ArticleRSC advances2022
Deeply-recursive convolutional neural network for Raman spectra identification.
Article in RSC advances, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Limits and benefits of unsupervised transfer learning for Raman spectroscopy-based bioprocess monitoring.Bioprocess and biosystems engineering · 2026Article
- Selection of Optimal Diagnostic Positions for Early Nutrient Deficiency in Cucumber Leaves Based on Spatial Distribution of Raman Spectra.Plants (Basel, Switzerland) · 2025Article
- Article
- Dense Convolutional Neural Network for Identification of Raman Spectra.Sensors (Basel, Switzerland) · 2023Article
- Deep-learning framework for fully-automated recognition of TiOScientific reports · 2022Article
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Authors and funding
5 authors.
Funding
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Abstract
Raman spectroscopy has been widely used in various fields due to its unique and superior properties. For achieving high spectral identification speeds and high accuracy, machine learning methods have found many applications in this area, with convolutional neural network-based methods showing great advantages. In this study, we propose a Raman spectral identification method using a deeply-recursive convolutional neural network (DRCNN). It has a very deep network structure (up to 16 layers) for improving performance without introducing more parameters for recursive layers, which eases the difficulty of training. We also propose a recursive-supervision extension to ease the difficulty of training. By testing several different open-source spectral databases, DRCNN has achieved higher prediction accuracies and better performance in transfer learning compared with other CNN-based methods. Superior identification performance is demonstrated, especially by identification, for characteristically similar and indistinguishable spectra.
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Registered trials
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