Integrated Machine Learning Models for Bakery Product Defect Prediction
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...
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| Format: | Article |
| Language: | English |
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Anhalt University of Applied Sciences
2025-04-01
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| Series: | Proceedings of the International Conference on Applied Innovations in IT |
| Subjects: | |
| Online Access: | https://icaiit.org/paper.php?paper=13th_ICAIIT_1/3_6 |
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| _version_ | 1839639229891608576 |
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| author | Nataliia Zaiets Nataliia Lutska Lidiia Vlasenko |
| author_facet | Nataliia Zaiets Nataliia Lutska Lidiia Vlasenko |
| author_sort | Nataliia Zaiets |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-8a38eb5d04be4df79f5b3bfec51a3a6a |
| institution | Matheson Library |
| issn | 2199-8876 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Anhalt University of Applied Sciences |
| record_format | Article |
| series | Proceedings of the International Conference on Applied Innovations in IT |
| spelling | doaj-art-8a38eb5d04be4df79f5b3bfec51a3a6a2025-07-04T11:10:50ZengAnhalt University of Applied SciencesProceedings of the International Conference on Applied Innovations in IT2199-88762025-04-01132519320110.25673/119234Integrated Machine Learning Models for Bakery Product Defect PredictionNataliia Zaiets0Nataliia Lutska1Lidiia Vlasenko2Department of Automation and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony 12v, 03041 Kyiv, UkraineDepartment of Automation and Computer Technologies of Control Systems, National University of Food Technologies, Volodymyrska 68, 01033 Kyiv, UkraineDepartment of Automation and Robotic Systems, National University of Life and Environmental Sciences of Ukraine, Heroiv Oborony 12v, 03041 Kyiv, UkraineThe 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.https://icaiit.org/paper.php?paper=13th_ICAIIT_1/3_6defect predictionmachine learning modelscorrectionbakery products |
| spellingShingle | Nataliia Zaiets Nataliia Lutska Lidiia Vlasenko Integrated Machine Learning Models for Bakery Product Defect Prediction Proceedings of the International Conference on Applied Innovations in IT defect prediction machine learning models correction bakery products |
| title | Integrated Machine Learning Models for Bakery Product Defect Prediction |
| title_full | Integrated Machine Learning Models for Bakery Product Defect Prediction |
| title_fullStr | Integrated Machine Learning Models for Bakery Product Defect Prediction |
| title_full_unstemmed | Integrated Machine Learning Models for Bakery Product Defect Prediction |
| title_short | Integrated Machine Learning Models for Bakery Product Defect Prediction |
| title_sort | integrated machine learning models for bakery product defect prediction |
| topic | defect prediction machine learning models correction bakery products |
| url | https://icaiit.org/paper.php?paper=13th_ICAIIT_1/3_6 |
| work_keys_str_mv | AT nataliiazaiets integratedmachinelearningmodelsforbakeryproductdefectprediction AT nataliialutska integratedmachinelearningmodelsforbakeryproductdefectprediction AT lidiiavlasenko integratedmachinelearningmodelsforbakeryproductdefectprediction |