Geographical classification of population: Analysis of amino acid in fingermark residues using UHPLC-QQQ-MS/MS combined with machine learning.

Objective: To determine the living regions of individuals based on amino acids in fingermark residues and to establish a rapid and accurate regional classification method using machine learning.

Methods: A total of 71 fingermark donors from six different provinces in various regions of China were selected. The content of 18 amino acids in their fingermarks was detected using UHPLC-QQQ-MS/MS.

Toward multidimensional information: A derivatization-free UHPLC-QqQ MS/MS method for amino acid components of fingerprint.

The analysis of fingerprint chemical composition is a meaningful way to excavate the multidimensional information of fingerprint, including the donor profiling information and the age of a fingerprint, which broadens the evidential values of fingerprint, especially for the partial and distorted fingerprint. But the research remains still in the pilot phases or is ongoing. Amino acids are the dominant organic substances in latent sweat fingerprint and influenced by many donor factors.

Visualization of latent fingermarks on fabric using multi-metal deposition (MMD)-A preliminary study.

Multi-metal deposition (MMD) is a versatile fingermarks detection technique adapted from the colloidal gold biolabeling. However, the tedious procedures of MMD makes it receive little attention compared with other methods. The aim of this study is to evaluate the efficacy of MMD technique on several common fabrics, which is considered notoriously challenging for latent fingermark detection.