Identification of Body Fluid Traces for Forensic Purposes: An Interview with Igor Lednev

As part of "The Future of Forensic Analysis," executive editor Jerome Workman, Jr. sat down with Igor Lednev to discuss several of his recent papers related to his spectroscopic research in forensic analysis.

Can you explain the advantages of using ATR FT-IR spectroscopy for detecting oral fluid (OF) stains compared to other forensic techniques, particularly in terms of specificity and spectral patterns (1)?

ATR FT-IR spectroscopy provides several advantages in detecting oral fluid (OF) stains due to its ability to capture unique spectral patterns specific to the biochemical composition of a sample. This specificity allows for high accuracy in differentiating OF from other biological materials. Moreover, ATR FT-IR is a rapid and nondestructive method, making it well-suited for forensic applications. It can reliably detect the presence of major OF bands, such as the thiocyanate ion, even after six months of deposition, without significant interference from nonporous substrates. This makes it a more effective and specific technique compared to traditional forensic methods.

Your study examined the detection of OF stains on various substrates, both porous and nonporous (1). What challenges did you encounter with porous substrates, and how did you address these challenges to ensure accurate detection?

Porous substrates posed a challenge due to the pronounced spectral contribution from the surface, which could interfere with the detection of OF stains. This issue was addressed by using background subtraction techniques to minimize substrate interference. By applying substrate-specific spectral corrections, the study successfully detected the key OF bands, except for the thiocyanate ion, which was diluted in some cases. This approach enabled reliable identification of OF stains even on highly porous and complex surfaces.

The use of scanning electron microscopy (SEM) provided insights into the morphology of OF stains (1). Can you describe the significance of the characteristic salt crystals and protein aggregates observed in both fresh and aged samples, and how these findings support the forensic application of ATR–FTIR and SEM?

Scanning electron microscopy (SEM) revealed characteristic salt crystals and protein aggregates in both fresh and aged OF stains. These structures are significant as they provide morphological evidence supporting the presence and the distribution of OF stains on various substrates. The persistence of these features after six months indicates the durability of OF deposits, enhancing the forensic value of ATR–FTIR and SEM for detecting and analyzing OF stains over extended periods. These findings bolster the reliability of the combined use of these techniques for forensic investigations. This novel approach combining SEM and ATR-FTIR was possible thanks to our long-term collaboration with Professor Al-Hetlani from Kuwait University.

What inspired the development of a self-referencing algorithm for distinguishing between human and non-human bloodstains using Raman spectroscopy, and what are its key advantages over previous methods (2)?

The development of a self-referencing algorithm for distinguishing between human and non-human bloodstains using Raman spectroscopy was inspired by the need for a more accessible and rapid method to differentiate blood origins in forensic investigations. Traditional methods often required complex computational analysis or destructive chemical tests. This algorithm simplifies the process by comparing specific Raman spectral band intensities, offering a rapid, nondestructive, and highly accurate approach. The key advantage over previous methods is its simplicity—it requires minimal training, making it easily applicable in forensic settings without advanced statistical knowledge.

In your study (2), you expanded the model to include 18 non-human species. Can you discuss the significance of this expansion and the challenges you faced in validating the self-referencing algorithm across a broader range of species?

The expansion of the model to include 18 non-human species significantly enhances its forensic utility, as it allows forensic analysts to apply the method across a broader range of species, increasing its versatility in investigations. However, this expansion posed validation challenges, particularly due to the differences in blood composition across species. Misclassifications, such as those observed with some chicken, cow, frog, and horse samples, highlighted the need to refine the algorithm to account for species-specific variations. Despite these challenges, the algorithm achieved a 96.2% true positive classification rate, demonstrating its reliability across species.

The self-referencing algorithm relies on the intensity ratios between specific Raman bands (2). Could you explain how these ratios are used to differentiate between human and non-human blood, and why this approach is particularly effective for forensic applications?

The self-referencing algorithm relies on comparing the intensity ratios between two specific Raman bands at 1003 and 1341 cm⁻¹, which represent biomarkers in hemoglobin that vary between humans and non-humans. These differences in peak intensities are tentatively linked to structural variations in hemoglobin. The effectiveness of this approach lies in its ability to establish a clear threshold for human versus non-human classification based on these ratios. This method is particularly effective for forensic applications as it is nondestructive, rapid, and requires minimal expertise, allowing for reliable and accessible bloodstain analysis in crime scene investigations.

