Detection of Oral Fluid Stains on Fabric via Solution Extraction Combined with Deep Ultraviolet Raman Spectroscopy

Area of Science:

• Forensic Science
• Analytical Chemistry
• Spectroscopy

Background:

• DNA phenotyping is crucial in forensics but faces significant backlogs due to overwhelming evidence.
• A rapid, nondestructive method to pre-screen biological stains is needed to improve laboratory efficiency.
• Oral fluid (OF) is a common biological trace evidence found at crime scenes.

Purpose of the Study:

• To demonstrate the effectiveness of Deep-UV Raman spectroscopy for identifying oral fluid stains.
• To compare Deep-UV Raman spectroscopy with Near-Infrared (NIR) Raman spectroscopy for oral fluid identification.
• To evaluate the potential of Deep-UV Raman spectroscopy in reducing forensic analysis backlogs.

Main Methods:

• Collected Raman spectra from oral fluid stains on cotton, polyester, and blue denim substrates.
• Compared spectra of oral fluid extracted from substrates with pure oral fluid spectra.
• Performed comparative analysis between Deep-UV Raman and NIR Raman spectroscopy.
• Utilized statistical methods including Principal Component Analysis (PCA) and Partial Least-Squares Discriminant Analysis (PLS-DA).

Main Results:

• Deep-UV Raman spectroscopy successfully identified oral fluid stains on various forensic substrates.
• Distinct spectral differences were observed between oral fluid and non-oral fluid samples.
• Deep-UV Raman spectroscopy offered advantages over NIR, including reduced sample preparation and minimized fluorescence.
• Statistical analysis enabled clear differentiation between oral fluid and non-oral fluid samples.

Conclusions:

• Deep-UV Raman spectroscopy is a promising, non-destructive tool for identifying oral fluid stains in forensic investigations.
• This technique can significantly improve the efficiency of forensic laboratories by reducing the number of irrelevant samples for DNA analysis.
• The method's advantages, such as simplified data processing and enhanced signal-to-noise ratio, highlight its potential for practical forensic applications.