Proceedings of International Conference on Applied Innovation in IT
2025/04/26, Volume 13, Issue 1, pp.193-201

Integrated Machine Learning Models for Bakery Product Defect Prediction


Nataliia Zaiets, Nataliia Lutska and Lidiia Vlasenko


Abstract: The paper discusses the development of a model for predicting the probability of occurrence of defects in bakery products using a set of input variables at different stages of the technological process. The model is based on the analysis of data including control variables, such as oven temperature and humidity, as well as disturbance variables characterizing the properties of flour, the dough preparation process and baking of products. Based on the results of the study, a GMM-based model was selected, which demonstrated the highest accuracy, with the achieved Precision and Recall values equal to 1.0 for the class of defective products, which indicates high correctness of forecasts. In terms of Log-Likelihood, the model demonstrated a large difference between the classes, which confirms its ability to accurately classify both defective and non-defective products. The proposed model is an effective tool for predicting defects and optimizing process parameters. It allows you to adjust control variables, such as temperature and humidity, to reduce the amount of defects, ensuring stability of product quality. The article also proposes different methods for adjusting the values of control variables based on historical data. This allows for optimization of the technological process and improvement of the quality of bakery products in real-time production conditions.

Keywords: Defect Prediction, Machine Learning Models, Correction, Bakery Products.

DOI: Under Indexing

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References:

