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Enhancing Precision in Spice Bag Dispensing for Noodle Cup Production through Automated Fuzzy Inference System Integration

by Ayman M. Mansour, Yazan A. Yousef, Mohammad A. Obeidat, Hesham I. Al-salem
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 187 - Number 67
Year of Publication: 2025
Authors: Ayman M. Mansour, Yazan A. Yousef, Mohammad A. Obeidat, Hesham I. Al-salem
10.5120/ijca2025926142

Ayman M. Mansour, Yazan A. Yousef, Mohammad A. Obeidat, Hesham I. Al-salem . Enhancing Precision in Spice Bag Dispensing for Noodle Cup Production through Automated Fuzzy Inference System Integration. International Journal of Computer Applications. 187, 67 ( Dec 2025), 21-28. DOI=10.5120/ijca2025926142

@article{ 10.5120/ijca2025926142,
author = { Ayman M. Mansour, Yazan A. Yousef, Mohammad A. Obeidat, Hesham I. Al-salem },
title = { Enhancing Precision in Spice Bag Dispensing for Noodle Cup Production through Automated Fuzzy Inference System Integration },
journal = { International Journal of Computer Applications },
issue_date = { Dec 2025 },
volume = { 187 },
number = { 67 },
month = { Dec },
year = { 2025 },
issn = { 0975-8887 },
pages = { 21-28 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume187/number67/enhancing-precision-in-spice-bag-dispensing-for-noodle-cup-production-through-automated-fuzzy-inference-system-integration/ },
doi = { 10.5120/ijca2025926142 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2025-12-18T17:50:29+05:30
%A Ayman M. Mansour
%A Yazan A. Yousef
%A Mohammad A. Obeidat
%A Hesham I. Al-salem
%T Enhancing Precision in Spice Bag Dispensing for Noodle Cup Production through Automated Fuzzy Inference System Integration
%J International Journal of Computer Applications
%@ 0975-8887
%V 187
%N 67
%P 21-28
%D 2025
%I Foundation of Computer Science (FCS), NY, USA
Abstract

This paper delves into the integration of an advanced spice bag dispensing system within the Noodle cup production line, focusing specifically on fried noodles. At the heart of this system lies a meticulously designed fuzzy inference system, engineered to enhance precision and adaptability in the identification and placement of spice bags. Leveraging real-time inputs from cameras, production line speed, and spice bag characteristics, the fuzzy system dynamically applies a set of rules, ensuring precise dispensing in the face of uncertainties and variations inherent in the production environment. Drawing comparisons with a scenario involving the classification of 350 collected photos, this study highlights the adaptability and precision of the fuzzy inference system. The results showcase outstanding performance, including an accuracy of 91.43%, precision of 88.24%, recall of 93.75%, and an F1 score of 90.91%. This developed system significantly contributes to elevating the quality of instant noodle production by ensuring the presence of spice packets in all final products, thereby guaranteeing actual quality despite the high-speed nature of the production line. Operating at a rate of 60,000 cartons in an 8-hour workday, each containing 24 cups of instant noodles, this system ensures heightened efficiency and productivity, maintaining a consistently high flavor profile in the production of instant noodle cups.