What motivated your research on using Raman spectroscopy paired with chemometrics for predicting the time since deposition (TSD) of bloodstains in extreme thermal environments (3)?

The motivation behind this research was the forensic need to accurately determine the time since deposition (TSD) of bloodstains in extreme thermal conditions, which are often encountered in real crime scenes. In particular, the study focused on predicting TSD in environments like a vehicle left in direct sunlight (55 °C), where bloodstains degrade faster than under ambient conditions. Traditional methods of TSD estimation often fail under such circumstances, and using Raman spectroscopy paired with chemometrics offers a rapid, nondestructive, and reliable approach to address this challenge. In addition, determining the TSD could be valuable to select biological stains relevant to the crime.

Several years ago, we were asked by a law enforcement agency to use our test on a sample from such car because they were not sure that the conventional biochemical tests were reliable under these extreme conditions. Unfortunately, I am not at liberty to provide any additional information about that case.

Can you describe the methodology you used to create the classification models for TSD predictions and how these models perform under the specified extreme thermal conditions (3)?

The study used Raman spectroscopy paired with chemometrics to create classification models that predict TSD of bloodstains aged in extreme heat (55 °C) up to 48 hours post-deposition. An SVMDA (support vector machine discriminant analysis) model was developed using blood spectra collected from five trials across three donors. The model was cross-validated, achieving an 81% correct classification of individual spectra, with external validation resulting in classification accuracy of 73%. A manual threshold of 40% correct spectral classification was applied to account for natural variations in blood stains due to accelerated degradation in extreme heat. This approach improved the model's performance, achieving 100% correct classification for individual samples after the threshold adjustment.

What were the key findings regarding the degradation rate of peripheral bloodstains in a high-temperature environment, and how do these findings impact forensic investigations conducted in such conditions (3)?

The study found that peripheral bloodstains degraded at a significantly accelerated rate when exposed to high temperatures, with degradation behaviors typically seen in month-old ambient samples appearing after just one hour at 55 °C. The accelerated degradation led to greater spectral variation between heated samples compared to ambient ones. These findings are crucial for forensic investigations, as they demonstrate that bloodstains exposed to extreme heat can still provide reliable TSD estimates, allowing investigators to place a person at a crime scene within a specific time frame even under challenging environmental conditions.

What were the key challenges you faced in adapting Raman spectroscopy for stand-off detection of biological stains, and how did you address these challenges (4)?

One of the key challenges in adapting Raman spectroscopy for stand-off detection was ensuring that the technique maintained the sensitivity and accuracy necessary for detecting biological stains from a distance. External factors such as ambient light, distance from the target, and potential surface interference could affect the quality of the spectra collected. To address these challenges, Dr. Lamyaa Almehmadi, a PhD student at that time and a postdoctoral fellow at MIT now, used a handheld Raman spectrometer with a stand-off attachment, which allowed for the collection of spectra from a distance while maintaining comparable quality to the spectra obtained from traditional benchtop Raman microscopes.

How did the performance of the handheld Raman spectrometer with a stand-off attachment compare to that of the benchtop Raman microscope in terms of accuracy and reliability for detecting bloodstains (4)?

The performance of the handheld Raman spectrometer with a stand-off attachment was found to be comparable to that of the benchtop Raman microscope in terms of accuracy and reliability for detecting bloodstains. The spectra obtained from both systems were visually similar, which demonstrated that the handheld device could be effectively used in field conditions without compromising the integrity or accuracy of the results. This finding suggests that the handheld Raman spectrometer with stand-off capabilities is a viable alternative to laboratory-based instruments for forensic applications.

Can you elaborate on the potential forensic applications of stand-off Raman spectroscopy and how it might transform the process of detecting and identifying body fluid traces at crime scenes (4)?

Stand-off Raman spectroscopy has significant potential for transforming forensic investigations by enabling the detection and identification of body fluid traces, such as bloodstains, from a distance. This capability would allow forensic investigators to survey crime scenes more efficiently without the need to physically disturb or contaminate evidence. Additionally, the use of handheld devices with stand-off attachments could streamline the process of identifying biological stains in situ, providing real-time information and potentially guiding evidence collection. This approach could also reduce the time required for forensic analyses and expand the use of Raman spectroscopy in the field, ultimately improving crime scene processing and evidence integrity.

What motivated your research team to explore the chemical profiling of fingermark (FM) residues for determining smoker status, and what potential impact could this have on forensic investigations (5)?

