Sex Classification from Human Scent Using Image Interpretation of 2D Gas Chromatography-Mass Spectrometry Data

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F68407700%3A21230%2F25%3A00386496" target="_blank" >RIV/68407700:21230/25:00386496 - isvavai.cz</a>

Výsledek na webu

<a href="https://doi.org/10.1007/978-3-031-95911-0_32" target="_blank" >https://doi.org/10.1007/978-3-031-95911-0_32</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/978-3-031-95911-0_32" target="_blank" >10.1007/978-3-031-95911-0_32</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Sex Classification from Human Scent Using Image Interpretation of 2D Gas Chromatography-Mass Spectrometry Data

Popis výsledku v původním jazyce

Two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC ToF-MS) provides detailed chemical profiles of complex mixtures, making it useful in areas such as environmental monitoring and medical diagnostics. A promising application is sex classification from human scent, where subtle chemical differences indicate biological sex. In this paper, we propose a pattern recognition approach to sex classification that interprets raw GCxGC ToF-MS data as images, moving beyond traditional compound-based analysis. Our approach employs convolutional neural networks (CNNs) to analyze these images, and we compare its performance against established techniques – linear SVM, Ridge regression, and QDA – demonstrating robust and competitive results. Furthermore, we introduce and release a new dataset of GCxGC ToF-MS measurements to support reproducibility in future studies. Using an identity-aware cross-validation strategy, where test subjects are completely unseen during training, our method achieves approximately 88% accuracy on 504 measurements from 40 individuals.

Název v anglickém jazyce

Sex Classification from Human Scent Using Image Interpretation of 2D Gas Chromatography-Mass Spectrometry Data

Popis výsledku anglicky

Two-dimensional gas chromatography coupled with time-of-flight mass spectrometry (GCxGC ToF-MS) provides detailed chemical profiles of complex mixtures, making it useful in areas such as environmental monitoring and medical diagnostics. A promising application is sex classification from human scent, where subtle chemical differences indicate biological sex. In this paper, we propose a pattern recognition approach to sex classification that interprets raw GCxGC ToF-MS data as images, moving beyond traditional compound-based analysis. Our approach employs convolutional neural networks (CNNs) to analyze these images, and we compare its performance against established techniques – linear SVM, Ridge regression, and QDA – demonstrating robust and competitive results. Furthermore, we introduce and release a new dataset of GCxGC ToF-MS measurements to support reproducibility in future studies. Using an identity-aware cross-validation strategy, where test subjects are completely unseen during training, our method achieves approximately 88% accuracy on 504 measurements from 40 individuals.

Druh

D - Stať ve sborníku

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

S - Specificky vyzkum na vysokych skolach

Rok uplatnění

2025

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

Image Analysis

ISBN

978-3-031-95911-0

ISSN

0302-9743

e-ISSN

1611-3349

Počet stran výsledku

14

Strana od-do

457-470

Název nakladatele

Springer, Cham

Místo vydání

—

Místo konání akce

Reykjavik

Datum konání akce

23. 6. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

001553875500032