References
  1. Smith, B., Garcia-Sanchez, F., Wang, L., and Holgado-Tello, F. P. 2019. Artificial intelligence in the food industry: A comprehensive review. Comprehensive Reviews in Food Science and Food Safety, 18(4), 1204–1222, doi:10.1111/1541-4337.1247
  2. Trianasari, M. Ushada and Suharno, "Ergonomic Risk Analysis for Cassava Noodle Production System Using Occupational Repetitive Action (OCRA)," 2019 5th International Conference on Science and Technology (ICST), Yogyakarta, Indonesia, 2019, pp. 1-5, doi: 10.1109/ICST47872.2019.9166284.
  3. K. Peng, X. -l. Zhou, Liu Ji-guang and Ren Hai-yan, "Study on the seasoning packets auto-inspection method of instant noodles," 2011 4th International Congress on Image and Signal Processing, Shanghai, 2011, pp. 293-297, doi: 10.1109/CISP.2011.6100026.
  4. S. Zhou and G. Liang, "Counting Method of Instant Noodle Products Outer Packaging Based on Improved YOLOV8," 2025 IEEE International Conference on Pattern Recognition, Machine Vision and Artificial Intelligence (PRMVAI), Loudi, China, 2025, pp. 1-5, doi: 10.1109/PRMVAI65741.2025.11108605.
  5. Zadeh, L. A. 1965. Fuzzy sets. Information and Control, 8(3), 338–353, doi:10.1016/S0019-9958(65)90241-X
  6. Aharari, A. and Mehdipour, F. 2024. Inventory control in food industry using an AI-driven precision counting. In Proceedings of the IEEE 13th Global Conference on Consumer Electronics (GCCE), Kitakyushu, Japan, 844–847. doi:10.1109/GCCE62371.2024.10760352.
  7. Tian, X. and Ge, P. 2024. Intelligent grain, oil and food processing equipment: AI-based fault diagnosis and predictive maintenance. In Proceedings of the International Conference on Artificial Intelligence, Deep Learning and Neural Networks (AIDLNN), Guangzhou, China, 210–216. doi:10.1109/AIDLNN65358.2024.00041.
  8. K., S., R., A., R., C., and R., N. 2025. AI-powered ingredient detector for allergies: Enhancing food safety through natural language processing and computer vision. In Proceedings of the International Conference on Modern Sustainable Systems.
  9. Zheng, M., Zhang, S., Zhang, Y., and Hu, B. 2021. Construct food safety traceability system for people’s health under the Internet of Things and big data. IEEE Access, 9, 70571–70583. doi:10.1109/ACCESS.2021.3078536.
  10. Misra, N. N., Dixit, Y., Al-Mallahi, A., Bhullar, M. S., Upadhyay, R., and Martynenko, A. 2022. IoT, big data, and artificial intelligence in agriculture and food industry. IEEE Internet of Things Journal, 9(9), 6305–6324. doi:10.1109/JIOT.2020.2998584.
  11. Chukkapalli, S. S. L. et al. 2020. Ontologies and artificial intelligence systems for the cooperative smart farming ecosystem. IEEE Access, 8, 164045–164064. doi:10.1109/ACCESS.2020.3022763.
  12. Dhelia, A., Chordia, S., and B, K. 2024. YOLO-based food damage detection: An automated approach for quality control in food industry. In Proceedings of the 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), Kirtipur, Nepal, 1444–1449. doi:10.1109/I-SMAC61858.2024.10714664.
  13. Korenfeld, M., Yehieli, A., and Winokur, M. 2025. IoT connected AI-powered 3D food printing for smart food production. In Proceedings of the 14th Mediterranean Conference on Embedded Computing (MECO), Budva, Montenegro, 1–4. doi:10.1109/MECO66322.2025.11049163.
  14. Kapoor, R. and Chavan, A. 2025. ToxiScan: AI-powered hazardous food ingredient detection system. In Proceedings of the International Conference on Modern Sustainable Systems (CMSS), Shah Alam, Malaysia, 880–888. doi:10.1109/CMSS66566.2025.11182405.
  15. S., K., Kurian, N. S., Subha, T. D., Nithiya, C., D., R., and Sai, K. C. 2025. IoT-enabled aquaponics with AI prospects: Advancing automated monitoring and sustainable food production. In Proceedings of the 6th International Conference on Intelligent Communication Technologies and Virtual Mobile Networks (ICICV), Tirunelveli, India, 1549–1553. doi:10.1109/ICICV64824.2025.11085781.
  16. Janokar, S., More, K., Pundir, R., Mhamulkar, R., Piraji, S., and Sangvikar, P. 2025. Food ingredient analysis integrating OCR and LLM for enhanced consumer safety. In Proceedings of the 5th International Conference on Soft Computing for Security Applications (ICSCSA), Salem, India, 594–599. doi:10.1109/ICSCSA66339.2025.11171116.
Index Terms

Computer Science
Information Sciences

Keywords

Dispensing System Fuzzy Inference System Instant Noodle Quality Enhancement Efficiency and Productivity