Preparation of Dietary Fiber Modified by Taro Peel and Its NO2- Adsorption Capacity Prediction Based on Algorithm Optimization Extreme Learning Machine
Journal Title: Chinese Journal of Inorganic Analytical Chemistry - Year 2025, Vol 15, Issue 6
Abstract
Based on the response surface methodology，all experimental data，including process parameters and NO2- adsorption capacities were collected. Following data preprocessing，suitable input variables (solid-to-liquid ratio，hydrochloric acid concentration，reaction temperature，and reaction time) were selected. An initial Extreme Learning Machine (ELM) model was constructed using the training dataset. Genetic Algorithm (GA)，Particle Swarm Optimization (PSO)，Sparrow Search Algorithm (SSA)，Grey Wolf Optimizer (GWO)，and Seagull Optimization Algorithm (SOA) were employed to optimize the ELM. The optimized ELM models were trained using the training dataset. The performance of models was evaluated using a testing dataset. Models was assessed by various performance metrics. The results showed that five optimized ELM models outperformed initial ELM model in all performance metrics. Among the five optimization algorithms，SSA-ELM model exhibited the most significant performance. MAE (Mean Absolute Error)，MSE (Mean Squared Error)，RMSE (Root Mean Squared Error)，and MAPE (Mean Absolute Percentage Error) values were 0.023 498，0.000 739 1，0.027 186，and 0.037267%. It was the lowest among all tested models. In terms of the coefficient of determination (R2)，the original ELM model had an R2 value of 0.013 291. The R2 values for the GA-ELM，PSO-ELM，SSA-ELM，GWO-ELM，and SOA-ELM models were 0.867 09，0.980 16，0.999 71，0.999 98，and 0.999 69，respectively. This indicated that the five optimized ELM models possess higher fitting accuracy，better generalization ability，and stability. R2 was a significant improvement values to compare with the original ELM model. The optimized ELM models provided a rapid and accurate predictive tool for estimating NO2- adsorption capacities of modified taro peel dietary fiber under varying processing conditions. This not only reduces experimental costs and time but also enhances production efficiency and product quality，thereby offering a reliable and robust solution for practical applications in the field.
Authors and Affiliations
Zhonghui DENG, Wei XIE
Keywords
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- EP ID EP767903
- DOI 10.20236/j.CJIAC.2025.06.017
- Views 12
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