Integration of Electronic Nose and Machine Learning for Monitoring Food Spoilage in Storage Systems
Seilov S. Abildinov D. Baydeldinov M. Nurzhaubayev A. Zhursinbek B. Yue X.G.
19 December 2024International Federation of Engineering Education Societies (IFEES)
International Journal of Online and Biomedical Engineering
2024#20Issue 16117 - 130 pp.
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.
electronic nose (e-nose) , gas analysis system , machine learning (ML) , post-harvest loss , potato spoilage , volatile organic compounds (VOC)
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Faculty of Information Technologies, L.N. Gumilyov Eurasian National University, Astana, Kazakhstan
Kazakh Academy of Infocommunications, Astana, Kazakhstan
Department of Computer Science and Engineering, School of Sciences, European University Cyprus, Nicosia, Cyprus
Faculty of Information Technologies
Kazakh Academy of Infocommunications
Department of Computer Science and Engineering
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