Deep learning-assisted Fe3O4 NPs for in-situ visual recognition of multi-dimensional information on latent fingerprints

Latent fingerprints (LFPs) are important trace evidences found at crime scenes that assist forensic experts in human identification, and a growing number of studies show that LFPs have some medical value. Herein, as one of the common nanoenzymes, Fe 3 O 4 nanoparticles (Fe 3 O 4 NPs) was used for LFPs imaging due to its excellent superparamagnetic properties and for recognition of glucose/H 2 O 2 on LFPs because of its horseradish peroxidase-like activity, which makes it possible to diagnose diabetes non-invasively through LFPs. Furthermore, the intelligent qualitative and semi-quantitative analysis of glucose/H 2 O 2 on LFPs was achieved by designing the classifier based on the multi-channel convolutional neural network (MC-CNN) technology. The qualitative accuracy rate is over 97% and the semi-quantitative accuracy rate reaches 90%. This work offers the promise to facilitate cross-disciplinary studies between artificial intelligence methods and fingerprint development technique.