IoT-Enabled Food Freshness Detection Using Multi-Sensor Data Fusion and Mobile Sensing Interface
Research Article  ·  Published: 30 June 2025
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ICCK Transactions on Sensing, Communication, and Control
Volume 2, Issue 2, 2025: 122-131
Research Article Open Access

IoT-Enabled Food Freshness Detection Using Multi-Sensor Data Fusion and Mobile Sensing Interface

1 Graduate School of Information Science and Technology, The University of Osaka, Japan
2 Faculty of Computer Science and Engineering, Ghulam Ishaq Khan Institute, Pakistan
3 Department of Computer Science, Islamia College Peshawar, KPK, Pakistan
* Corresponding Author: Mazhar Iqbal, [email protected]
Volume 2, Issue 2

Abstract

Ensuring the freshness of food products is essential for both acute and chronic health outcomes. However, significant health risks can be triggered by dietary resources subjected to improper storage protocols. Current methods are often unreliable and unfeasible for detecting food freshness. This research proposes an IoT-based food freshness detection system that uses biosensors and gas sensors to monitor perishable items like meat, produce, and dairy. The system is integrated with a mobile application that allows users to analyze food quality in real-time, based on predefined degradation thresholds. This study assists in providing valuable insights for future research and improving food safety by contributing to the data storage of food-contextual sensor thresholds. This strategy leads to more informed decision-making by consumers, mitigating food wastage and promoting healthier food choices in the process.

Graphical Abstract

IoT-Enabled Food Freshness Detection Using Multi-Sensor Data Fusion and Mobile Sensing Interface

Keywords

food freshness IoT system biosensors mobile application spoilage detection

Data Availability Statement

Data will be made available on request.

Funding

This work was supported without any funding.

Conflicts of Interest

The authors declare no conflicts of interest.

Ethical Approval and Consent to Participate

Not applicable.

