Chemical substance classification using long short-term memory recurrent neural network
Jinlei Zhang, Junxiu Liu, Yuling Luo, Qiang Fu, Jinjie Bi, Senhui Qiu, Yi Cao, Xuemei Ding · 2017
This paper proposed a chemical substance detection method using the Long Short-Term Memory of Recurrent Neural Networks (LSTM-RNN). The chemical substance data was collected using a mass spectrometer which is a time-series data. The classification accuracy using the LSTM-RNN classifier is 96.84%, which is higher than 75.07% of the ordinary feed forward neural networks. The experimental results show that the LSTM-RNN can learn the properties of the chemical substance dataset and achieve a high detection accuracy.