Integration of Electronic Nose and Machine Learning for Monitoring Food Spoilage in Storage Systems

Abstract

The integration of sensor technology and artificial intelligence (AI) is transforming agricul-ture, particularly in post-harvest management. This study focuses on utilizing an electronic nose (e-nose) system in conjunction with machine learning (ML) models to monitor and detect potato spoilage in storage environments. The e-nose system, equipped with sensitive gas sen-sors, detects volatile organic compounds (VOCs) emitted by potatoes during different spoilage stages. By analyzing these emissions, the system can identify early signs of spoilage, offering a valuable solution for mitigating post-harvest losses, which remain a significant challenge in the agricultural sector. Through a series of controlled experiments, VOCs were captured and ana-lyzed using a neural network model, classifying the potatoes into three categories: fresh, mildly spoiled, and fully spoiled. The neural network was trained on data from multisensory gas analy-sis, achieving a high level of classification accuracy. This study demonstrates that the integration of e-nose technology and ML algorithms can effectively monitor potato quality in storage, providing real-time insights to optimize storage conditions, extend shelf life, and reduce wastage.