Smell Sensing and Classification by Machine Learning

This paper is concerned with smell sensing and classification using machine learning. Sensors used here is metal-oxide semi-conductor gas sensors and classification is machine learning. After explaining sensing principle, we show the classification results of coffee companies and kinds of coffees. Then their method apply to sensing and classification of human body smell so called Kunkun Body. The Kunkun Body can classify one of three human smells such as sweaty smell, middle-aged smell, and old-aged smell using four smell sensors. To train the Kunkun Body we gathered 2,100 persons who have one of three human body smells. We divide them into three groups according to each smell for human body and divide into 10 classes according to the density levels. The first stage for learning is to train the neural network of competitive learning to chemicals which are main components for human body smells. After pre-learning we settle the final values of weighting coefficients as the initial values. The real data for 2,100 persons are divided three groups for typical each smell and ten levels for strength. If the misclassification does not decrease, we adjust the structure of the neural network by increasing the number of neurons. When we can get high accuracy classification results, those weighting values will be stored and their values are used for Kunkun Body. Copyright © VBRI Press.