Determination of gender from blood stains on different fabric types using a combined ATR-FT-IR spectroscopy and chemometric approach

Aim: In forensic science, biological samples collected from crime scenes have a critical role in criminal profiling (such as determining gender, race, and age). Due to the time-consuming sample preparation processes required by traditional methods and their destructive nature, interest in ATR-FT-IR spectroscopy, which is based on molecular vibration and provides more rapid results, has increased in recent years. However, biological fluids at crime scenes are rarely found isolated; they are typically present on various surfaces such as fabric and glass. These surfaces affect the spectra of biological fluids and complicate the analysis. The aim of this study is to determine gender using ATR-FT-IR spectroscopy and chemometric methods from dried blood stains on different textile surfaces (cotton, denim, and polyester).

Materials and Methods: In this study, blood samples were collected from a total of 50 volunteers, including 25 women and 25 men. These samples were absorbed onto cotton, denim, and polyester fabrics and analyzed using ATR-FT-IR spectroscopy after a 24-hour drying period.

Results: Spectral analysis revealed variations in the characteristic band regions of the blood depending on the type of fabric. Principal Component Analysis (PCA) was applied to the obtained high-dimensional data set using the R package to reduce the number of dimensions; then, logistic regression analyses were performed using SPSS. Examination of the PCA loadings revealed that the spectral bands belonging to cholesterol and creatinine molecules have the potential to serve as discriminative biomarkers for gender differentiation.

Conclusions: The study results showed that logistic regression models built using only raw spectral data or simple variables had limited explanatory power for the gender-dependent variable. However, including the principal components obtained from PCA in the model had an effect that increased the model's explanatory power. When the substrate effect was evaluated, it was determined that blood samples on polyester fabric performed better in terms of accurate gender classification rates compared to cotton and denim surfaces. These findings highlight the importance of considering surface interactions when using spectroscopic methods in forensic serology.