A Comparative Analysis of Machine Learning Algorithms for Multi-Task Prediction of the Parameters of the Pectin Hydrolysis--Extraction Process
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, en...
| Authors | Mullosharaf K. Arabov, Shavkat Yo. Kholov, Zainiddin K. Muhiddin |
|---|---|
| Primary category | cs.LG |
| Published | 30 May 2026 |
| Last updated at source | 30 May 2026 |
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