  Fluorescence spectroscopy is a fundamental tool in life sciences and
chemistry, widely used for applications such as environmental monitoring, food
quality control, and biomedical diagnostics. However, analysis of spectroscopic
data with deep learning, in particular of fluorescence excitation-emission
matrices (EEMs), presents significant challenges due to the typically small and
sparse datasets available. Furthermore, the analysis of EEMs is difficult due
to their high dimensionality and overlapping spectral features. This study
proposes a new approach that exploits domain adaptation with pretrained vision
models, alongside a novel interpretability algorithm to address these
challenges. Thanks to specialised feature engineering of the neural networks
described in this work, we are now able to provide deeper insights into the
physico-chemical processes underlying the data. The proposed approach is
demonstrated through the analysis of the oxidation process in extra virgin
olive oil (EVOO) during ageing, showing its effectiveness in predicting quality
indicators and identifying the spectral bands, and thus the molecules involved
in the process. This work describes a significantly innovative approach in the
use of deep learning for spectroscopy, transforming it from a black box into a
tool for understanding complex biological and chemical processes.
