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

arXiv: https://arxiv.org/abs/1708.09022 · HTML: https://mlsanity.com/paper/1708.09022/

## Key facts

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

## Built on by (most cited first, 25 of 25)

- [RamanNet: A generalized neural network architecture for Raman Spectrum Analysis](https://mlsanity.com/paper/2201.09737/) (2022)
- [A universal synthetic dataset for machine learning on spectroscopic data](https://mlsanity.com/paper/2206.06031/) (2022)
- [Raman spectroscopy in open world learning settings using the Objectosphere approach](https://mlsanity.com/paper/2111.06268/) (2021)
- [Raman Spectrum Matching with Contrastive Representation Learning](https://mlsanity.com/paper/2202.12549/) (2022)
- [Automatic Neural Network Hyperparameter Optimization for Extrapolation: Lessons Learned from Visible and Near-Infrared Spectroscopy of Mango Fruit](https://mlsanity.com/paper/2210.01124/) (2022)
- [SpectrumWorld: Artificial Intelligence Foundation for Spectroscopy](https://mlsanity.com/paper/2508.01188/) (2025)
- [PGNAA Spectral Classification of Metal with Density Estimations](https://mlsanity.com/paper/2208.13836/) (2022)
- [Auto-Encoder Neural Network Incorporating X-Ray Fluorescence Fundamental Parameters with Machine Learning](https://mlsanity.com/paper/2210.12239/) (2022)
- [Skin cancer diagnosis using NIR spectroscopy data of skin lesions in vivo using machine learning algorithms](https://mlsanity.com/paper/2401.01200/) (2024)
- [Reevaluating Convolutional Neural Networks for Spectral Analysis: A Focus on Raman Spectroscopy](https://mlsanity.com/paper/2509.25964/) (2025)
- [Benchmarking Deep Learning Models for Raman Spectroscopy Across Open-Source Datasets](https://mlsanity.com/paper/2601.16107/) (2026)
- [RAMANMETRIX: a delightful way to analyze Raman spectra](https://mlsanity.com/paper/2201.07586/) (2022)
- [Identifying charge density and dielectric environment of graphene using Raman spectroscopy and deep learning](https://mlsanity.com/paper/2203.00431/) (2022)
- [SCAI: A Spectral data Classification framework with Adaptive Inference for the IoT platform](https://mlsanity.com/paper/2206.12420/) (2022)
- [Spectrum-BERT: Pre-training of Deep Bidirectional Transformers for Spectral Classification of Chinese Liquors](https://mlsanity.com/paper/2210.12440/) (2022)
- [Assessing the Performance of 1D-Convolution Neural Networks to Predict Concentration of Mixture Components from Raman Spectra](https://mlsanity.com/paper/2306.16621/) (2023)
- [From Spectra to Geography: Intelligent Mapping of RRUFF Mineral Data](https://mlsanity.com/paper/2411.11693/) (2024)
- [Self-Calibrated Dual Contrasting for Annotation-Efficient Bacteria Raman Spectroscopy Clustering and Classification](https://mlsanity.com/paper/2412.20060/) (2024)
- [Using machine learning to map simulated noisy and laser-limited multidimensional spectra to molecular electronic couplings](https://mlsanity.com/paper/2503.15706/) (2025)
- [OASIS: A Deep Learning Framework for Universal Spectroscopic Analysis Driven by Novel Loss Functions](https://mlsanity.com/paper/2509.11499/) (2025)
- [Rapid Machine Learning-Driven Detection of Pesticides and Dyes Using Raman Spectroscopy](https://mlsanity.com/paper/2511.12167/) (2025)
- [A Brain-Inspired Deep Separation Network for Single Channel Raman Spectra Unmixing](https://mlsanity.com/paper/2604.22324/) (2026)
- [Reconstructing Randomly Masked Spectra Helps DNNs Identify Discriminant Wavenumbers](https://mlsanity.com/paper/2606.21289/) (2026)
- [Attributing Preprocessing Invariance in Spectral Foundation Models](https://mlsanity.com/paper/2608.14227/) (2026)
- [Sharpness-Aware Minimization (SAM) Improves Classification Accuracy of Bacterial Raman Spectral Data Enabling Portable Diagnostics](https://mlsanity.com/paper/2609.19453/) (2026)

## 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.