Dense Convolutional Neural Network for Identification of Raman Spectra Article Swipe
Wei Zhou
,
Ziheng Qian
,
Xinyuan Ni
,
Yujun Tang
,
Hanming Guo
,
Songlin Zhuang
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23177433
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23177433
The rapid development of cloud computing and deep learning makes the intelligent modes of applications widespread in various fields. The identification of Raman spectra can be realized in the cloud, due to its powerful computing, abundant spectral databases and advanced algorithms. Thus, it can reduce the dependence on the performance of the terminal instruments. However, the complexity of the detection environment can cause great interferences, which might significantly decrease the identification accuracies of algorithms. In this paper, a deep learning algorithm based on the Dense network has been proposed to satisfy the realization of this vision. The proposed Dense convolutional neural network has a very deep structure of over 40 layers and plenty of parameters to adjust the weight of different wavebands. In the kernel Dense blocks part of the network, it has a feed-forward fashion of connection for each layer to every other layer. It can alleviate the gradient vanishing or explosion problems, strengthen feature propagations, encourage feature reuses and enhance training efficiency. The network’s special architecture mitigates noise interferences and ensures precise identification. The Dense network shows more accuracy and robustness compared to other CNN-based algorithms. We set up a database of 1600 Raman spectra consisting of 32 different types of liquid chemicals. They are detected using different postures as examples of interfered Raman spectra. In the 50 repeated training and testing sets, the Dense network can achieve a weighted accuracy of 99.99%. We have also tested the RRUFF database and the Dense network has a good performance. The proposed approach advances cloud-enabled Raman spectra identification, offering improved accuracy and adaptability for diverse identification tasks.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23177433
- https://www.mdpi.com/1424-8220/23/17/7433/pdf?version=1692973592
- OA Status
- gold
- Cited By
- 10
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4386223603
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4386223603Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s23177433Digital Object Identifier
- Title
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Dense Convolutional Neural Network for Identification of Raman SpectraWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-08-25Full publication date if available
- Authors
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Wei Zhou, Ziheng Qian, Xinyuan Ni, Yujun Tang, Hanming Guo, Songlin ZhuangList of authors in order
- Landing page
-
https://doi.org/10.3390/s23177433Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/23/17/7433/pdf?version=1692973592Direct link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.mdpi.com/1424-8220/23/17/7433/pdf?version=1692973592Direct OA link when available
- Concepts
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Convolutional neural network, Identification (biology), Raman spectroscopy, Artificial neural network, Artificial intelligence, Computer science, Pattern recognition (psychology), Physics, Optics, Biology, BotanyTop concepts (fields/topics) attached by OpenAlex
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10Total citation count in OpenAlex
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2025: 3, 2024: 5, 2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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