References

  1. Wang, D., Zhang, M., Jiang, Q., & Mujumdar, A. S. (2024). Intelligent system/equipment for quality deterioration detection of fresh food: Recent advances and application. Foods, 13(11), 1662.
    [CrossRef] [Google Scholar]
  2. Loutfi, A., Coradeschi, S., Mani, G. K., Shankar, P., & Rayappan, J. B. B. (2015). Electronic noses for food quality: A review. Journal of Food Engineering, 144, 103-111.
    [CrossRef] [Google Scholar]
  3. Lu, L., Hu, Z., Hu, X., Li, D., & Tian, S. (2022). Electronic tongue and electronic nose for food quality and safety. Food Research International, 162, 112214.
    [CrossRef] [Google Scholar]
  4. Alam, A. N., Hashem, M. A., Matar, A. M., Ali, M. S., Monti, J. A., Hossain, M. J., ... & Mia, N. (2024). Cutting edge technologies for the evaluation of plant-based food and meat quality: A comprehensive review. Meat Research, 4(1).
    [CrossRef] [Google Scholar]
  5. Akinsemolu, A. A., & Onyeaka, H. N. (2024). Microorganisms associated with food spoilage and foodborne diseases. In Food Safety and Quality in the Global South (pp. 489-531). Springer.
    [CrossRef] [Google Scholar]
  6. Shen, D., Zhang, M., Mujumdar, A. S., & Ma, Y. (2024). Consumer-oriented smart dynamic detection of fresh food quality: Recent advances and future prospects. Critical Reviews in Food Science and Nutrition, 64(30), 11281-11301.
    [CrossRef] [Google Scholar]
  7. Bendre, S., Shinde, K., Kale, N., & Gilda, S. (2022). Artificial intelligence in food industry: A current panorama. Asian Journal of Pharmacy and Technology, 12(3), 242-250. http://dx.doi.org/10.52711/2231-5713.2022.00040
    [Google Scholar]
  8. Khan, H., Jan, Z., Ullah, I., Alwabli, A., Alharbi, F., Habib, S., ... & Koo, J. (2024). A deep dive into AI integration and advanced nanobiosensor technologies for enhanced bacterial infection monitoring. Nanotechnology Reviews, 13(1), 20240056.
    [CrossRef] [Google Scholar]
  9. Inês, A., & Cosme, F. (2025). Biosensors for detecting food contaminants—An overview. Processes, 13(2), 380.
    [CrossRef] [Google Scholar]
  10. Chen, Y., Wang, Y., Zhang, Y., Wang, X., Zhang, C., & Cheng, N. (2024). Intelligent biosensors promise smarter solutions in Food Safety 4.0. Foods, 13(2), 235.
    [CrossRef] [Google Scholar]
  11. Pallavi, L., Prasad, P., Hariharan, G., Karthikayani, K., & Shudapreyaa, R. (2022). IoT and mobile app-based food spoilage alert system. International Journal of Health Sciences, 6, 13911-13924.
    [CrossRef] [Google Scholar]
  12. Nath, S. (2024). Advancements in food quality monitoring: Integrating biosensors for precision detection. Sustainable Food Technology, 2, 112-125.
    [CrossRef] [Google Scholar]
  13. Konfo, T. R. C., Tchekessi, C. K. C., & Baba-Moussa, F. A. K. (2024). Status report on innovations and applications of smart bio-systems for real-time monitoring of food quality. Applied Food Research, 5, 100546.
    [CrossRef] [Google Scholar]
  14. Chun, M., Yu, H. J., & Jung, H. (2024). A deep learning-based rotten food recognition app for older adults: Development and usability study. JMIR Formative Research, 8, e55342.
    [CrossRef] [Google Scholar]
  15. Stephan, T., Paramana, P. P. D., Lin, C. C., Agarwal, S., & Verma, R. (2025). Federated learning-driven IoT system for automated freshness monitoring in resource-constrained vending carts. Journal of Big Data, 12(1), 1-34.
    [CrossRef] [Google Scholar]
  16. Doğan, V., Evliya, M., Kahyaoglu, L. N., & Kılıç, V. (2024). On-site colorimetric food spoilage monitoring with smartphone embedded machine learning. Talanta, 266, 125021.
    [CrossRef] [Google Scholar]
  17. Kolikipogu, R., Shivaputra, Muniyandy, E., Maroor, J. P., Lakshmi, G. V. R., Konduri, B., & Naveenkumar, R. (2025). Improving Food Safety by IoT-based Climate Monitoring and Control Systems for Food Processing Plants. Remote Sensing in Earth Systems Sciences, 1-13.
    [CrossRef] [Google Scholar]
  18. Liu, Y., Han, W., Zhang, Y., Li, L., Wang, J., & Zheng, L. (2016). An Internet-of-Things solution for food safety and quality control: A pilot project in China. Journal of Industrial Information Integration, 3, 1-7.
    [CrossRef] [Google Scholar]
  19. Almassar, K. M., & Khasawneh, M. T. (2024). Using IoT and AI to replenish household food supplies: A systematic review. Journal of Smart Cities and Society, 3(1), 23-62.
    [CrossRef] [Google Scholar]
  20. Naik, A., Lee, H. S., Herrington, J., Barandun, G., Flock, G., Güder, F., & Gonzalez-Macia, L. (2024). Smart Packaging with Disposable NFC-enabled Wireless Gas Sensors for Monitoring Food Spoilage. ACS sensors, 9(12), 6789-6799.
    [CrossRef] [Google Scholar]
  21. Poh, K. H., Tan, C. Y., Ruslan, N. S. M., & Othman, W. A. F. W. (2019). Design and implementation of simple IoT-based smart home system using arduino. Technical Journal of Electrical Electronic Engineering and Technology, 3(1), 1-13.
    [Google Scholar]
  22. Mishra, N., Jain, S. K., Agrawal, N., Jain, N. K., Wadhawan, N., & Panwar, N. L. (2023). Development of drying system by using internet of things for food quality monitoring and controlling. Energy Nexus, 11, 100219.
    [CrossRef] [Google Scholar]
  23. Huang, H., Liu, L., & Ngadi, M. O. (2014). Recent developments in hyperspectral imaging for assessment of food quality and safety. Sensors, 14(4), 7248-7276.
    [CrossRef] [Google Scholar]
  24. Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., & Martynenko, A. (2020). IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of things Journal, 9(9), 6305-6324.
    [CrossRef] [Google Scholar]
  25. Arduino Team. (2015). Arduino Uno Rev3 technical specifications. Arduino Documentation.
    [Google Scholar]
  26. Zhou, G. H., Xu, X. L., & Liu, Y. (2010). Preservation technologies for fresh meat—A review. Meat Science, 86(1), 119-128.
    [CrossRef] [Google Scholar]
  27. Swathi, B., Pooja, T., Shankar, Y., & Gowtham, V. (2022, March). Survey on IoT based farm freshness mobile application. In 2022 International Conference on Advanced Computing Technologies and Applications (ICACTA) (pp. 1-7). IEEE.
    [CrossRef] [Google Scholar]

