Citation: LI Wen-Jie, SUN Ling-Hui, YOU Wei, TIAN Shi-Si, WANG Li-Xue, ZHAO Ya-Bin, LI Zhan-Ping. Gender Recognition of Fingerprint Remnant Based on Time-of-Flight Secondary Ion Mass Spectrometry Imaging Signal of Fingerprint Substance[J]. Chinese Journal of Analytical Chemistry, ;2022, 50(1): 112-118. doi: 10.19756/j.issn.0253-3820.210555 shu

Gender Recognition of Fingerprint Remnant Based on Time-of-Flight Secondary Ion Mass Spectrometry Imaging Signal of Fingerprint Substance

LI Wen-Jie ¹ ,
SUN Ling-Hui ¹ ,
YOU Wei ¹ ,
TIAN Shi-Si ¹ ,
WANG Li-Xue ² ,
ZHAO Ya-Bin ^1,, ,
LI Zhan-Ping ^3,4,,

1.
Academy of Forensic Science, People's Public Security University of China, Beijing 100038, China;
2.
Anhui Public Security Education Research Institute, Hefei 230031, China;
3.
Analysis Center, Tsinghua University, Beijing 100084, China;
4.
Key Laboratory of Organic Optoelectronics & Molecular Engineering of Ministry of Education, Department of Chemistry, Tsinghua University, Beijing 100084, China

Corresponding author: ZHAO Ya-Bin, LI Zhan-Ping,
Received Date: 8 June 2021
Revised Date: 21 September 2021

Fund Project: Supported by the National Key R&D Program of China (No.2018YFA0702600) and the Basic Research Project of People's Public Security University of China (No.2021JKF216).

Time-of-flight secondary ion mass spectrometry imaging (TOF-SIMSI) can not only obtain the third level characteristics of fingerprints, but also obtain the material information, which provides more possibilities for the evidential value of partial fingerprints lifted from crime scenes, especially for individual gender analysis based on fingerprint materials. In this work, TOF-SIMS technology combined with multi-layer perceptron neural network was used to explore the feasibility of population gender characteristics analysis based on fingerprint material ion signals. Firstly, the ion signals of 107 fingerprint substances shared by all volunteers detected by TOF-SIMS were analyzed by independent sample T test and the ion signals with significant differences were screened out. After dimension reduction by principal component analysis, they were input into the multi-layer perceptron neural network for gender classification and recognition. The results showed that the method correctly identified about 85% of males and 95% of females, and the total sample accuracy was about 90%. This work provided an effective method for further mining multidimensional information of partial fingerprints.
- Time-of-flight secondary ion mass spectrometry,
- Partial fingerprints,
- Gender judgment,
- Multilayer perceptron neural network
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