Adversarial nets for baseline correction in spectra processing

Abstract

Abstract Almost all kinds of spectra data, such as Raman spectroscopy, X-Ray Diffraction (XRD), mass spectroscopy, and infrared spectroscopy, etc., are interrupted by baseline drifts. This large-scale background fluctuation seriously affects the identification of signals. Traditional baseline recognition methods require manual parameters to achieve better performance. In this article, a deep learning scheme is proposed that provides a strategy for generating sufficient training data and the construction of a baseline recognition model using adversarial nets. The new scheme is an intelligent system that has substantial advantages in automation. The model is named as Baseline Recognition Networks. The new method offers better performance both in terms of qualitative and quantitative studies.