Analysis of the ex-vivo transformation of semen, saliva and urine as they dry out using ATR-FTIR spectroscopy and chemometric approach

Abstract

The ex-vivo biochemical changes of different body fluids also referred as aging of fluids are potential marker for the estimation of Time since deposition. Infrared spectroscopy has great potential to reveal the biochemical changes in these fluids as previously reported by several researchers. The present study is focused to analyze the spectral changes in the ATR-FTIR spectra of three body fluids, commonly encountered in violent crimes i.e., semen, saliva, and urine as they dry out. The whole analytical timeline is divided into relatively slow phase I due to the major contribution of water and faster Phase II due to significant evaporation of water. Two spectral regions i.e., 3200–3400 cm−1 and 1600–1000 cm−1 are the major contributors to the spectra of these fluids. Several peaks in the spectral region between 1600 and 1000 cm−1 showed highly significant regression equation with a higher coefficient of determination values in Phase II in contrary to the slow passing Phase I. Principal component and Partial Least Square Regression analysis are the two chemometric tool used to estimate the time since deposition of the aforesaid fluids as they dry out. Additionally, this study potentially estimates the time since deposition of an offense from the aging of the body fluids at the early stages after its occurrence as well as works as the precursor for further studies on an extended timeframe.

Innovative Vibrational Spectroscopy Research for Forensic Application

“…They were able to detect and differentiate trace blood, urine, and semen that had been deposited on different substrates, including glass, paper, cotton, denim, and poly-blend fabric. Further, using ATR-FTIR spectroscopy and chemometrics to monitor the biochemical changes of stains over time, Das et al investigated the aging mechanisms of multiple body fluids, including semen, saliva, and urine . Their goal was to measure aging in the short period after deposition as the sample dried.…”

“…Further, using ATR-FTIR spectroscopy and chemometrics to monitor the biochemical changes of stains over time, Das et al investigated the aging mechanisms of multiple body fluids, including semen, saliva, and urine. 47 Their goal was to measure aging in the short period after deposition as the sample dried. Their work revealed that, as they dry out, water loss is a major contributor to the spectral changes in semen, saliva, and urine stains.…”

Attenuated total reflection-Fourier transform infrared spectroscopy: a universal analytical technique with promising applications in forensic analyses

“…Defining the age of seminal stains can be very informative in criminal investigations of rape and sexual assault. Therefore, the time-dependent changes in the ATR-FTIR spectra of seminal stains have been investigated to explore the potential of this technique in estimating the age of semen stains [84,85]. These changes have been divided in two phases basing on the speed of water evaporation.…”

Identification of hepatocellular carcinoma and paracancerous tissue based on the peak area in FTIR microspectroscopy

“…[10][11][12] It depends on the quantum energy of the infrared radiation, the absorption of which induces a transition between the vibrational states of the molecule. 13 FTIR spectroscopy has been widely applied in various research studies including plants, 14,15 animals, [16][17][18] microorganisms, 19,20 food [21][22][23] and human diseases, [24][25][26] for various types of samples including serum, 27,28 urine, 29,30 cell lines, 31 and tissue sections. 32,33 Although a wide range of biological studies have been covered by FTIR spectroscopy, most of the analyses are based on the full spectrum, peak frequency shi 34 and intensity of the bands, 35,36 the relative ratios [37][38][39] of the vibrating groups and so on, 40 while in this study, we found that the similarity of the full spectrum between the HCC foci and the paracancerous tissue makes it difficult to identify the groups by some machine learning techniques, and hence, the peak areas of the spectra were calculated and selected as the features to perform the classication models of k-nearest neighbor (KNN), random forest (RF) and support vector machine (SVM).…”