Electronic Nose Signals for Analysing Similarity of Male and Female Axillary Odour to Food Material Aroma

Abstract

Human axillary odour is a unique discussion because it stores various useful information, including health, food quality analysis, and the similarity to the smell of the food eaten which can be done using an electronic nose (enose). However, rarely for research use an e-nose to determine the similarity of human axillary odour to food material odours. This research aimed to determine the similarity between male and female odours based on food material odours, such as garlic, shallot, and red chilli using an e-nose. We propose a method using an electronic nose with 8 Taguchi gas sensor (TGS) and 1 digital & humidity (DHT) sensor, then processing the resulting data using the fast fourier transform (FFT) smoothing method. Sensor data that have anomalies are removed by the quantile method. The standardization process is carried out to the signal data and feature extraction is processed with mean, standard deviation, and minimum value. This study found that the proposed method can produce a fairly good cluster of aroma food materials, faithfully getting the highest accuracy of 96.67 % using support vector classifier (SVC) and k-nearest neighbour (KNN) with each best parameter. We also found that the smell of human armpit sweat generally has a lot of similarity with food materials, specifically that male has the highest similarity to the shallot, while female has the highest level of similarity to garlic. In contrast, red chilli has the lowest level of similarity with human armpit odour.