Determination of Lipid Components in Fingerprints by Gas Chromatography-Mass Spectrometry and Gender Recognition of Fingerprint Donors by Machine Learning
10.19756/j.issn.0253-3820.251023
- VernacularTitle:基于气相色谱-质谱法测定指纹中的脂质类成分并结合机器学习判定指纹遗留人性别的研究
- Author:
Zi-Chen YI
1
;
Wen-Ji ZHANG
;
Zi-Yong ZHU
;
Wei YI
;
Jia-Si JIANG
;
Zi-Hua LI
Author Information
1. 中国刑事警察学院刑事科学技术学院,沈阳 110854
- Keywords:
Fingerprint;
Fingerprint gender;
Fatty acids;
Support vector machine;
Gas chromatography-mass spectrometry
- From:
Chinese Journal of Analytical Chemistry
2025;53(8):1290-1299,中插19-中插22
- CountryChina
- Language:Chinese
-
Abstract:
Gender recognition based on the analysis of fingerprint residue can assist investigators in narrowing down the scope of investigation and play an important role in the field of criminal investigation.This study established a quantitative analysis method for lipid substances in fingerprints based on gas chromatography-mass spectrometry(GC-MS).Fatty acids in fingerprints were methylated using sulfuric acid methanol derivatization reagent(7%,V/V),the extraction reagent was dichloromethane-methanol(1∶1,V/V)solution,the reaction temperature was 70℃and the heating time was 45 min.Quantitative analysis of the relative content of 23 kinds of fatty acids and squalene in fingerprints residue by different genders was conducted,and orthogonal partial least squares-discriminant analysis(OPLS-DA)was used to reduce the dimensionality of the quantitative results.A total of 13 kinds of components in the fingerprints were selected to maximize the difference in relative content between male and female fingerprints.Three machine learning models,including binary logistic regression(BLR),support vector machine(SVM)and random forest(RF),were further used as feature variables to classify the gender of fingerprints.The classification performance of each model was compared through five indicators,and it was found that the most suitable model for binary classification of fingerprint gender was SVM model.The results showed that the SVM fingerprint residual gender binary classification model established based on the relative content data of 13 kinds of lipid substances in fingerprints achieved a classification accuracy of 90%and an area under the receiver operating characteristic curve(AUC)value of 0.98.This study provided a new research method for detecting lipid components in fingerprints and a methodological basis for gender recognition of fingerprints.
