Direct recognition of Raman spectra without baseline correction based on deep learning

Abstract

Raman spectroscopy, widely used for material analysis, has formed an extensive spectral library. In practical applications, it is usually necessary to preprocess Raman spectroscopy of the target material and then identify the material through spectral-library comparisons. Baseline correction is an important step during pre-processing and it usually requires a special algorithm. However, it demands time and high-level professional skill, confining Raman spectroscopy to laboratories rather than large-scale applications. Therefore, to improve its efficiency and take advantage of the big data in the spectral library, this paper proposes a simple data-augmented deep learning method to achieve Raman spectroscopy recognition without baseline correction. In this method, a simple mathematical baseline (linear or sine function) is added to the spectrum in the database to complete data augmentation. Its training set is used to train the deep learning model. The trained deep-learning model can identify 20 minerals with 100% accuracy without additional baseline correction. Therefore, the method is effective for rapid and direct recognition of Raman spectra.