Perspectives on deep learning for near-infrared spectral data modelling

Abstract

Deep learning for near-infrared spectral data is a recent topic of interest for near-infrared practitioners. In recent years, applications of deep learning are flourishing from analyses of point spectrometer data to hyperspectral image analysis. However, there are also some cases where simple partial least-squares based models are sufficient. This paper provides a concise view of the state of the art of deep learning for near-infrared data modelling, particularly discussing when deep learning is useful. Discussion is also provided on what is already achieved and what ideas would be interesting to pursue regarding deep learning modelling of near-infrared data.