The research team was motivated by the growing interest in using chemical profiling of fingermark (FM) residues to extract personal traits, such as smoker status, which can help narrow down the suspect pool in criminal investigations. While fingerprint ridge patterns have long been used for human identification, vibrational spectroscopy of FM residues offered a new avenue to provide additional intelligence, including lifestyle habits like smoking. This research has the potential to significantly impact forensic investigations by adding a layer of phenotypical information that could assist in suspect identification or exclusion.

How did the use of attenuated total reflection Fourier transform-infrared (ATR FT-IR) spectroscopy combined with partial least squares-discriminant analysis (PLS-DA) enhance the ability to differentiate between smoker and non-smoker donors from their FM residues (5)?

The use of attenuated total reflection Fourier transform-infrared (ATR FT-IR) spectroscopy combined with partial least squares-discriminant analysis (PLS-DA) enhanced the ability to differentiate between smoker and non-smoker donors from their FM residues by analyzing the chemical composition of the residues. ATR FT-IR captured the vibrational modes of key components like proteins, lipids, and esters. By applying PLS-DA, the researchers were able to build a binary classification model that could distinguish between the two groups based on subtle spectral differences. This approach resulted in 84% correct classification at the spectral level and 92% correct classification at the donor level, demonstrating its effectiveness.

Can you discuss the role of the genetic algorithm (GA) in improving the discrimination rate of your model and how it contributed to achieving high classification accuracy for both spectral and donor levels (5)?

The genetic algorithm (GA) played a crucial role in improving the discrimination rate of the model by selecting specific regions of the ATR FT-IR spectra that were most significant for distinguishing between smoker and non-smoker FM residues. By focusing on these relevant spectral regions, the GA enhanced the overall classification accuracy. This contributed to achieving high classification accuracy, with the model reaching 100% accuracy at the donor level during external validation testing. The GA was essential in refining the model, ensuring that the analysis was more precise and reliable for forensic applications.

References

(1) Dalal Al-Sharji, M. O.; Amin, M. O.; Lednev, I. K.; Al-Hetlani, E. Detection of Oral Fluid Stains on Common Substrates Using SEM and ATR–FTIR Spectroscopy for Forensic Purposes. ACS Omega 2024, Publication Date: June 18, 2024. https://doi.org/10.1021/acsomega.3c09358.

(2) Dickler, H. M.; Weber, A. R.; Lednev, I. K. Discrimination Between Human and Animal Blood Using Raman Spectroscopy and a Self-Reference Algorithm for Forensic Purposes: Method Expansion and Validation. Appl. Spectrosc. Pract. 2024, 2 (2). https://doi.org/10.1177/27551857241252175.

(3) Barber, A. P.; Weber, A. R.; Lednev, I. K. Raman Spectroscopy To Determine the Time Since Deposition of Heated Bloodstains. Forensic Chem. 2024, 37, 100549. https://doi.org/10.1016/j.forc.2024.100549.

(4) Almehmadi, L. M.; Lednev, I. K. Stand-off Raman Spectroscopy Is a Promising Approach for the Detection and Identification of Bloodstains for Forensic Purposes. J. Raman Spectrosc. 2024, 55 (2), 227–231. https://doi.org/10.1002/jrs.6609.

(5) Amin, M. O.; Al-Hetlani, E.; Lednev, I. K. Discrimination of Smokers and Nonsmokers Based on the Analysis of Fingermarks for Forensic Purposes. Microchem. J. 2023, 188, 108466. https://doi.org/10.1016/j.microc.2023.108466.

About the Interviewee

Igor K. Lednev is Williams-Raycheff Professor in Chemistry and SUNY Distinguished Professor at the University at Albany, State University of New York. He is a cofounder of two startup companies commercializing a universal method for identification of body fluid traces for forensic purposes, and screening tests for the early diagnostics of Alzheimer’s disease. Lednev is a Fellow of the Royal Society of Chemistry, U.K and the Society for Applied Spectroscopy. He received several prestigious awards including the 2022 Charles Mann Award for Applied Raman Spectroscopy and Gold Medal Award from the Society for Applied Spectroscopy. Lednev has served as an advisory member on the White House Subcommittee for Forensic Science. U.S. Dr. Lednev has co-authored over 270 publications in peer-reviewed journals and 10 patents reaching h-index of 77. His work has been covered by media more than 90 times including 20 TV interviews.