection accuracy for mixture components. This framework offers a promising solution for improving mixture identification in Raman spectroscopy, with potential applications in industries such as pharmaceuticals, food safety, and environmental monitoring. |
| format | Article |
| id | doaj-art-1da245f1155849e6b62b1d07d90684e5 |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-1da245f1155849e6b62b1d07d90684e52024-11-26T17:48:01ZengMDPI AGApplied Sciences2076-34172024-11-0114221024510.3390/app142210245General Network Framework for Mixture Raman Spectrum Identification Based on Deep LearningYilong Zhang0Tianke Wang1Kang Du2Peng Chen3Haixia Wang4Haohao Sun5School of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310018, ChinaSchool of Computer Science and Technology, Zhejiang University of Technology, Hangzhou 310018, ChinaRaman spectroscopy is a powerful tool for identifying substances, yet accurately analyzing mixtures remains challenging due to overlapping spectra. This study aimed to develop a deep learning-based framework to improve the identification of components in mixtures using Raman spectroscopy. We propose a three-branch feature fusion network that leverages spectral pairwise comparison and a multi-head self-attention mechanism to capture both local and global spectral features. To address limited data availability, traditional data augmentation techniques were combined with deep convolutional generative adversarial networks (DCGAN) to expand the dataset. Our framework significantly outperformed existing Raman spectroscopy-based methods in both qualitative and quantitative analyses. The model demonstrated superior accuracy compared to U-Net and ResNext, achieving higher detection accuracy for mixture components. This framework offers a promising solution for improving mixture identification in Raman spectroscopy, with potential applications in industries such as pharmaceuticals, food safety, and environmental monitoring.https://www.mdpi.com/2076-3417/14/22/10245Raman spectroscopydeep learningfeature fusionattention mechanismconvolutional generative adversarial networks |
| spellingShingle | Yilong Zhang Tianke Wang Kang Du Peng Chen Haixia Wang Haohao Sun General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning Applied Sciences Raman spectroscopy deep learning feature fusion attention mechanism convolutional generative adversarial networks |
| title | General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning |
| title_full | General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning |
| title_fullStr | General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning |
| title_full_unstemmed | General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning |
| title_short | General Network Framework for Mixture Raman Spectrum Identification Based on Deep Learning |
| title_sort | general network framework for mixture raman spectrum identification based on deep learning |
| topic | Raman spectroscopy deep learning feature fusion attention mechanism convolutional generative adversarial networks |
| url | https://www.mdpi.com/2076-3417/14/22/10245 |
| work_keys_str_mv | AT yilongzhang generalnetworkframeworkformixtureramanspectrumidentificationbasedondeeplearning AT tiankewang generalnetworkframeworkformixtureramanspectrumidentificationbasedondeeplearning AT kangdu generalnetworkframeworkformixtureramanspectrumidentificationbasedondeeplearning AT pengchen generalnetworkframeworkformixtureramanspectrumidentificationbasedondeeplearning AT haixiawang generalnetworkframeworkformixtureramanspectrumidentificationbasedondeeplearning AT haohaosun generalnetworkframeworkformixtureramanspectrumidentificationbasedondeeplearning |