Quantitative Evaluation of Latent Fingerprints Developed by Fluorescent Methods Based on Python
10.19756/j.issn.0253-3820.241000
- VernacularTitle:基于Python的手印荧光显现质量的量化评估
- Author:
Zhuo-Hong YU
1
;
Zhi-Ze XU
;
Meng WANG
;
Wen-Zhuo FAN
;
Jie LI
;
Ming LI
;
Chuan-Jun YUAN
Author Information
1. 中国刑事警察学院刑事科学技术学院,沈阳 110035
- Keywords:
Latent fingerprint;
Fingerprint development;
Fluorescence;
Contrast;
Sensitivity;
Selectivity
- From:
Chinese Journal of Analytical Chemistry
2024;52(7):964-974,中插1-中插12
- CountryChina
- Language:Chinese
-
Abstract:
A serious of rare earth luminescent micro/nano-materials with various properties were synthesized via chemical method for fluorescent development of latent fingerprints(LFPs).Three evaluation indexes namely contrast,sensitivity and selectivity were introduced to evaluate the effects of LFP development.Quantitative formulas for calculating the contrast,sensitivity and selectivity were further put forward,and a quality evaluation system based on Python was thus established.In addition,the objective evaluation value was finally confirmed to be consistent with the subjective visual judgment.The reproducibility of this evaluation method was finally confirmed.The effects of luminescence intensity and color of developing materials on the contrast,particle size of developing materials on the sensitivity,and micromorphology and surface property of developing materials on the selectivity were discussed in detail.Five effective ways were also proposed to promote the quality of LFP development,such as increasing the luminescence intensity,tuning the luminescence color,decreasing the particle size,adjusting the micromorphology,and modifying the surface property.This quality evaluation system based on Python could evaluate the effects of LFP development objectively,accurately and comprehensively,exhibiting easy operability,high efficiency,sensitive response,accurate and reliable results,and wide applicability,which would provide beneficial references for the reasonable selection of LFP development methods as well as objective evaluation of evidence value.
