分析化学2024，Vol.52Issue(7)：964-974,中插1-中插12,23.DOI:10.19756/j.issn.0253-3820.241000
基于Python的手印荧光显现质量的量化评估
Quantitative Evaluation of Latent Fingerprints Developed by Fluorescent Methods Based on Python
摘要
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.关键词
潜在手印/手印显现/荧光/对比度/灵敏度/选择性Key words
Latent fingerprint/Fingerprint development/Fluorescence/Contrast/Sensitivity/Selectivity引用本文复制引用
于卓弘,徐致泽,王猛,范文卓,李杰,李明,袁传军..基于Python的手印荧光显现质量的量化评估[J].分析化学,2024,52(7):964-974,中插1-中插12,23.基金项目
国家自然科学基金项目(Nos.21205139,21802169)、辽宁省应用基础研究计划项目(No.2023JH2/101300097)、辽宁省2020年百千万人才工程项目、辽宁省教育厅科学研究经费项目(Nos.LJKZ0068,LJKZ0076,LJKMZ20220384)和中国刑事警察学院2023年研究生创新能力提升项目(Nos.2023YCYB45,2023YCYB46)资助. Supported by the National Natural Science Foundation of China(Nos.21205139,21802169),the Project of Applied Basic Research Program from Liaoning Province,China(No.2023JH2/101300097),the Liaoning BaiQianWan Talents Program in 2020,the Project from Educational Department of Liaoning Province,China(Nos.LJKZ0068,LJKZ0076,LJKMZ20220384),and the Innovation Ability Enhancement Project of Postgraduates from Criminal Investigation Police University of China(Nos.2023YCYB45,2023YCYB46). （Nos.21205139,21802169）
