Raman spectral preprocessing using multitask deep-learning network

Raman spectroscopy technology is widely used in various fields due to its advantages such as non-destructiveness, speed, and high sensitivity. To make effective use of this technology, preprocessing operations including additive noise reduction and baseline correction are usually required. Traditional preprocessing tasks involve the appropriate selection of parameters and methods. To address these challenges, we proposed using a multi-task deep learning network for preprocessing. This network is built on ResNet and can perform baseline correction and noise removal simultaneously. To train the deep learning network, we generate training data using mathematical methods to overcome the problem of data scarcity. We verified the superiority of our method compared to existing preprocessing methods using both simulated and real Raman spectral data.