  Feature selection of high-dimensional labeled data with limited observations
is critical for making powerful predictive modeling accessible, scalable, and
interpretable for domain experts. Spectroscopy data, which records the
interaction between matter and electromagnetic radiation, particularly holds a
lot of information in a single sample. Since acquiring such high-dimensional
data is a complex task, it is crucial to exploit the best analytical tools to
extract necessary information. In this paper, we investigate the most commonly
used feature selection techniques and introduce applying recent explainable AI
techniques to interpret the prediction outcomes of high-dimensional and limited
spectral data. Interpretation of the prediction outcome is beneficial for the
domain experts as it ensures the transparency and faithfulness of the ML models
to the domain knowledge. Due to the instrument resolution limitations,
pinpointing important regions of the spectroscopy data creates a pathway to
optimize the data collection process through the miniaturization of the
spectrometer device. Reducing the device size and power and therefore cost is a
requirement for the real-world deployment of such a sensor-to-prediction system
as a whole. We specifically design three different scenarios to ensure that the
evaluation of ML models is robust for the real-time practice of the developed
methodologies and to uncover the hidden effect of noise sources on the final
outcome.
