Liquid chromatography (LC), often coupled with mass spectrometry (MS), has become a cornerstone of forensic science. Its sensitivity, selectivity, and adaptability make it indispensable for addressing diverse analytical challenges, from estimating time of death to identifying emerging synthetic drugs.

In this article, we highlight how LC is being advanced for forensic applications. Ida Marie Marquart Løber from Aarhus University, Denmark, explains how ultrahigh-pressure liquid chromatography–quadrupole time-of-flight mass spectrometry (UHPLC–QTOF-MS) and machine learning (ML) can assist in postmortem interval (PMI) estimation. J. Tyler Davidson of Sam Houston State University in the United States, contributes his work on structural characterization of nitazene analogs via LC–electrospray ionization (ESI)-MS/MS fragmentation profiling. Finally, Walter B. Wilson from the National Institute of Standards and Technology (NIST) addresses the persistent challenge of distinguishing hemp from marijuana by chromatographic means.

Metabolomics and Machine Learning for PMI Estimation

Løber’s recent research focuses on advancing PMI estimation using a workflow in which biological tissues (blood, brain, muscle, eye fluid) from decomposing rat models were sampled across time, then profiled by UHPLC–QTOF-MS to capture dynamic chemical changes (1). From that data set, she explained, “we narrowed it down to just 15 biomarkers per tissue” using ML to serve as the basis for modeling. Her team applied algorithms such as Lasso regression and Random Forest to relate those features to elapsed decomposition time, typically achieving error margins of 3–6 hours.

She emphasizes that one advantage of this approach is its objectivity. The models cut down on uncertainty and remove the subjectivity inherent in conventional PMI estimation. However, she also noted some limitations: extending this framework to human cadavers will demand more comprehensive data sets. The translation requires careful calibration to determine intersubject variability and rigorous external testing before forensic adoption.

However, by analyzing these samples with UHPLC–QTOF-MS, she was able to capture a comprehensive snapshot of how the chemical landscape and associated biomarkers evolve over time after death.

Fragmentation Profiling of Nitazene Analogs via LC–ESI-MS/MS

J. Tyler Davidson’s research addresses a particularly thorny problem: the forensic identification of nitazene analogs, a growing class of highly potent synthetic opioids (2). He notes that the identification of nitazene analogs is challenging “due to the high degree of structural similarity between analogs.”

Davidson’s group analyzed 38 nitazene analogs using LC–ESI-MS/MS to establish fragmentation routes and diagnostic product ions. In the past, he has used gas chromatography (GC)–EI–MS, which determined the limitations of the technique for this research; LC–ESI-MS/MS is not intended to supplant GC–EI–MS, but to complement it: “LC–ESI-MS/MS is better for determining the molecular weight based on the presence of [M+H]+ … although there are still challenges with relatively fragmentation-poor product ion spectra and similar retention times.” LC–ESI-MS/MS was therefore able to provide extra information about the nitrazene analogs.

Several recurring diagnostic ions emerge across analogs (m/z 100, 72, 44, 107), often linked to substituents on amine or benzyl groups. More unique subjects, such as piperidine or pyrrolidine rings, give ions at m/z 112 or 98. For analogs with a methoxy substitution on the phenyl ring, a distinctive product ion at m/z 121 provides a marker that can guide multiple reaction monitoring (MRM) or precursor ion scan (PIS) methods.

Davidson cautioned, however, that novel analogs often lack library spectra or standards. He emphasizes that forensic labs must “interpret their analytical data … and utilize alerts from organizations … share the latest news about what compounds are being identified.” The forensic community can only benefit from shared spectral data, software tools, and coordinated trend tracking.

Chromatographic Differentiation: Hemp vs. Marijuana

Distinguishing hemp from marijuana is critical for regulatory compliance and forensic casework. Recent NIST studies extended LC–photo diode array (PDA) methods to analyze 16 commercial hemp samples alongside 20 seized cannabis samples, simulating real-world forensic testing conditions. These studies revealed that certain naturally occurring compounds like CBNA, as well as synthetic by-products such as Δ8-THC, can interfere with Δ9-THC measurements, potentially causing misidentification of hemp as marijuana. To address these challenges, researchers applied method optimization techniques, including selective detection, peak deconvolution, and adjusted chromatographic conditions. The result is a more robust and accurate approach for reliably distinguishing hemp from marijuana, supporting compliance with the 0.3% Δ9-THC legal threshold and enhancing the integrity of forensic analyses (3).

Historically, GC–MS has been used in the analysis of cannabinoids. However, LC offers several advantages. It allows direct analysis of acidic cannabinoids such as Δ⁹‑THCA and cannabinolic acid (CBNA) without requiring derivatization, preserving the native chemical forms present in plant material. This reduces sample preparation time, minimizes chemical alteration, and improves quantitation accuracy. LC coupled with MS or MS/MS can achieve the same separation and enhanced sensitivity, but such instrumentation comes with substantially higher costs.

Conclusion

Across all three studies, a common theme emerges: the strategic integration of advanced analytical techniques with data-driven or method-optimized workflows to address persistent forensic challenges. Whether estimating postmortem intervals through metabolomic profiling and ML, differentiating structurally similar nitazene analogs via LC–ESI-MS/MS, or distinguishing hemp from marijuana using refined LC-based methods, each approach demonstrates how combining high-resolution chromatography–mass spectrometry with intelligent data interpretation enhances accuracy, objectivity, and reproducibility. Collectively, these efforts reflect a broader trend in modern forensic science—leveraging precision instrumentation and computational tools to overcome ambiguity and improve reliability.

For an extended interview with Ida Marie Marquart Løber, please click here.

For an extended interview with J. Tyler Davidson, please click here.

For an extended interview with Walter B. Wilson, please click here.

References

(1) Løber, I. M. M.; Hedemann, M. S.; Villesen, P.; Nielsen, K. L. Untangling the Postmortem Metabolome: A Machine Learning Approach for Accurate PMI Estimation. Anal. Chem. 2025, 97, 16123–16132. DOI: 10.1021/acs.analchem.4c05796

(2) Hardwick, E. K.; Davidson, J. T. Structural Characterization of Nitazene Analogs Using Electrospray Ionization-Tandem Mass Spectrometry (ESI-MS/MS). Drug Test Anal. 2025. DOI: 10.1002/dta.3921

(3) Wilson, W. B.; Yarberry, A. J.; Goldman S. Chromatographic Interferences Potentially Inflating the Levels of Δ9-THC in Cannabis Sativa Plant Samples and Possible Solutions. J. Chromatogr. A 2025, 1748, 465871. DOI: 10.1016/j.chroma.2025.465871