mlsanityData to 14 Oct 2026

Deep Convolutional Neural Networks for Raman Spectrum Recognition: A Unified Solution

arXiv 1708.09022 ↗First posted 18 Aug 2017cs.LG stat.MLcited by 25

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Abstract (arXiv)

Machine learning methods have found many applications in Raman spectroscopy, especially for the identification of chemical species. However, almost all of these methods require non-trivial preprocessing such as baseline correction and/or PCA as an essential step. Here we describe our unified solution for the identification of chemical species in which a convolutional neural network is trained to automatically identify substances according to their Raman spectrum without the need of ad-hoc preprocessing steps. We evaluated our approach using the RRUFF spectral database, comprising mineral sample data. Superior classification performance is demonstrated compared with other frequently used machine learning algorithms including the popular support vector machine.

Key facts. Cited by 25 papers in the map (data through 14 Oct 2026). First posted on arXiv 18 Aug 2017.

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 1; 2022: 10; 2023: 1; 2024: 3; 2025: 5; 2026: 5.

25 papers.

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  1. 2022RamanNet: A generalized neural network architecture for Raman Spectrum Analysiscited by 5
  2. 2022A universal synthetic dataset for machine learning on spectroscopic datacited by 3
  3. 2021Raman spectroscopy in open world learning settings using the Objectosphere approachcited by 2
  4. 2022Raman Spectrum Matching with Contrastive Representation Learningcited by 2
  5. 2022Automatic Neural Network Hyperparameter Optimization for Extrapolation: Lessons Learned from Visible and Near-Infrared Spectroscopy of Mango Fruitcited by 2
  6. 2025SpectrumWorld: Artificial Intelligence Foundation for Spectroscopycited by 2
  7. 2022PGNAA Spectral Classification of Metal with Density Estimationscited by 1
  8. 2022Auto-Encoder Neural Network Incorporating X-Ray Fluorescence Fundamental Parameters with Machine Learningcited by 1
  9. 2024Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithmscited by 1
  10. 2025Reevaluating Convolutional Neural Networks for Spectral Analysis: A Focus on Raman Spectroscopycited by 1
  11. 2026Benchmarking Deep Learning Models for Raman Spectroscopy Across Open-Source Datasetscited by 1
  12. 2022RAMANMETRIX: a delightful way to analyze Raman spectracited by 0
13 more
  1. 2022Identifying charge density and dielectric environment of graphene using Raman spectroscopy and deep learningcited by 0
  2. 2022SCAI: A Spectral data Classification framework with Adaptive Inference for the IoT platformcited by 0
  3. 2022Spectrum-BERT: Pre-training of Deep Bidirectional Transformers for Spectral Classification of Chinese Liquorscited by 0
  4. 2023Assessing the Performance of 1D-Convolution Neural Networks to Predict Concentration of Mixture Components from Raman Spectracited by 0
  5. 2024From Spectra to Geography: Intelligent Mapping of RRUFF Mineral Datacited by 0
  6. 2024Self-Calibrated Dual Contrasting for Annotation-Efficient Bacteria Raman Spectroscopy Clustering and Classificationcited by 0
  7. 2025Using machine learning to map simulated noisy and laser-limited multidimensional spectra to molecular electronic couplingscited by 0
  8. 2025OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functionscited by 0
  9. 2025Rapid Machine Learning-Driven Detection of Pesticides and Dyes Using Raman Spectroscopycited by 0
  10. 2026A Brain-Inspired Deep Separation Network for Single Channel Raman Spectra Unmixingcited by 0
  11. 2026Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumberscited by 0
  12. 2026Attributing Preprocessing Invariance in Spectral Foundation Modelscited by 0
  13. 2026Sharpness-Aware Minimization (SAM) Improves Classification Accuracy of Bacterial Raman Spectral Data Enabling Portable Diagnosticscited by 0