Abstract
<jats:p> Post-mortem interval (PMI) estimation is an important element in medico-legal death investigations, as reliable estimates can establish timelines and generate investigative leads. Various analytical techniques are used to monitor biomolecular degradation throughout decomposition, with promising results obtained from fluorescence spectroscopy. In this work, we analyzed the fluorescent profile of cell lysates from 96 biopsy punches collected from 14 human cadavers at a human taphonomic facility in Québec, Canada, with PMIs ranging from 2 to 1098 days. Excitation-emission matrices (EEMs) were acquired for each lysate and deconvoluted using parallel factor analysis for fluorophore identification. We analyzed both composite and tissue-specific (muscle, adipose, and skin) samples. Overall, tryptophan fluorescence decreased with increasing PMI, whereas fluorescence between 400-500 nm, attributed to fluorescent oxidation products, increased. Fluorescence in skin and muscle was dominated by tryptophan at early PMIs, whereas adipose exhibited minimal signal. As PMI increased, EEM fluorescence became progressively more homogeneous across tissue types, indicating that tissue-specific contributions decrease as decomposition advances. Six machine learning models were built from the composite samples to predict PMI, and XGBoost performed best, with an R <jats:sup>2 </jats:sup> of 0.88 and RMSE of 127.2 days, corresponding to an error of 11.6% relative to the maximum PMI. Our findings demonstrate that fluorescence analysis of cell lysates is practical and yields accurate PMI estimates, provides important insight into decomposition-related biomolecular degradation, and can also be readily integrated into DNA/RNA co-extractions. </jats:p>