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Vol. 38 No. 4 (2026): Vol 38 Issue 4, 2026

Copyright (c) 2026 SOONMIN HO, sari, F. KABEAKAN, H. HASIBUAN

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This work is licensed under a Creative Commons Attribution 4.0 International License.

Technological Evolution in Latent Fingerprint Detection: From Nanomaterials to Artificial Intelligence

https://doi.org/10.14233/ajchem.2026.35435
Sri Adelila Sari
Department of Chemistry, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, North Sumatera 20221 Medan, Indonesia
https://orcid.org/0000-0001-5911-6575
F.Y.A. Kabeakan
Department of Chemistry, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, North Sumatera 20221 Medan, Indonesia
https://orcid.org/0009-0002-5010-3211
H. Hasibuan
Department of Chemistry, Universitas Negeri Medan, Jl. William Iskandar Ps. V, Kenangan Baru, Kec. Percut Sei Tuan, Kabupaten Deli Serdang, North Sumatera 20221 Medan, Indonesia
https://orcid.org/0009-0005-2389-5032
S.M. Ho
Faculty of Health and Life Sciences, INTI International University, Persiaran Perdana BBN Putra Nilai, 71800 Nilai, Negeri Sembilan, Malaysia
https://orcid.org/0000-0002-3697-5091

Corresponding Author(s) : S.M. Ho

soonmin.ho@newinti.edu.my

Asian Journal of Chemistry, Vol. 38 No. 4 (2026): Vol 38 Issue 4, 2026

Abstract

Latent fingerprints are one of the most important forensic pieces of evidence in criminal investigations due to their uniqueness and persistence throughout an individual’s life. Technological developments in latent fingerprint detection and analysis methods have advanced significantly, especially through the application of nanotechnology, artificial intelligence and spectroscopic techniques. This study aims to systematically evaluate innovations in latent fingerprint detection methods that have been developed between 2015 and 2025. The literature review shows that the integration of nanoparticle-based technologies, optical sensors and digital image processing has improved the sensitivity and effectiveness in latent fingerprint visualisation. In addition, Artificial Intelligence (AI), especially deep learning and machine learning, has accelerated and improved the accuracy of matching fingerprints with forensic databases. However, while these innovations offer more adaptive and efficient solutions, challenges remain, especially in the aspects of method standardisation, technology scalability as well as cost-effectiveness. With a multidisciplinary approach combining chemistry, physics and artificial intelligence, this research highlights the potential for developing more advanced, efficient and environmentally friendly latent fingerprint identification methods.

Keywords

Latent fingerprints Forensic science Nanotechnology Artificial intelligence Spectroscopy
(1)
Sari, S. A.; Kabeakan, F.; Hasibuan, H.; Ho, S. Technological Evolution in Latent Fingerprint Detection: From Nanomaterials to Artificial Intelligence. ajc 2026, 38, 843-851.
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