Comparison of an Artificial Neural Network and a Response Surface Model during the Extraction of Selenium-Containing Protein from Selenium-Enriched Brassica napus L

A comparative study using response surface methodology and artificial neural network for modeling the bio-reduction of hexavalent chromium (Cr⁶⁺) by immobilized cells of Paenibacillus taichungensis strain MAHA in an alginate-gellan gum matrix

Chromium (Cr⁶⁺) waste poses a hazard as it leads to imbalanced ecosystems and severe health issues. Although, it is widely associated with many industries. Chromium (Cr⁶⁺) reduction by the immobilized cells of Paenibacillus taitungensis strain MAHA-MIE was optimized using response surface methodology (RSM) and artificial neural networks (ANN). The RSM-Box-Behnken Design (BBD) was selected to investigate the effects of chromium (Cr⁶⁺) concentration, alginate concentration, gellan gum concentration, bead size, and the number of beads on chromium (Cr⁶⁺) reduction rate. Experimental data from the BBD was used to train a feed-forward, multilayer artificial neural network (ANN). Results show that the ANN model outperformed the response surface methodology (RSM) based on actual and predicted data, with lower errors and a higher R2 value. The ANN model predicted the optimum points as follows: 155 ppm chromium (Cr⁶⁺), 0.32% alginate, 0.65% gellan gum, 0.5 cm beads, and 27 beads. The validation confirmed a high agreement of chromium (Cr⁶⁺) reduction rate between the validation value (99.00%) and the predicted value (99.99%), with the lowest deviation at 0.1%. Modeling abilities were compared using statistical criteria, including Root Mean Square Error (RMSE), Standard Error of Prediction (SEP), Relative Percent Deviation (RPD), and regression coefficients (R2). The ANN analysis showed the high predictive performance, with high R2 (0.9911), low SEP (0.45%), RPD (1.88), and RMSE (1.37%). The results of this study approved that alginate-gellan gum immobilized cells of Paenibacillus taitungensis strain MAHA-MIE could be effectively used for the handling of chromium (Cr⁶⁺).

A comprehensive review on the application of neural network model in microbial fermentation

The development of high-performance strains and the continuous breakthrough of strain screening technology also pose challenges to downstream fermentation optimization and scale-up. Therefore, neural network models are utilized to optimize the fermentation process to meet the goals of boosting yield or lowering cost, with the use of artificial intelligence technology in conjunction with the peculiarities of the fermentation process. High-performance strains' yield rise and fermentation process amplification will be sped up with the aid of neural network models. This paper offers a helpful review for anyone interested in state-of-the-art microbial fermentation processes, as it thoroughly reviews the application of neural network models in predicting fermentation yield, optimizing the fermentation process, and monitoring the fermentation process.

Study on the Residence Time and Texture Prediction of Pea Protein Extrusion Based on Image Analysis

This paper initially involves three main processing parameters: screw speed, feeding speed, and initial material moisture content, exploring the RTD of materials inside the extruder barrel under varying parameters and clarifying the impact of parameter variations on RTD. Finally, machine vision technology was utilized to link extruded product images to texture features, and a texture prediction model based on image features was established using a Back Propagation (BP) neural network. Particle Swarm Optimization (PSO) and Genetic Algorithm (GA) were applied to optimize the BP neural network. The results indicate that the feeding speed has a stronger impact than the screw speed on the extrusion process, and an increase in the initial material moisture content tends to shorten the RTD. Specifically, an increase in screw speed results in a denser product structure, while higher feeding speeds lead to reduced pore size in the microstructure. As the initial material moisture content increased from 55% to 70%, the average residence time MRT decreased from 265.21 s to 166.62 s. Additionally, elevated moisture content causes a more porous microstructure. After optimizing the texture prediction model of extruded products through the application of Particle Swarm Optimization and Genetic Algorithm models, it was discovered that the Genetic Algorithm was more effective in reducing errors (p < 0.05) than the Particle Swarm Optimization algorithm. It was found that the Particle Swarm Optimization model exhibited better prediction performance. The results of the prediction indicated a significant association between the image features of the product and hardness, resilience, and chewiness, as corroborated by correlation coefficients of 0.93913, 0.94040, and 0.94724, respectively.