2024/06/14 by Umberto Michelucci, Michelucci, Umberto, Francesca Venturini +1
Biochemistry, Genetics and Molecular Biology · Environmental Science · #Artificial Intelligence (cs.AI) #Data Analysis #FOS: Computer and information sciences #FOS: Physical sciences #Machine Learning (cs.LG) #Optics (physics.optics) #Spectroscopy Techniques in Biomedical and Chemical Research #Statistics and Probability (physics.data-an) #Water Quality Monitoring and Analysis
paper · pdf · doi:10.48550/arxiv.2406.10031
openalex publication_date 2024/06/14 · openalex created_date 2024/06/18 · openalex updated_date 2026/07/28
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.