1. Proceedings
  2. I3M
  3. 2021
  4. MAS
  5. 10.46354/i3m.2021.mas.007

Exploiting Machine Learning and Industry 4.0 traceability technologies to re-engineering the seasoning process of traditional Parma’s Ham

  • a Davide Mezzogori , 
  • b Francesco Zammori
  • a,b University of Parma Dipartimento di Ingegneria e Architettura, Via G.P. Usberti 181/A, Parma, 43100, Parma
Cite as
Mezzogori D., Zammori F. (2021). Exploiting Machine Learning and Industry 4.0 traceability technologies to re-engineering the seasoning process of traditional Parma’s Ham. Proceedings of the 20th International Conference on Modeling & Applied Simulation (MAS 2021), pp. 57-64. DOI: https://doi.org/10.46354/i3m.2021.mas.007

Abstract

The work presents a Machine Learning approach for predicting the quality of the curing process of Parma ham, combined with a study of business process re-engineering, based on RFID and Deep Learning technologies for automatic recognition and tracking of the hams along the curing process. Quality management has proven to be crucial for efficient and effective processes, even more so for the food industry, both for commercial and regulatory purposes. This is even more evident in artisanal-based processes, such as the one concerning traditional Prosciutto di Parma seasoning. The work proposes and compares a Feed-Forward Neural Network and a Random Forest for predicting the distribution of the number of hams by commercial quality class of a given aging lot. Such a prediction, based on origin, process, and curing data, can provide early indications of process output, enabling strategic commercial competitive advantages. The importance of the genetic component in the determination of the final quality is also evaluated, as it is considered one of the most influential external variables. Moreover, following the AS-IS description of the current process, a redesign is proposed, to enable data collection and tracking of individual ham in order to propose a future precision prediction system that would allow even finer control of the process.

References

  1. Dabbene, F., Gay, P., & Tortia, C. (2014). Traceability issues in food supply chain management: A review. Biosystems engineering, 120, 65-80.
  2. Dora, M., Kumar, M., Van Goubergen, D., Molnar, A., & Gellynck, X. (2013). Food quality management system: Reviewing assessment strategies and a feasibility study for European food small and medium-sized enterprises. Food control, 31(2), 607-616.
  3. Evans, J. R., & Lindsay, W. M. (2002). The management and control of quality (Vol. 5, pp. 115-128). Cincinnati, OH: South-western.
  4. Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, No. 10). New York: Springer series in statistics.
  5. García-Esteban, J. A., Curto, B., Moreno, V., & Martín, B. (2018). Quality Control Monitoring by using Artificial Neural Networks for an Iberian Ham Industry. In ICINCO (2) (pp. 638-645).
  6. Goyache, F., Bahamonde, A., Alonso, J., López, S., Del Coz, J. J., Quevedo, J. R., ... & Díez, J. (2001). The usefulness of artificial intelligence techniques to assess subjective quality of products in the food industry. Trends in Food Science & Technology, 12(10), 370-381.
  7. Goodfellow, I., Bengio, Y., Courville, A., & Bengio, Y. (2016). Deep learning (Vol. 1, No. 2). Cambridge: MIT press.
  8. Kelepouris, T., Pramatari, K., & Doukidis, G. (2007). RFID‐enabled traceability in the food supply chain. Industrial Management & data systems.
  9. Lin, T. Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., ... & Zitnick, C. L. (2014, September). Microsoft coco: Common objects in context. In European conference on computer vision (pp. 740-755). Springer, Cham.
  10. Redmon, J., & Farhadi, A. (2018). Yolov3: An incremental improvement. arXiv preprint arXiv:1804.02767.
  11. Pan, S. J., & Yang, Q. (2009). A survey on transfer learning. IEEE Transactions on knowledge and data engineering, 22(10), 1345-1359.
  12. Wessel R., San Daniele Prosciutto to Wear RFID Seal of Authenticity, https://www.rfidjournal.com/san-daniele-prosciutto-to-wear-rfid-seal-of-authenticity
  13. Zu, X., Fredendall, L. D., & Douglas, T. J. (2008). The evolving theory of quality management: the role of Six Sigma. Journal of operations Management, 26(5), 630-650.