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 mainly 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 and
meaningful 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), showing its effectiveness in
predicting quality indicators and identifying relevant spectral bands. This
work describes significantly innovative results in the use of deep learning for
spectroscopy, transforming it from a black box into a tool for understanding
complex biological and chemical processes.

该研究提出了一种新方法，利用预训练的视觉模型进行领域适应，并结合新的可解释性算法，以解决荧光光谱数据（尤其是激发 - 发射矩阵）分析中的挑战。通过对特定特征工程的神经网络的描述，我们现在能够更深入、更有意义地理解数据背后的物理化学过程。该方法通过对初榨橄榄油（EVOO）中的氧化过程进行分析来进行论证，展示了在预测质量指标和识别相关光谱带方面的有效性。该研究在光谱学领域中使用深度学习取得显著创新性结果，将其从黑盒转变为理解复杂生物和化学过程的工具。