Irzal Ahmad Sabilla, Zakiya Azizah Cahyaningtyas, R. Sarno, Asra Al Fauzi, D. Wijaya, Rudy Gunawan
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引用次数: 3
摘要
人类两种生理性别都有相同的激素,但水平不同。激素水平的差异使两性在几个方面得以区分。受荷尔蒙影响的事情之一就是出汗。汗臭与人类腋下的大汗腺有关。该实验研究了基于成人腋窝白天汗液的两性分类。采样方法采用电子鼻(E-nose)系统采集腋下汗液气味。电子鼻系统传感器阵列由7个传感器组成:TGS 822、TGS 2612、TGS 2620、TGS 826、TGS 2603、TGS 2600和TGS 813。这些传感器产生电阻比(Rs/Ro)值,通过机器学习方法学习分类和基于汗液中挥发性有机化合物(VOC)的疾病潜力。研究表明,男性样本的胺类气体含量高于女性样本,其中一种是三甲胺(TMA)。TMA是一种会分解成三甲胺- n -氧化物(TMAO)的化合物,是导致各种心血管疾病的一个因素。采用主成分分析(PCA)作为预处理方法,支持向量机(SVM)作为机器学习方法,对人类生物性别进行分类,准确率达到94.12%。
Classification of Human Gender from Sweat Odor using Electronic Nose with Machine Learning Methods
Both human biological genders have the same hormone but at different levels. The difference in hormone levels makes the two genders distinguishable from several aspects. One of the things that are influenced by hormones is sweat. The odor of sweat is related to the apocrine glands found in human armpits. This experiment studied the classification of both genders based on daytime sweat in adult human armpits. The sampling method used an electronic nose (E-nose) system to collect the armpit sweat odor. The E-nose system sensor array consisted of seven sensors: TGS 822, TGS 2612, TGS 2620, TGS 826, TGS 2603, TGS 2600, and TGS 813. These sensors generate resistance ratio (Rs/Ro) values which are learned by the machine learning methods for classification and disease potential based on the volatile organic compound (VOC) in sweat. The study shows the male samples have higher amine gas than female samples, one of which is Trimethylamine (TMA). TMA is a compound that will be broken down into trimethylamine-N-oxide (TMAO), a factor to various cardiovascular diseases. The result achieved 94.12% accuracy in classifying human biological gender using principal component analysis (PCA) as the pre-processing method and support vector machine (SVM) as the machine learning method.