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

IF 2.2 3区 医学 Q1 MEDICINE, LEGAL
Lu-Chuan Tian (田陆川), Shi-Si Tian (田师思), Ya-Bin Zhao (赵雅彬)
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引用次数: 0

Abstract

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. Classification models were established using various machine learning algorithms, and the cross-validation accuracy of 72 combinations, including feature engineering, classification algorithms, and optimization algorithms, was compared. Results: UHPLC-QQQ-MS/MS successfully quantified 16 amino acids. Significant differences in the relative content of amino acids were found between the fingermarks from the eastern and western regions of China, as well as among neighboring provinces. The combination of SFS+SVM+BO was identified as the optimal classification model, achieving an accuracy of 90.14 %. Conclusion: The study found regional differences in the relative content of amino acids in fingermarks and established a regional classification method combining UHPLC-QQQ-MS/MS and machine learning. The method developed in this study can be applied to incomplete or distorted fingermarks, and the experimental results can be directly used in police investigations. This research uncovers the multidimensional information carried by fingerprint substances, demonstrating innovation and application value. It not only saves and shortens investigation time and provides investigative leads, but also enables previously unusable physical evidence to play a role again, enhancing the profiling of suspects.
人口地理分类:利用超高效液相色谱-质谱-质谱/质谱结合机器学习分析指印残留物中的氨基酸。
目的根据指痕残基中的氨基酸确定个体的生活区域,并利用机器学习建立快速准确的区域分类方法:方法:选取来自中国不同地区 6 个不同省份的 71 名指痕供体。采用超高效液相色谱-质谱-质谱/质谱联用技术检测了指印中 18 种氨基酸的含量。采用多种机器学习算法建立分类模型,比较了特征工程、分类算法和优化算法等72种组合的交叉验证准确性:结果:UHPLC-QQQ-MS/MS 成功定量了 16 种氨基酸。结果:超高效液相色谱-质谱-质谱/质谱法成功定量了 16 种氨基酸,发现中国东部和西部地区以及相邻省份的指印之间氨基酸相对含量存在显著差异。SFS+SVM+BO组合被确定为最佳分类模型,准确率达到90.14%:该研究发现了指印中氨基酸相对含量的区域差异,并建立了超高效液相色谱-质谱-质谱/质谱联用和机器学习相结合的区域分类方法。本研究开发的方法可用于不完整或扭曲的指痕,实验结果可直接用于警方调查。该研究揭示了指纹物质所携带的多维信息,具有创新性和应用价值。它不仅节约和缩短了侦查时间,提供了侦查线索,还使以前无法使用的物证重新发挥作用,增强了对犯罪嫌疑人的特征分析。
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来源期刊
Forensic science international
Forensic science international 医学-医学:法
CiteScore
5.00
自引率
9.10%
发文量
285
审稿时长
49 days
期刊介绍: Forensic Science International is the flagship journal in the prestigious Forensic Science International family, publishing the most innovative, cutting-edge, and influential contributions across the forensic sciences. Fields include: forensic pathology and histochemistry, chemistry, biochemistry and toxicology, biology, serology, odontology, psychiatry, anthropology, digital forensics, the physical sciences, firearms, and document examination, as well as investigations of value to public health in its broadest sense, and the important marginal area where science and medicine interact with the law. The journal publishes: Case Reports Commentaries Letters to the Editor Original Research Papers (Regular Papers) Rapid Communications Review Articles Technical Notes.
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