Analysis of Fingerprints by Desorption Electrospray Ionization Mass Spectrometry Imaging

Importance of the Topic

The ability to extract personal information from latent fingerprints is of growing interest in forensic science. Beyond ridge patterns, the chemical constituents of fingerprint residue reflect glandular secretions, exogenous exposures and lifestyle factors. Developing ambient, high-resolution mass spectrometry imaging methods to characterize these compounds can provide noninvasive, rapid donor profiling, such as gender determination, directly at a crime scene or in the laboratory.

Study Objectives and Overview

This study demonstrates the use of desorption electrospray ionization mass spectrometry imaging (DESI-MSI) for gender prediction from fingerprints. Fingerprints from four donors (two men, two women; six prints each) were analyzed in both profiling and imaging modes. Multivariate analysis was applied to the spectral data to distinguish male and female donors based on their lipidomic profiles.

Methodology

Samples were collected on conductive glass slides and analyzed under ambient conditions without any chemical matrix. Two acquisition modes were employed:

• Profiling mode: a single line scan across each fingerprint ridge.
• Imaging mode: two-dimensional raster at 100 µm pixel size for spatial mapping.

Acquisition parameters included a methanol–water (95:5 v/v) spray at 0.7 µL/min, 4 bar nitrogen nebulization, and raffinose as an internal lockmass. Data processing involved lockmass correction, background subtraction and spectral alignment before statistical modeling.

Instrumentation Used

• Waters Xevo G2-XS QTof Mass Spectrometer
• Prosolia 2D DESI Source
• Prosolia DESI sprayer with methanol–water solvent system
• Polysine® glass slides for sample deposition

Main Results and Discussion

Positive-ion spectra revealed mono-, di-, and triglycerides, as well as phospholipids, while negative-ion mode detected fatty acids and cholesterol sulfate. Principal component analysis combined with linear discriminant analysis (PCA-LDA) achieved 96% gender classification accuracy in negative mode and 100% in positive mode. The imaging experiments also reproduced fingerprint ridge morphology through selective ion maps, confirming both chemical specificity and spatial resolution.

Benefits and Practical Applications of the Method

• Minimal sample preparation and no matrix application compared to traditional MALDI imaging.
• Ambient, non-destructive analysis preserving samples for further tests.
• Rapid profiling and spatial imaging of multiple compound classes.
• High sensitivity and specificity for forensic donor characterization.

Future Trends and Applications

Further developments may include expanding the workflow to age estimation, dietary or drug use history, and disease biomarker detection. Integration with machine learning algorithms could enhance predictive power, while portable DESI platforms may enable on-site forensic assessments. Coupling DESI-MSI with complementary modalities (e.g., Raman imaging) could deepen chemical insight into fingerprint residues.

Conclusion

DESI-MSI on a Xevo G2-XS QTof platform offers a robust, ambient, and minimally invasive approach to fingerprint analysis. The demonstrated lipidomic profiling enables accurate gender determination, highlighting the potential of this method for advanced forensic intelligence.

Reference

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