The time since deposition (TSD) of latent fingermarks (LFMs) serves as "witnesses" for crime scene reconstructions. Nevertheless, existing TSD prediction approaches focused on either physical or chemical aging parameters leading… Click to show full abstract
The time since deposition (TSD) of latent fingermarks (LFMs) serves as "witnesses" for crime scene reconstructions. Nevertheless, existing TSD prediction approaches focused on either physical or chemical aging parameters leading to inaccurate estimation. A novel label-free protocol has been developed, where both physical ridge patterns and lipid oxide (LipOx) degradation kinetics are realized using optical microscopy and scanning electrochemical microscopy (SECM) and combined for TSD prediction. Specifically, the surface interrogation (SI)-SECM titration was utilized to monitor the LipOx degradation in LFM arrays aligned by hole array masks, through which we derived the LipOx degradation function. After establishing the relationship between several titration parameters and titrated area by experimental and numerical simulation methods, the titrated area could be reasonably estimated and subsequently used to calculate the surface coverage of LipOx. Results demonstrated that the tip transient revealed the LipOx coverage of deposited LFMs. Notably, LipOx coverage was found to increase during the first day and then decrease over time, whose degradation rate was susceptible to light. Thus, TSD candidates of an LFM could be limited to two values through the established function. Due to the nonmonotonic trend of LipOx aging, a physical parameter "the gray value ratio (GVR) of furrows to ridges" was proposed to exclude irrelevant TSD through support vector machine (SVM) classification. Ultimately, we predicted TSDs of seven LFMs with estimation errors of 2.2-26.8%. Overall, our strategy, with the outperformed capability of gleaning physical and electrochemical information on LFMs, can provide a truly label-free way of studying LFMs and hold great promise for multidimensional fingerprint information analysis.
               
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