Research on fentanyl classification model based on multi-dimensional parallel convolutional neural network and mass spectrometry data
In recent years,fentanyl and its analogues are easily synthesized,and the required chemicals and equipment,etc.,are not difficult to find,leading to numerous cases of overdose deaths from the abuse of fentanyl and its analogues worldwide.To avoid the circulation of this new drug and to improve the recognition rate of fentanyl and its analogs,a fentanyl classification model based on a multidimensional parallel convolutional neural network with mass spectrometry data is designed,and a SeLU activation function is used to prevent gradient disappearance.The model uses the Fcal loss function as a loss function to improve the recognition accuracy of similar species,splices the query mass spectrum and five classical reference spectra into five two-dimensional images,and finally inputs the original query one-dimensional mass spectrum and its spliced five two-dimensional images into the parallel convolutional neural network to classify the query mass spectrum.The Experimental results show that the recognition rate of 99.73％in the public dataset of the method verifies the corresponding effectiveness of the method.
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