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Article
2025
Integrating sensor data, laboratory analysis, and computer vision in machine learning-driven E-Nose systems for predicting tomato shelf life
Integrating sensor data, laboratory analysis, and computer vision in machine learning-driven E-Nose systems for predicting tomato shelf life
Abstract
Assessing the quality of fresh produce is essential to ensure a safe and satisfactory product. Methods to monitor the quality of fresh produce exist; however, they are often expensive, time-consuming, and sometimes require the destruction of the sample. Electronic Nose (E-Nose) technology has been established to track the ripeness, spoilage, and quality of fresh produce. Our study developed a freshness monitoring system for tomatoes, combining E-Nose technology with storage condition monitoring, color analysis, and weight-loss tracking. Different post-purchase scenarios were investigated, focusing on the influence of temperature and mechanical damage on shelf life. Support Vector Classifier (SVC) and k-Nearest Neighbor (kNN) were applied to classify storage scenarios and storage days, while Support Vector Regression (SVR) and kNN regression were used for predicting storage days. By using a data fusion approach with Linear Discriminant Analysis (LDA), the SVC achieved an accuracy of 72.91% in predicting storage days and an accuracy of 86.73% in distinguishing between storage scenarios. The kNN yielded the best regression results, with a Mean Absolute Error (MAE) of 0.841 days and a coefficient of determination of 0.867. The results highlight the method’s potential to predict storage scenarios and storage days, providing insight into the product’s remaining shelf life.
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Chemosensors, 13 (2025), 7, 255.
https://doi.org/10.3390/chemosensors13070255.
ISSN: 2227-9040
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Senge, J. M., Kaltenecker, F., & Krupitzer, C. (2025). Integrating sensor data, laboratory analysis, and computer vision in machine learning-driven E-Nose systems for predicting tomato shelf life. Chemosensors, 13(7). https://doi.org/10.3390/chemosensors13070255
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English
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660 Chemical engineerin
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University bibliography
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Sustainable Development Goals
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@article{Senge2025,
url = {https://hohpublica.uni-hohenheim.de/handle/123456789/18101},
doi = {10.3390/chemosensors13070255},
author = {Senge, Julia Marie and Kaltenecker, Florian and Krupitzer, Christian et al.},
title = {Integrating sensor data, laboratory analysis, and computer vision in machine learning-driven E-Nose systems for predicting tomato shelf life},
journal = {Chemosensors},
year = {2025},
volume = {13},
number = {7},
}