主管：中华人民共和国司法部
主办：司法鉴定科学研究院
ISSN 1671-2072  CN 31-1863/N
中国司法鉴定

中国司法鉴定 ›› 2025 ›› Issue (1): 37-44.DOI: 10.3969/j.issn.1671-2072.2025.01.005

• 专题研究：基于理化分析的人员特征刻画技术 • 上一篇    下一篇

基于GA-RF的不同人群手足气味特征组分识别刻画研究

张    宇1，2，胡晓光1，宋    歌2，董林沛2，赵    鹏2，张云峰2，任昕昕2   

  1. 1. 中国人民公安大学 侦查学院，北京 100038； 2. 公安部鉴定中心，北京 100038
  • 收稿日期:2024-09-10 出版日期:2025-01-15 发布日期:2025-01-15
  • 通讯作者: 任昕昕（1986—），女，警务技术正高级任职资格，博士，主要从事毒物分析与特征刻画研究。E-mail：renxinxin2008@126.com
  • 作者简介:张宇（2000—），男，硕士研究生，主要从事人体气味分析、特征刻画研究。E-mail：550289488@qq.com
  • 基金资助:
    国家重点研发计划项目（2022YFC3320700）；北京市科技新星计划（20230484316）

Study on Characterization of Hand and Foot Odor in Different Populations Based on Genetic Algorithm-Random Forest

 ZHANG Yu1, 2, HU Xiaoguang1, SONG Ge2, DONG Linpei2, ZHAO Peng2, ZHANG Yunfeng2, REN Xinxin2   

  1. 1. School of Investigation, People’s Public Security University of China, Beijing 100038, China; 2. Institute of Forensic Science, Ministry of Public Security, Beijing 100038, China
  • Received:2024-09-10 Published:2025-01-15 Online:2025-01-15

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摘要： 目的 筛选人体手足气味中与性别、年龄相关的特征组分，进行不同人群性别与年龄特征刻画。方法 采用热解吸-气相色谱-质谱法（thermal desorption-gas chromatography-mass spectrometry，TD-GC-MS）检测人体手足中的挥发性气味信息，利用单因素分析与多因素分析筛选出不同性别、年龄人群手足气味中的差异组分，并通过遗传算法-随机森林 （genetic algorithm-random forest，GA-RF）机器学习方法预测不同性别、年龄的特征组分，并构建判别模型。结果 从人体手足部位中共检测出 304 种挥发性物质，通过t检验和正交偏最小二乘判别分析（orthogonal partial least squares-discriminant analysis, OPLS-DA）筛选P<0.05且变量投影重要性（variable importance in projection，VIP）>1的差异组分，使用遗传算法（genetic algorithm，GA）优化随机森林（random forest，RF）算法构建判别模型，利用手足特征进行性别识别的准确率分别为92.02%和81.46%，年龄识别的准确率分别为76.13%和73.49%。结论 基于统计学和GA-RF机器学习方法，筛选出人体手足气味中不同性别、年龄的差异标志物，构建判别模型，为人体气味在法庭科学领域中的应用提供新思路。

关键词: 热解吸, 气相色谱-质谱法, 手足气味特征, 遗传算法, 随机森林, 识别

Abstract: Objective To identify and screen characteristic volatile components in human hand and foot odors associated with gender and age, thereby characterizing the attributes of different population groups. Methods Thermal desorption-gas chromatography-mass spectrometry (TD-GC-MS) was used to analyze the volatile compounds from human hands and feet. Single-factor and multi-factor analyses were used to identify different components related to gender and age across various populations. Genetic algorithm-random forest (GA-RF) machine learning techniques were used to predict the characteristic components of different genders and ages, and to construct a classification model. Results A total of 304 volatile components were identified from human hand and foot odors. They were discerned through t-tests and orthogonal partial least squares-discriminant analysis (OPLS-DA). Components with P<0.05 and VIP>1 were selected. Genetic algorithm (GA) was used to optimize random forest (RF) to construct a judgment model. The accuracies of gender identification using hand and foot features were 92.02% and 81.46%, respectively, and those for age identification were 76.13% and 73.49%, respectively. Conclusion Based on statistics and GA-RF machine learning methods, gender and age difference markers in human hand and foot odor were screened, and a prediction model was constructed, providing a novel approach for the biometric characterization of human odor in forensic science.

Key words: thermal desorption, gas chromatography-mass spectrometry (GC-MS), hand and foot odor, genetic algorithm (GA), random forest (RF), identification

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