In a recent paper titled “Soft and Robust Identification of Body Fluid Using Fourier Transform Infrared Spectroscopy and Chemometric Strategies for Forensic Analysis,” a team of researchers described the use of Fourier transform infrared spectroscopy (FT-IR) for the forensic analysis and identification of body fluids.
In a recent paper titled “Soft and Robust Identification of Body Fluid Using Fourier Transform Infrared Spectroscopy and Chemometric Strategies for Forensic Analysis,” a team of researchers described the use of Fourier transform infrared spectroscopy (FT-IR) for the forensic analysis and identification of body fluids (1). In their paper, the team indicated that contrasting with current methods that use serological and biochemical techniques, vibrational spectroscopic approaches such as FT-IR provide alternative advantages for forensic body fluid identification, such as non-destructivity and versatility for various body fluid types and analytical interests.
The researchers collected attenuated total reflection (ATR) FT-IR spectra of five types of body fluids, peripheral blood, saliva, semen, urine, and sweat from ten to twenty volunteers. Because body fluid body fluid spectra have spatially dependent variations as well as donor-dependent variations, the ATR FT-IR spectra were collected from different areas in the BF samples dried overnight, resulting in a total of 100 spectra for peripheral blood, semen, and urine, 90 spectra for saliva, and 75 spectra for sweat.
The spectra from each body fluid type were shown with characteristic contributions from each principle component and different spreads of the distribution. The researchers found that the results indicated that the spectral characteristics of a body fluid type cannot be represented by a single or average spectrum. They determined that multivariate statistical analysis of the spectral variations is essential to characterize and objectively distinguish the body fluid spectra.
Reference
(1) DOI:10:1038/s41598-26873-9
Artificial Intelligence Accelerates Molecular Vibration Analysis, Study Finds
July 1st 2025A new review led by researchers from MIT and Oak Ridge National Laboratory outlines how artificial intelligence (AI) is transforming the study of molecular vibrations and phonons, making spectroscopic analysis faster, more accurate, and more accessible.
Machine Learning and Optical Spectroscopy Advance CNS Tumor Diagnostics
July 1st 2025A new review article highlights how researchers in Moscow are integrating machine learning with optical spectroscopy techniques to enhance real-time diagnosis and surgical precision in central nervous system tumor treatment.
New Ecofriendly Spectrophotometric Method Boosts Accuracy in Veterinary Drug Analysis
June 30th 2025A recent study showcases a cost-effective, ecofriendly UV spectrophotometric method enhanced with dimension reduction algorithms to accurately quantify veterinary drugs dexamethasone and prednisolone, offering a sustainable alternative to traditional analysis techniques.