Beyond identification: inferring physical activity from fingerprint lipids using machine learning.

Clinical Snapshot

Key Findings

• P Value: Not reported

• Effect Size: Ensemble algorithm: 75±8% accuracy; Neural network: 73±7% accuracy

• Primary Outcome: Classification accuracy for physical activity inference

• Nnt Or Sensitivity: Sensitivity and specificity not reported; only overall accuracy provided

• Confidence Interval: Not reported

Clinical Application

Low clinical feasibility due to specialized MALDI-MS equipment requirements and lack of standardized protocols Limited relevance to Australian clinical practice; potential forensic applications but no established regulatory framework through TGA for diagnostic use Limited applicability - primarily forensic rather than clinical populations

Abstract

Fingerprints are a widely recognized form of forensic evidence, valued for their ability to link individuals with specific locations. Traditional fingerprint analysis relies on optical imaging to identify a match in a fingerprint database; however, where no match is found, the evidential value of a latent print is limited. Here, we present the first study to integrate matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) with supervised machine learning to infer physical activity from fingerprint chemistry, expanding the utility of fingerprints beyond identification alone. Physical activity labels were derived from a validated questionnaire and converted into binary classes. Supervised machine learning algorithms were trained on the lipid features and evaluated against the survey-derived labels. The top-performing models were an ensemble algorithm based on multiple decision trees and a neural network, which classified physical activity with accuracies of 75 ± 8% and 73 ± 7%, respectively. These results demonstrate that fingerprint lipid chemistry encodes biologically meaningful information related to physical activity and establish a new approach for extracting lifestyle and behavioral indicators from trace evidence, with potential applications in forensic investigations and noninvasive fingerprint-based assessments in medicine.

References

1. 1.Patten, D. R., Buckman Johnson, R. L., Forsman, T. T., & Lee, Y. J. (2026). Beyond identification: inferring physical activity from fingerprint lipids using machine learning. Analytical Methods. https://doi.org/10.1039/d5ay02066b