@article{ZHANG2025, 
author = {Yu ZHANG and Xinxin REN and Ge SONG and Linpei DONG and Jiayi LI and Xiaoguang HU},
title = {Machine Learning-based Human Odor Recognition and Application in Forensic Science},
year = {2025},
journal = {Forensic Science and Technology},
volume = {50},
number = {5},
pages = {529-536},
keywords = {forensic science, human odor, gas chromatography-mass spectrometry, machine learning, characterization},
url = {https://www.sciopen.com/article/10.16467/j.1008-3650.2024.0065},
doi = {10.16467/j.1008-3650.2024.0065},
abstract = {Human body odor arises from the secretion of various glands on the skin’s surface, which, when acted upon by microorganisms, evaporate to produce a distinct scent. This odor contains valuable biological information, with certain compounds exhibiting strong stability and individual specificity, serving as “odor fingerprinting” that can distinguish between different populations. Machine learning is an important method for human odor research, which can not only explore the characteristic components of odor in different populations, but also investigate the differences between different individuals. This paper discusses the application of “odor fingerprinting” in individual identification and feature characterization, drawing upon recent literature. It outlines the data processing procedures involved in human odor analysis, highlights the challenges encountered, and explores current research trends. Finally, the application trends of the recognition of human odor are discussed in order to provide reference for odor recognition research.}
}