Classification of Hazardous Chemicals with Raman Spectrum by Convolution Neural Network

Abstract

Dangerous chemicals have always been the hidden danger of social security, how to accurately identify chemicals is very important. In this experiment, the Raman scattering instrument will provide us with the Raman spectrum signal of about 190 chemical substances, each of which has its own characteristics. However, the traditional methods of identifying and classifying chemicals are not only inefficient, but also lack of security. This study proved the feasibility of using neural network to classify chemical substances. For one-dimensional signal, the experiment mainly uses the semi-supervised learning method to establish the 1D-DCNN model and simulate the real noise environment. One-dimensional signal is used as input and then the model is trained to get the model. The experimental results show that the accuracy of toxic and toxic, flammable, corrosive, environment hazard, health hazard, safe, expansive, harmful classification is 99% ± 1%. This shows that the 1D-DCNN model has strong anti-interference and robustness for signals in noise environments. This rapid classification method will provide reference value for the identification of chemical substances.