分析化学2026，Vol.54Issue(8)：1411-1422,12.DOI:10.19756/j.issn.0253-3820.261097
基于24种脂质标志物降解动力学的指纹遗留时间预测模型研究
Research on Fingerprint Time Since Deposition Prediction Model Based on Degradation Kinetics of 24 Kinds of Lipid Biomarkers
摘要
Abstract
Predicting the age of fingerprint impressions can provide a basis for inferring the time of crime occurrence and evaluating the reliability of testimonies,effectively narrowing down the scope of investigation.In this study,the content ranges of 23 kinds of fatty acids and squalene in fingerprint impressions were quantitatively analyzed using gas chromatography-mass spectrometry(GC-MS).By combining 24 types of lipids in pairs,276 lipid ratio feature sets were constructed.Spearman's univariate analysis was used to screen ratio features significantly correlating with the age of fingerprint impressions.The Gini importance scores of the random forest algorithm were combined to explore the nonlinear contribution of features to the prediction model,ultimately constructing a fingerprint age prediction model based on lipid ratio features.The results showed that the model could effectively distinguish fingerprint impressions left for 0,9,and 13 d,and exhibited good cross-individual robustness.The overall model performance had a coefficient of determination(R2)of 0.785 and a root mean squared error(RMSE)of 2.10 d.This study provided new technical insights and quantitative evidence for accurate prediction of fingerprint age.关键词
指纹/指纹遗留时间/脂质/随机森林/气相色谱-质谱法Key words
Fingerprint/Time since deposition of fingerprint/Lipid/Random forest/Gas chromatography-mass spectrometry引用本文复制引用
张文骥,易梓琛..基于24种脂质标志物降解动力学的指纹遗留时间预测模型研究[J].分析化学,2026,54(8):1411-1422,12.基金项目
沈阳市中青年科技人才培养专项项目(No.RC240610)、辽宁省教育厅基本科研项目(No.JYTMS20261415)和中央高校基本科研业务费项目(No.980237)资助. Supported by the Shenyang Special Project for Cultivating Young and Middle-aged Scientific and Technological Talents(No.RC240610),the Basic Scientific Research Project of Liaoning Provincial Department of Education(No.JYTMS20261415)and the General Project of Basic Scientific Research Funds for Central Universities(No.980237). （No.RC240610）
