Comparison of augmentation and pre-processing for deep learning and chemometric classification of infrared spectra

Abstract

Infrared spectroscopy hampered by physical distortions from scattering and instrumental effects. Therefore, it is generally accepted that spectra should be pre-processed before further data analysis. Deep learning community offers augmentation techniques as an alternative approach to deal with variability in the data. In this paper we propose an Extended Multiplicative Signal Augmentation (EMSA) method for augmenting physical distortions in infrared spectra. In order to study the effect of pre-processing and augmentation techniques we combine them with a wide range of classifiers from chemometrics, machine learning and statistics for four different spectral data sets. While the conventional pre-processing strategies perform well with all classifiers evaluated, augmentation can replace pre-processing when combined with Deep Convolutional Neural Networks and is especially successful when applied to small data sets.