Bloodstain age estimation through infrared spectroscopy and Chemometric models

The chemical profiling of bloodstains is essential to link the suspect with the crime. The current study proposed a proof-of-concept methodology for the investigation of bloodstains by utilizing advanced ATR-FTIR spectroscopy coupled with new generation chemometric methods. Current study providesencouraging datato allow discrimination between human and animal blood though with small sample size. In this study, different models for the age estimation of human bloodstains are developed from the trained data sets of 1-175 days old bloodstains. The models such as curve estimation (CE), multiple linear regression (MLR), and partial least squares regressions (PLSR) are developed to determine the best prediction model for aged human bloodstains. The obtained results on the dating of bloodstains are very encouraging and also tested for unknown samples. The maximum dating errors are observed in the curve estimation models whereas, the other models MLR, PLSR show excellent age estimation of unknown bloodstains. These models represent an error of ~3 ± 1 days and ~4 ± 1 days in actual and estimated date, respectively, which is lowest ever reported so far. The present methodology is expected to provide a valuable insight into forensic society and hence, to the law enforcement community. The present methodology can further be explored for an ideal model by including all other external variables/factors and for more longer aging time.