1. Studies on the feasibility of using chemometric modeling of spectral data for the determination of post-mortem interval of skeletal remains. Kenneth W. Busch, Marianna A. Busch, Jody Dogra, Patricia Diamond Center for Analytical Spectroscopy Baylor University Waco, TX 76798 FACSS 2008

2. Post-mortem interval The post-mortem interval is the time that has elapsed since a person died. Post-mortem interval

3. Current Methodology algor mortis (body cooling) rigor mortis (stiffening of the limbs), livor mortis (settling of the blood in the body) Medical techniques— Applicable within a few hours of death

4. Current Methodology Entomological evidence— Once skeletonization has occurred, few techniques exist. Applicable up to 7 weeks

5. In regions like Texas with high heat and humidity, excarnation can occur relatively quickly. Skeletonization

6. The objective of this study was to determine whether spectroscopic examination of bone could be used to predict the PMI of skeletal remains. Research Objective

7. Following death, bones lose water and the proteins begin to decompose to amino acids. Living bone contains— About 12% water About 35% organic matter, primarily collagen Research Hypothesis

8. Since changes in water content and organic makeup of skeletal remains would occur following death, it was hypothesized— that these changes could be followed spectroscopically and correlated with PMI by means of PLS-regression modeling. Research Hypothesis

9. Diffuse reflectance NIR spectroscopy should be a good technique for this study. Sensitive to moisture and protein No sample preparation Non-destructive

10. Instrumentation NIR Spectrometer

11. Methodology Sample Preparation 8 cross sections 2mm thick were prepared from the bones. Rib bones from freshly slaughtered domesticated swine (Sus scrofa) were chosen for this study because pigs are widely used in forensic studies. Tissue was removed from the bones Bones were kept in the hood at room temperature for 3 months

12. Methodology Spectral Data Collection Spectral data in the form of log (1/R) were collected at 2 nm intervals from 1200 nm to 2200 nm for each sample. Spectra were collected about every 2 days for 90 days, resulting in 304 spectra. 13-point Savitky-Golay smoothing (6 points on either side) was done on the raw spectral data.

13. Methodology Data Analysis Spectral data were correlated with the known age of the bones with PLS regression modeling. Models were optimized in terms of wavelength range, PLS components, and outliers.

14. Smoothed diffuse reflectance spectra Arrows show location of water overtone and combination bands. Results

15. Expanded spectra showing general trend in displacement with time Notice that the spectra get closer together in the later time periods. Results

16. Plot of log(1/R) at 1450 nm versus time in days

17. Results Plot of log(1/R) versus log t for 1450 nm

18. Model for 90-day study using log t 5 PLS components (79% of Y variance); R 2 = 0.78

19. Validation Studies Regression models were validated by randomly removing 8 spectra from the data set. A model was made with the remaining spectra (277) The process was repeated 4 times.

20. 90-day model

21. For non-linear data, sometimes segmenting it into different regions can help. To test whether this would help, the data were divided into 3 sets and models were made for each. Month 1 Month 2 Month 3 Segmented Models

22. Validation results with segmented models

23. Using the segmented approach cuts the prediction error compared with the full 90-day model. But this creates a problem. For an unknown sample, how do we know which model to use? Can classification techniques like discriminant analysis help? Summary 1

24. PLS-Discriminant Analysis ModelClassification with discriminant analysis Month 1—blue Month 2—red Month 3—green

25. These sample spectra were randomly selected and correctly classified as belonging to month 1 before being predicted by the month 1 regression model. Classify and then predict approach for Month 1

26. Samples in blue were misclassified by DA and predicted by the wrong model (month 2). Classify and then predict approach for Month 2

27. Classify and then predict approach for Month 3 Average error for all 3 studies = 6. 6 days

28. 1.Conclusions limited to 90-day period (can’t extrapolate beyond data) 2.Diffuse reflectance NIR spectra of bones follow a logarithmic decrease with time at 1450 nm 3.Data can be linearized by using log t 4.Classification by DA followed by segmented PLS regression gives the best results 5.Typical margin of error about 6 days. Conclusions 1 Laboratory Studies

29. Field Study An initial field study was conducted— 3 slabs of ribs were placed outdoors (1 slab in an aquarium with potting soil, and 2 on the grass in wire mesh cages). These slabs received varying amounts of sun and shade. After the meat had decomposed, bone samples were cut from the bone and their spectra taken. 16 spectra were obtained corresponding to days 22, 23, 29, 30, and 38. Models were optimized in terms of outliers and wavelength range.

30. Field Study Spectral data for field study Sun/soil Shade/grass Sun/grass

31. Field Study Regression model for field study 4 PLS Components (97% Y variance); R 2 = 0.97

32. Field Study Validation results for field study

33. Conclusions 2 Field Studies 1.Promising but limited study (only 16 spectra) 2.Sampling protocol may be more realistic than laboratory study 3.Bones may not dry out as fast if not sliced into sections at the start of the study 4.Margin of error about 2 days 5.Study needs to be repeated with a larger set of samples under a more diverse set of conditions.

34. Acknowledgments We would like to thank CAMO, Inc. for the donation of the Unscrambler software used in this study.

36. Initial regression model for 90-day study 6 PLS components (77% of Y variance); R 2 = 0.76

37. Validation results for Group 1 with 90 day model

38. Validation results for Groups 1-4 with 90 day model

39. Regression model for Month 1 data 4 PLS components (74% of Y variance); R 2 = 0.74

40. Regression model for Month 2 data 6 PLS components (83% of Y variance); R 2 = 0.83

41. Regression model for Month 3 data 5 PLS components (80% of Y variance); R 2 = 0.81

42. PLS-Discriminant Analysis Model 5 PLS components (99% X variance explained; 52% of Y variance explained)