Fluorescence Spectroscopy

Fluorescence spectroscopy is an optical technique used to analyze the fluorescence emitted by substances, providing insights into their chemical composition and properties. Current research focuses on applying this technique in diverse fields, leveraging machine learning algorithms like deep learning (including convolutional neural networks and diffusion probabilistic models) and XGBoost to analyze complex spectral data for applications such as medical imaging (e.g., cancer detection), food quality assessment (e.g., olive oil grading), and nanoparticle tracking. These advancements enable rapid, cost-effective, and often non-destructive analysis, impacting various sectors by improving quality control, diagnostics, and process optimization.