Benchmarking spectroscopic-chemometric models for human bloodstain time since deposition in tropical outdoor...

Reliable estimation of the time since deposition (TSD) of bloodstains remains a key challenge in forensic reconstruction, particularly under realistic outdoor conditions. This study benchmarks visible (Vis) reflectance, attenuated total reflectance-Fourier transform infrared (ATR-FTIR) and micro-Raman spectroscopy, each coupled with partial least squares (PLS) regression and artificial neural networks (ANN), for TSD estimation of human bloodstains aged up to 100 days under naturally fluctuating tropical outdoor microenvironments. Blood from multiple donors was deposited on glass slides and aged under two conditions: open-air exposure and enclosed storage in sealed plastic bags. Spectra were acquired from the outer ring of each stain, preprocessed by standard normal variate transformation and partitioned by donor into training and independent test sets. Vis reflectance primarily tracked haemoglobin chromophore changes via Soret and α/β bands, whereas ATR-FTIR highlighted protein secondary structure and hydration through amide I-III and O-H/N-H stretching features. Micro-Raman spectra emphasised porphyrin breathing and CC stretching modes, together with aromatic amino-acid and amide bands that remained informative even at late ageing stages. Across all techniques, PLS models provided robust quantitative performance (R²P up to 0.98, RMSEP typically ∼5-10 days, RPD > 3), with micro-Raman-PLS giving the highest predictive accuracy and enclosed exposure consistently improving model stability relative to open air. ANN models did not provide systematic advantages over PLS and were more sensitive to condition-specific variance. In the context of previous outdoor ATR-FTIR studies, these results show that established Vis-, ATR-FTIR- and Raman-based chemometric models can deliver competitive TSD estimates when calibrated within clearly defined environmental domains, underscoring the importance of donor-independent and environmental-aware validation for forensic interpretation.