  1. H. J. Bungartz, S. Zimmer, H. Buchholz, and D. Pfluger, "Modeling and simulation," Springer Undergraduate Texts in Mathematics and Technology, vol. 10, 2014, [Online]. Available: https://doi.org/10.1007/978-3-642-39524-6.
  2. F. Beaudoin, K. Sanchez, and P. Perdu, "Dynamic laser stimulation techniques for advanced failure analysis and design debug applications," Microelectronics Reliability, vol. 47, no. 9-11, pp. 1517-1522, 2007, [Online]. Available: https://doi.org/10.1016/j.microrel.2007.07.054.
  3. F. Lin, T. Jia, R. Y. Fung, and P. Wu, "Impacts of inspection rate on integrated inventory models with defective items considering capacity utilization: Rework-versus delivery-priority," Computers & Industrial Engineering, vol. 156, p. 107245, 2021, [Online]. Available: https://doi.org/10.1016/j.cie.2021.107245.
  4. A. Mondal, A. K. Halder, S. Nayak, R. R. Kumar, A. Chakraborty, S. K. Gudimetla, and M. Dubois, "Root cause analysis of indentation mark defect on Zn coated steel sheet in continuous galvanizing line," Engineering Failure Analysis, vol. 157, p. 107883, 2024, [Online]. Available: https://doi.org/10.1016/j.engfailanal.2023.107883.
  5. C. Chen, C. Liu, T. Wang, A. Zhang, W. Wu, and L. Cheng, "Compound fault diagnosis for industrial robots based on dual-transformer networks," Journal of Manufacturing Systems, vol. 66, pp. 163-178, 2023, [Online]. Available: https://doi.org/10.1016/j.jmsy.2022.12.006.
  6. C. Gao, B. Cai, Y. Zhang, X. Shao, C. Yang, and L. Gao, "A life cycle reliability testing and assessment method for deepwater oil and gas equipment systems," Ocean Engineering, vol. 311, part 1, p. 118928, 2024, [Online]. Available: https://doi.org/10.1016/j.oceaneng.2024.118928.
  7. D. Mathews, M. Brinkerink, and P. Deane, "A framework for high resolution coupled global electricity & hydrogen models based on integrated assessment model scenarios," International Journal of Hydrogen Energy, vol. 97, pp. 516-531, 2025, [Online]. Available: https://doi.org/10.1016/j.ijhydene.2024.11.077.
  8. H. Liu, E. I. K. Ibrahim, M. Centanni, C. Sarr, K. Venkatakrishnan, and L. E. Friberg, "Integrated modeling of biomarkers, survival and safety in clinical oncology drug development," Advanced Drug Delivery Reviews, vol. 216, p. 115476, 2025, [Online]. Available: https://doi.org/10.1016/j.addr.2024.115476.
  9. I. Safra, K. Ghachem, F. Benabdallah, H. Albalawi, and L. Kolsi, "Integrated operations planning model for the automotive wiring industry," Heliyon, vol. 10, no. 11, p. e31820, 2024, [Online]. Available: https://doi.org/10.1016/j.heliyon.2024.e31820.
  10. Ö. F. Görçün, A. R. Mishra, A. Aytekin, V. Simic, and S. Korucuk, "Evaluation of Industry 4.0 strategies for digital transformation in the automotive manufacturing industry using an integrated fuzzy decision-making model," Journal of Manufacturing Systems, vol. 74, pp. 922-948, 2024, [Online]. Available: https://doi.org/10.1016/j.jmsy.2024.05.005.
  11. D. Aghajani, H. Seraji, H. Kaur, and J. Vilko, "A sustainable integrated model for multi-objective planning of an agri-food supply chain under uncertain parameters: A case study," Computers & Chemical Engineering, vol. 188, p. 108766, 2024, [Online]. Available: https://doi.org/10.1016/j.compchemeng.2024.108766.
  12. A. Li, H. Bian, B. Liu, Y. Li, and Y. Zhao, "A general defect modelling and simulation-assisted approach for fault isolation in failure analysis," Microelectronics Reliability, vol. 139, p. 114805, 2022, [Online]. Available: https://doi.org/10.1016/j.microrel.2022.114805.
  13. Y. Lin, J. Ma, Q. Wang, and D. W. Sun, "Applications of machine learning techniques for enhancing nondestructive food quality and safety detection," Critical Reviews in Food Science and Nutrition, vol. 63, no. 12, pp. 1649-1669, 2023.
  14. A. Saberironaghi, J. Ren, and M. El-Gindy, "Defect detection methods for industrial products using deep learning techniques: A review," Algorithms, vol. 16, no. 2, p. 95, 2023.
  15. Z. Jia, M. Wang, and S. Zhao, "A review of deep learning-based approaches for defect detection in smart manufacturing," Journal of Optics, vol. 53, no. 2, pp. 1345-1351, 2024.
  16. I. Elía and M. Pagola, "Anomaly detection in Smart-manufacturing era: A review," Engineering Applications of Artificial Intelligence, vol. 139, Part B, p. 109578, 2025, [Online]. Available: https://doi.org/10.1016/j.engappai.2024.109578.
  17. Z.-H. Jiao and X. Shan, "Consecutive statistical evaluation framework for earthquake forecasting: Evaluating satellite surface temperature anomaly detection methods," Journal of Asian Earth Sciences: X, vol. 7, p. 100096, 2022, [Online]. Available: https://doi.org/10.1016/j.jaesx.2022.100096.
  18. N. Lutska, N. Zaiets, and L. Vlasenko, "Development of Diagnostic System for the State of Electric Drives of Food Enterprise," Przegląd elektrotechniczny, vol. 2024, no. 2, pp. 164-167, 2024, [Online]. Available: https://doi.org/10.15199/48.2024.02.33.
  19. J. Pang, D. Liu, Y. Peng, and X. Peng, "Temporal dependence Mahalanobis distance for anomaly detection in multivariate spacecraft telemetry series," ISA Transactions, vol. 140, pp. 354-367, 2023, [Online]. Available: https://doi.org/10.1016/j.isatra.2023.06.002.
  20. A. Oluwasegun and J.-C. Jung, "A multivariate Gaussian mixture model for anomaly detection in transient current signature of control element drive mechanism," Nuclear Engineering and Design, vol. 402, p. 112098, 2023, [Online]. Available: https://doi.org/10.1016/j.nucengdes.2022.112098.
  21. K. Wang, C. Liu, and Y. Lu, "Ensemble Bayesian Network for root cause analysis of product defects via learning from historical production data," Journal of Manufacturing Systems, vol. 75, pp. 102-115, 2024, [Online]. Available: https://doi.org/10.1016/j.jmsy.2024.06.001.
  22. C. Song, L. Niu, and M. Lei, "A Brief Survey on Graph Anomaly Detection," Procedia Computer Science, vol. 242, pp. 1263-1270, 2024, [Online]. Available: https://doi.org/10.1016/j.procs.2024.08.145.
  23. G. Czibula, I.-G. Chelaru, I. G. Czibula, and A.-J. Molnar, "An unsupervised learning-based methodology for uncovering behavioural patterns for specific types of software defects," Procedia Computer Science, vol. 225, pp. 2644-2653, 2023, [Online]. Available: https://doi.org/10.1016/j.procs.2023.10.256.
  24. N. Zaiets, N. Lutska, L. Vlasenko, and A. Zhyltsov, "Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods," 2023 IEEE 4th KhPI Week on Advanced Technology (KhPIWeek), pp. 1-6, Nov. 2023, [Online]. Available: https://doi.org/10.1109/KhPIWeek61412.2023.10312974.
  25. A. Syamsuddin, A. C. Adhi, A. Kusumawardhani, T. Prahasto, and A. Widodo, "Predictive Maintenance Based on Anomaly Detection in Photovoltaic System Using SCADA Data and Machine Learning," Results in Engineering, p. 103589, 2024, [Online]. Available: https://doi.org/10.1016/j.rineng.2024.103589.

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