E-Nose Sensor Array Optimization Based on Volatile Compound Concentration Data

Abstract

Currently, most e-nose studies are for lab-based applications, the e-nose does not provide access from other places. To be able to implement the internet of things (IoT) technology that is gaining momentum, the e-nose device must be efficient. This study proposes a sensor array optimization technique. If in previous studies using electrical signal data, our study used volatile organic compounds concentration data to minimize the use of sensors. From 10 initial sensors used in the e-nose prototype, only 4 sensors remained. The experimental results showed that by using the KNN algorithm, these 4 sensors were able to predict banana samples with an 80% accuracy rate. When applied to the final e-nose product, the prediction accuracy was 78%.