What Factors Impact Forensic Applications and Investigations?

A recent presentation at Pittcon 2026, which was titled, ““Multi-Modal Spectroscopic and Biochemical Approaches for Fentanyl Detection: Integrating Raman, ATR-FTIR, and Enzyme Kinetics Analysis,” addressed how spectroscopic techniques such as Raman spectroscopy and attenuated total reflectance Fourier transform infrared (ATR-FTIR) were used to detect fentanyl.¹

This presentation was given by Dr. Lenka Halámková, who is an assistant professor of high-dimensional data analysis at Texas Tech University. Pittcon 2026 represented a good forum for Halámková to present her team’s most recent research because the conference brought together laboratory professionals, researchers, and instrument suppliers under one roof (in this case, it was the Henry B. Gonzalez Convention Center in San Antonio, Texas).

The Halámková Group at Texas Tech University mostly concentrates on using machine learning (ML) and multivariate statistical methods to interpret vibrational spectroscopic data for forensic and biomedical applications.² By focusing on new strategies to interpret vibrational spectral data, Halámková and her group want to develop new analytical methods that can classify and detect forensic trace evidence.²

Our conversation with Halámková delved into key aspects of her group’s research. We started the conversation by asking her to provide a brief summary of her talk. Then, we asked her about how ATR-FTIR spectroscopy was effective at capturing the chemical signatures of fentanyl within the nail’s keratin matrix. The third part of the conversation dove into the multivariate methods and machine learning algorithms. It was here that she discussed how the partial least squares discriminant analysis (PLS-DA) and support vector machine discriminant analysis (SVM-DA) models performed.

In the fourth part of our conversation with Halámková, she addresses the main limitations of her team’s proof-of-concept study. She discusses that variables, such as sample size and sample type, are areas that future studies could improve on by increasing the sample size and increasing the sample variety. For example, in her team’s study, all donors were using the same type of fentanyl for medical purposes. As a result, this limits the study's applicability to real-world scenarios involving fentanyl used as a drug. Halámková also stresses the need for validation on diverse fentanyl analogs and in the presence of other medications. And finally, Halámková discusses that future work could increase the sample size and ensuring the model's accuracy with more varied and real-world samples.

Spectroscopy will be continuing to provide coverage of the Pittcon 2026 conference on an ongoing basis as we report back from San Antonio. You can stay up to date with our coverage of the Pittcon 2026 conference here.

References

1. Halámková, L. Multi-Modal Spectroscopic and Biochemical Approaches for Fentanyl Detection: Integrating Raman, ATR-FTIR, and Enzyme Kinetics Analysis. Presented at Pittcon 2026, in San Antonio, Texas. Available at: https://app.swapcard.com/event/pittcon-2026/planning/UGxhbm5pbmdfNDM0Mjc5MQ==
2. Texas Tech University, Lenka Halamkova, Ph.D. TTU.edu. Available at: https://www.depts.ttu.edu/entx/Department/Personnel/Faculty/Lenka_Halamkova.php (accessed 2026-03-11).