  Deep learning methods are used on spectroscopic data to predict drug content
in tablets from near infrared (NIR) spectra. Using convolutional neural
networks (CNNs), features are ex- tracted from the spectroscopic data. Extended
multiplicative scatter correction (EMSC) and a novel spectral data augmentation
method are benchmarked as preprocessing steps. The learned models perform
better or on par with hypothetical optimal partial least squares (PLS) models
for all combinations of preprocessing. Data augmentation with subsequent EMSC
in combination gave the best results. The deep learning model CNNs also
outperform the PLS models in an extrapolation chal- lenge created using data
from a second instrument and from an analyte concentration not covered by the
training data. Qualitative investigations of the CNNs kernel activations show
their resemblance to wellknown data processing methods such as smoothing,
slope/derivative, thresholds and spectral region selection.
