# Raman spectral analysis of mixtures with one-dimensional convolutional neural network

arXiv: https://arxiv.org/abs/2106.05316 · HTML: https://mlsanity.com/paper/2106.05316/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 4 of its 37 references that are in the map.
- First posted on arXiv 1 Jun 2021.

## Builds on (most cited first, 4 of 4)

- [Rapid identification of pathogenic bacteria using Raman spectroscopy and deep learning](https://mlsanity.com/paper/1901.07666/) (2019)
- [Data Augmentation of Spectral Data for Convolutional Neural Network (CNN) Based Deep Chemometrics](https://mlsanity.com/paper/1710.01927/) (2017)
- [Feature visualization of Raman spectrum analysis with deep convolutional neural network](https://mlsanity.com/paper/2007.13354/) (2020)
- [A Review of 1D Convolutional Neural Networks toward Unknown Substance Identification in Portable Raman Spectrometer](https://mlsanity.com/paper/2006.10575/) (2020)

## Abstract (arXiv)

Recently, the combination of robust one-dimensional convolutional neural networks (1-D CNNs) and Raman spectroscopy has shown great promise in rapid identification of unknown substances with good accuracy. Using this technique, researchers can recognize a pure compound and distinguish it from unknown substances in a mixture. The novelty of this approach is that the trained neural network operates automatically without any pre- or post-processing of data. Some studies have attempted to extend this technique to the classification of pure compounds in an unknown mixture. However, the application of 1-D CNNs has typically been restricted to binary classifications of pure compounds. Here we will highlight a new approach in spectral recognition and quantification of chemical components in a multicomponent mixture. Two 1-D CNN models, RaMixNet I and II, have been developed for this purpose. The former is for rapid classification of components in a mixture while the latter is for quantitative determination of those constituents. In the proposed method, there is no limit to the number of compounds in a mixture. A data augmentation method is also introduced by adding random baselines to the Raman spectra. The experimental results revealed that the classification accuracy of RaMixNet I and II is 100% for analysis of unknown test mixtures; at the same time, the RaMixNet II model may achieve a regression accuracy of 88% for the quantification of each component.