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.