Explosive Detection - Raman Spectrum Recognition
Uses Deep Convolutional Neural Networks for classification of chemicals present in an explosive from their Raman Spectrum.
- Data Preprocessing
- Smoothening by Savitzky Golay filter
- Derivatization of spectra
- Normalization
- Principal Component Analysis (PCA) for dimentionality reduction. (Optional)
- Deep Neural Network (Multi-layer Perceptron architecture) for classification.
Hardware and Software used
Hardware |
Specs |
Processor |
Intel i7 |
RAM |
4 GB |
HDD |
1 TB |
GPU |
12GB NVIDIA Tesla K80 GPU |
Software |
Details |
Operating System |
Linux |
Development Environment |
Google Colab, Jupyter notebook |
Language and Libraries |
Python and libraries (Pandas, Scikit-learn, Matplotlib), Tensorflow, Keras |
- Spectra of chemicals including Sulphur, Acetone, Urea, DNT, DMSO, AN, Ethyl aclcohol, Nepthalene, HMX, PNBA etc.
- Data for Open-souce distribution: RRUFF Dataset consisting of 3700 spectrum samples.
Liu J, Osadchy M, Ashton L, Foster M, Solomon CJ, Gibson SJ. Deep convolutional neural networks for Raman spectrum recognition: a unified solution. Analyst. 2017;142(21):4067-74.