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Cite This Article

APA Style
Iqbal, M., Yousaf, J., Khan, A., & Muhammad, T. (2025). IoT-Enabled Food Freshness Detection Using Multi-Sensor Data Fusion and Mobile Sensing Interface. ICCK Transactions on Sensing, Communication, and Control, 2(2), 122–131. https://doi.org/10.62762/TSCC.2025.401245
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TY  - JOUR
AU  - Iqbal, Mazhar
AU  - Yousaf, Junaid
AU  - Khan, Atif
AU  - Muhammad, Tila
PY  - 2025
DA  - 2025/06/30
TI  - IoT-Enabled Food Freshness Detection Using Multi-Sensor Data Fusion and Mobile Sensing Interface
JO  - ICCK Transactions on Sensing, Communication, and Control
T2  - ICCK Transactions on Sensing, Communication, and Control
JF  - ICCK Transactions on Sensing, Communication, and Control
VL  - 2
IS  - 2
SP  - 122
EP  - 131
DO  - 10.62762/TSCC.2025.401245
UR  - https://www.icck.org/article/abs/TSCC.2025.401245
KW  - food freshness
KW  - IoT system
KW  - biosensors
KW  - mobile application
KW  - spoilage detection
AB  - Ensuring the freshness of food products is essential for both acute and chronic health outcomes. However, significant health risks can be triggered by dietary resources subjected to improper storage protocols. Current methods are often unreliable and unfeasible for detecting food freshness. This research proposes an IoT-based food freshness detection system that uses biosensors and gas sensors to monitor perishable items like meat, produce, and dairy. The system is integrated with a mobile application that allows users to analyze food quality in real-time, based on predefined degradation thresholds. This study assists in providing valuable insights for future research and improving food safety by contributing to the data storage of food-contextual sensor thresholds. This strategy leads to more informed decision-making by consumers, mitigating food wastage and promoting healthier food choices in the process.
SN  - 3068-9287
PB  - Institute of Central Computation and Knowledge
LA  - English
ER  - 
BibTeX Format
Compatible with LaTeX, BibTeX, and other reference managers
@article{Iqbal2025IoTEnabled,
  author = {Mazhar Iqbal and Junaid Yousaf and Atif Khan and Tila Muhammad},
  title = {IoT-Enabled Food Freshness Detection Using Multi-Sensor Data Fusion and Mobile Sensing Interface},
  journal = {ICCK Transactions on Sensing, Communication, and Control},
  year = {2025},
  volume = {2},
  number = {2},
  pages = {122-131},
  doi = {10.62762/TSCC.2025.401245},
  url = {https://www.icck.org/article/abs/TSCC.2025.401245},
  abstract = {Ensuring the freshness of food products is essential for both acute and chronic health outcomes. However, significant health risks can be triggered by dietary resources subjected to improper storage protocols. Current methods are often unreliable and unfeasible for detecting food freshness. This research proposes an IoT-based food freshness detection system that uses biosensors and gas sensors to monitor perishable items like meat, produce, and dairy. The system is integrated with a mobile application that allows users to analyze food quality in real-time, based on predefined degradation thresholds. This study assists in providing valuable insights for future research and improving food safety by contributing to the data storage of food-contextual sensor thresholds. This strategy leads to more informed decision-making by consumers, mitigating food wastage and promoting healthier food choices in the process.},
  keywords = {food freshness, IoT system, biosensors, mobile application, spoilage detection},
  issn = {3068-9287},
  publisher = {Institute of Central Computation and Knowledge}
}

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CC BY Copyright © 2025 by the Author(s). Published by Institute of Central Computation and Knowledge. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made.
ICCK Transactions on Sensing, Communication, and Control
ICCK Transactions on Sensing, Communication, and Control
ISSN: 3068-9287 (Online) | ISSN: 3068-9279 (Print)
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