Task-Specific Multimodal Imaging and Spectroscopy for Post-Mortem Interval Assessment in Human Skeletal Remains: An Exploratory Decision-Support Framewor

Background/Objectives: Estimating the post-mortem interval (PMI) of human skeletal remains remains challenging because bone undergoes structural, molecular and optical changes that evolve differently over time and are strongly influenced by taphonomic conditions. Rather than assuming that a single multimodal classifier performs equally well across all PMI intervals, this study aimed to determine which imaging or spectroscopic modality is most informative for specific forensic decision tasks and to derive an exploratory task-specific diagnostic decision-support framework based on internally evaluated sample-level analyses. Methods: Human femoral bone samples were assigned to five PMI classes ranging from 0–2 weeks to >100 years and examined using micro-computed tomography, hyperspectral imaging, handheld and microscopic Raman spectroscopy, and NIR-ONE spectroscopy. Repeated acquisitions were aggregated at the physical-sample level. Four targeted diagnostic contrasts were defined for the present reanalysis: archaeological class 5 versus classes 1–4, early classes 1 + 2 versus later classes 4 + 5, classes 1 + 2 versus class 4 within the Raman-compatible forensic range, and class 1 versus class 2. In addition, an exploratory direct pairwise comparison of class 4 versus class 5 was performed to specifically assess the boundary between the latest forensic interval and archaeological material. Parameter-wise ROC analysis, bootstrap confidence intervals, exploratory operating points at approximately 95% specificity, and repeated stratified cross-validation were used. All preprocessing for multivariate modelling was performed within the respective cross-validation folds. Results: Diagnostic performance was strongly task-dependent. Archaeological class 5 was distinguished from classes 1–4 by micro-CT Mean2 (AUC 0.984), NIR-ONE reflectance at 1944 nm (AUC 0.980), and HSI-derived tissue water index (TWI; AUC 0.961). In the additional exploratory direct C4-versus-C5 analysis, micro-CT Mean2 showed complete separation of the available samples (AUC 1.000), while NIR-ONE reflectance at 1944 nm retained excellent discriminatory performance (AUC 0.963). In contrast, HSI-derived TWI showed only moderate direct C4-versus-C5 discrimination (AUC 0.742). For early classes 1 + 2 versus later classes 4 + 5, the device-derived HSI StO2 index showed the highest univariate performance (AUC 0.940), followed by TWI (AUC 0.894) and the NIR spectral slope between 1550 and 1950 nm (AUC 0.860). Within the Raman-compatible forensic range, HSI remained highly informative, while Raman carbonate/phosphate and crystallinity parameters provided complementary molecular information. Class 1 versus class 2 discrimination remained moderate. Corrected five-class modelling achieved only moderate balanced accuracy and did not consistently improve upon NIR-ONE alone. Conclusions: Diagnostic performance was strongly task-dependent. In this internally evaluated cohort, micro-CT Mean2 and high-wavelength NIR-ONE features showed the strongest discrimination of archaeological-compatible C5 material, whereas HSI-derived optical indices were most informative for the separated early-versus-later contrast and Raman spectroscopy provided complementary molecular information within C1–C4. Because C5 comprised only six unique physical samples and archaeological context was confounded with chronological age, the very high C5-related AUC estimates should be regarded as exploratory, hypothesis-generating estimates rather than validated measures of chronological PMI. The proposed decision-support framework is likewise exploratory and requires independent external validation before forensic implementation.