Control system for the process of preparation of dough based on the regression tree model
DOI:
https://doi.org/10.31548/energiya4(74).2024.005Abstract
The paper develops a machine learning model for the dough preparation process control system at a bakery, which includes dough quality monitoring, control and forecasting. The control system will ensure continuous analysis of dough parameters, such as temperature, humidity, lifting force and titran acidity, which will ensure timely detection of deviations in dough quality and avoidance of production problems. In addition, it will contribute to the automation of the quality control process and ensure the stability of production processes, which will have a positive impact on the company's reputation and customer satisfaction. The study considered seven machine learning models, including three linear and four types of binary decision trees. The analysis of the best model - a regression decision tree - confirmed its effectiveness and the validity of the resulting hierarchical structure, in particular, the prediction of important dough indicators with a high degree of reliability, which indicates its potential in practical applications in production.
Key words: dough, baking production, machine learning, monitoring, forecasting, decision tree
References
R. Suchintita Das, B.K. Tiwari and M. Garcia-Vaquero (2023). "The Fundamentals of Bread Making: The Science of Bread." Traditional European Breads: An Illustrative Compendium of Ancestral Knowledge and Cultural Heritage. Cham: Springer International Publishing, 1-40, doi: 10.1007/978-3-031-23352-4_1.
L. N. Meda, T. N. Sipiwe, and D. Ignatius (2023). "Optimization of baking parameters in producing bread through development of a generic control system." in American Institute of Physics Conference Series, 2581(1),. 030007, doi: 10.1063/5.0126294.
Dymchenko, G. Milan and G. Tomáš Gregor (2023). "Trends in bread waste utilisation," in Trends in Food Science & Technology, 132, 93-102, doi: 10.1016/j.tifs.2023.01.004 .
K. Świąder and M. Marczewska (2021). "Trends of using sensory evaluation in new product development in the food industry in countries that belong to the eit regional innovation scheme", Foods, 10, 446, doi:10.3390/foods10020446.
N. Zaiets, N. Lutska and L. Vlasenko (2022). "Improving the Efficiency of a Multistage Evaporator Station for Sugar Production Using Neural Networks," 2022 IEEE 4th International Conference on Modern Electrical and Energy System (MEES), Kremenchuk, Ukraine, 2022, 1-5, doi: 10.1109/MEES58014.2022.10005745.
N. Lutska, N. Zaiets, L. Vlasenko (2024). "Development of Diagnostic System for the State of Electric Drives of Food Enterprise," in Przeglad Elektrotechniczny, 2024(2), 164, doi: 10.15199/48.2024.02.33.
N. Zaiets, N. Lutska, L. Vlasenko and A. Zhyltsov (2023). "Forecasting Breakdowns of Electric Motors of a Sugar Factory Using Machine Learning Methods," 2023 IEEE 4th KhPI Week on Advanced Technology (KhPIWeek), Kharkiv, Ukraine, 2023, 1-6, doi: 10.1109/KhPIWeek61412.2023.10312974.
B. di Diodoro, C. Bax, N. Dellarosa, F. Corazza, G. Langfelder and L. Capelli (2022). "Bread baking monitoring by smart sensory system: a feasibility study," 2022 IEEE International Symposium on Olfaction and Electronic Nose (ISOEN), Aveiro, Portugal, 2022, 1-4, doi: 10.1109/ISOEN54820.2022.9789664.
Kumar, J. Rawat, N. Mohd, and S. Husain (2021). "Opportunities of artificial intelligence and machine learning in the food industry," Journal of Food Quality, 2021, 1-10, doi: 10.1155/2021/4535567.
J.P. Usuga Cadavid, S. Lamouri, B. Grabot, R. Pellerin and A. Fortin (2020). "Machine learning applied in production planning and control: A state-of-the-art in the era of industry 4.0", Journal of Intelligent Manufacturing, 31, 1531-1558, doi: 10.1007/s10845-019-01531-7.
J. Lee, Y. Kim and S. Kim (2023). "The Study of an Adaptive Bread Maker Using Machine Learning," Foods, 12(22), 4160, doi: 10.3390/foods12224160.
C. Gonzalez Viejo, N. M. Harris, and S. Fuentes (2022). "Quality Traits of Sourdough Bread Obtained by Novel Digital Technologies and Machine Learning Modelling," Fermentation, 8(10), 516, doi: 10.3390/fermentation8100516.
B. Charbuty, and A. Abdulazeez (2021). "Classification based on decision tree algorithm for machine learning," Journal of Applied Science and Technology Trends, 2(01), 20-28, doi: 10.38094/jastt20165.
ZH Zhou, Decision Trees (2021). Machine Learning. Springer, Singapore, 79–102, doi: 10.1007/978-981-15-1967-3_4
H. H. Patel, and P. Prajapati (2018). "Study and analysis of decision tree based classification algorithms," International Journal of Computer Sciences and Engineering, 6(10), 74-78, 2018.
S. Kaparthi, and D. Bumblauskas (2020). "Designing predictive maintenance systems using decision tree-based machine learning techniques," International Journal of Quality & Reliability Management, 37(4), 659-686, doi: 10.1108/IJQRM-04-2019-0131.
Antoniadis, S. Lambert-Lacroix, and J. M. Poggi (2021). "Random forests for global sensitivity analysis: A selective review," Reliability Engineering & System Safety, 206, 107312, doi: 10.1016/j.ress.2020.107312.
G. James, D. Witten, T. Hastie, R. Tibshirani and J. Taylor (2023). "Resampling methods," in An Introduction to Statistical Learning: with Applications in Python. Cham: Springer International Publishing, 201-228, doi: 10.1007/978-3-031-38747-0_5.
Yu Chong Ho (2019). "Resampling methods: concepts, applications, and justification," in Practical Assessment, Research, and Evaluation, 8 (19), 19, doi: 10.7275/9cms-my97.
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