Summary of Deep Learning Domain Adaptation to Understand Physico-chemical Processes From Fluorescence Spectroscopy Small Datasets: Application to Ageing Of Olive Oil, by Umberto Michelucci et al.
Deep Learning Domain Adaptation to Understand Physico-Chemical Processes from Fluorescence Spectroscopy Small Datasets: Application to Ageing of Olive Oil
by Umberto Michelucci, Francesca Venturini
First submitted to arxiv on: 14 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI); Data Analysis, Statistics and Probability (physics.data-an); Optics (physics.optics)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A new approach is proposed to overcome challenges in analyzing fluorescence excitation-emission matrices (EEMs) with deep learning. The method combines domain adaptation with pretrained vision models and a novel interpretability algorithm, enabling deeper insights into physico-chemical processes. This was demonstrated by analyzing the oxidation process in extra virgin olive oil (EVOO) during ageing, predicting quality indicators, and identifying spectral bands and molecules involved. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses deep learning to analyze fluorescence spectroscopy data, which is important for applications like environmental monitoring, food quality control, and biomedical diagnostics. The approach helps understand complex biological and chemical processes by providing deeper insights into the data. |
Keywords
» Artificial intelligence » Deep learning » Domain adaptation
