Study on Characterization of Hand and Foot Odor in Different Populations Based on Genetic Algorithm-Random Forest
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.
thermal desorptiongas chromatography-mass spectrometry(GC-MS)hand and foot odorgenetic algorithm(GA)random forest(RF)identification
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