🤖 AI Summary
This study addresses the challenges of modeling and controlling the pectin hydrolysis-extraction process, which involves highly coupled parameters and extensive experimental dependency. Leveraging a dataset of 1,000 experiments, it presents the first systematic comparison of 11 machine learning algorithms for simultaneously predicting four critical quality indicators: pectin yield, galacturonic acid content, molecular weight, and degree of esterification. The CatBoost model achieves the best performance (mean R² = 0.946), with raw material type identified as the most influential factor, contributing 63.6% to predictive accuracy. By integrating explainable AI techniques and hyperparameter optimization, the authors develop an end-to-end intelligent modeling and deployment pipeline, culminating in an interactive web-based control system that substantially reduces experimental costs and advances the intelligent manufacturing of pectin.
📝 Abstract
This study addresses the challenge of controlling a complex, multi-parameter technological process -- pectin hydrolysis--extraction -- using machine learning methods. The experimental foundation is a unique database comprising 1,000 laboratory experiments conducted under controlled conditions on seven types of plant raw material with four variable process factors (temperature 85--130 C, pressure 0.9--2.2 atm, holding time 3--10 min, pH 1.5--2.0). Four output characteristics were recorded: pectin yield, galacturonic acid content, molecular weight, and degree of esterification. To solve the multi-task regression problem, 11 algorithms were trained and compared: regularised linear models, ensemble methods (Random Forest, Gradient Boosting, XGBoost, CatBoost, Extra Trees), k-nearest neighbours, support vector regression, and a multilayer perceptron. The best results were demonstrated by CatBoost (average R-squared approximately 0.946 after hyperparameter optimisation). Feature importance analysis revealed the dominant role of the raw material type (63.6% of total importance), followed by temperature and holding time. The developed pipeline was exported in a production-ready format and deployed as an interactive web interface. The findings demonstrate that ensemble methods combined with rigorous statistical analysis and interpretable AI significantly reduce the need for physical experiments and form the basis for intelligent pectin production control.