Endogenous Fluorescence

Endogenous fluorescence, the light emitted by naturally occurring molecules within biological or chemical samples, is increasingly studied to extract valuable information about sample composition and processes. Current research focuses on leveraging deep learning, particularly convolutional neural networks and autoencoders, to analyze complex fluorescence data like excitation-emission matrices and hyperspectral images, overcoming challenges posed by high dimensionality and limited datasets. This allows for improved quantification of fluorophore abundances and physico-chemical properties, with applications ranging from food quality control and brain tumor surgery to vegetation monitoring and improved microscopic imaging. These advancements enable more precise and efficient analysis, leading to better diagnostics and process optimization across various fields.