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IR Frequency Region: Fingerprint Region

IR Frequency Region: Fingerprint Region

IR spectra are divided into two main regions: the diagnostic region and the fingerprint region. The diagnostic region of the spectrum lies above 1500 cm−1. The absorptions resulting from single-bond vibrations of the N–H, C–H, and O–H stretch at higher wavenumbers and appear on the left side of the spectrum. The stretching absorptions of the C≡C and C≡N occur between 2100–2300 cm−1. In contrast, those arising from stretching absorptions of the C=O, C=N, and C=C occur between 1600–1850 cm−1.
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Beyond identification: inferring physical activity from fingerprint lipids using machine learning.

Daphne R Patten¹, Raven L Buckman Johnson¹, Trevor T Forsman¹

¹Department of Chemistry, Iowa State University, Ames, Iowa 50011, USA. yjlee@iastate.edu.

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|April 7, 2026

Summary

This summary is machine-generated.

Researchers used mass spectrometry and machine learning to analyze fingerprint chemistry. This novel approach infers physical activity from latent prints, expanding forensic science beyond identification.

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Area of Science:

Forensic Science
Analytical Chemistry
Biochemistry

Background:

Fingerprints are crucial forensic evidence for linking individuals to locations.
Traditional analysis is limited when no database match is found for latent prints.
Expanding fingerprint utility beyond identification is a key research area.

Purpose of the Study:

To integrate matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) with machine learning.
To infer physical activity from fingerprint lipid chemistry.
To explore novel applications of trace evidence analysis.

Main Methods:

Lipid profiles from fingerprints were analyzed using MALDI-MS.
Supervised machine learning algorithms were trained on lipid features.
Physical activity data was obtained from validated questionnaires and converted to binary classes.

Main Results:

Machine learning models accurately classified physical activity from fingerprint chemistry.
An ensemble algorithm achieved 75 ± 8% accuracy; a neural network achieved 73 ± 7% accuracy.
Fingerprint lipid profiles contain biologically relevant information about physical activity.

Conclusions:

Fingerprint chemistry analysis can reveal lifestyle and behavioral indicators.
This method expands the evidential value of latent prints.
Potential applications include forensic investigations and noninvasive medical assessments.