Effect of jute fiber length on drilling performance of biocomposites: optimization comparison between RSM, ANN, and genetic algorithm

Belaadi, Ahmed; Boumaaza, Messaouda; Alshahrani, Hassan; Bourchak, Mostefa

doi:10.1007/s00170-022-10801-3

Cited by 9 publications

(1 citation statement)

References 54 publications

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“…RSM (Response Surface Methodology) and ANN (Artificial Neural Network) models are commonly employed for data analysis and prediction in optimization experiments across various domains, such as food [ 18 , 19 ] and materials [ 20 , 21 ]. The RSM model utilizes a second-order polynomial model: to study the relationship between one or more response variables and a number of independent variables, allowing for intuitive analysis of the influence of experimental factors on the results [ 22 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Optimization of preparation conditions for Salsola laricifolia protoplasts using response surface methodology and artificial neural network modeling

Guo,

Xi,

Guzailinuer

et al. 2024

Plant Methods

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Background Salsola laricifolia is a typical C3–C4 typical desert plant, belonging to the family Amaranthaceae. An efficient single-cell system is crucial to study the gene function of this plant. In this study, we optimized the experimental conditions by using Box-Behnken experimental design and Response Surface Methodology (RSM)-Artificial Neural Network (ANN) model based on the previous studies. Results Among the 17 experiment groups designed by Box-Behnken experimental design, the maximum yield (1.566 × 106/100 mg) and the maximum number of viable cells (1.367 × 106/100 mg) were obtained in group 12, and the maximum viability (90.81%) was obtained in group 5. Based on these results, both the RSM and ANN models were employed for evaluating the impact of experimental factors. By RSM model, cellulase R-10 content was the most influential factor on protoplast yield, followed by macerozyme R-10 content and mannitol concentration. For protoplast viability, the macerozyme R-10 content had the highest influence, followed by cellulase R-10 content and mannitol concentration. The RSM model performed better than the ANN model in predicting yield and viability. However, the ANN model showed significant improvement in predicting the number of viable cells. After comprehensive evaluation of the protoplast yield, the viability and number of viable cells, the optimal results was predicted by ANN yield model and tested. The amount of protoplast yield was 1.550 × 106/100 mg, with viability of 90.65% and the number of viable cells of 1.405 × 106/100 mg. The corresponding conditions were 1.98% cellulase R-10, 1.00% macerozyme R-10, and 0.50 mol L−1 mannitol. Using the obtained protoplasts, the reference genes (18SrRNA, β-actin and EF1-α) were screened for expression, and transformed with PEG-mediated pBI121-SaNADP-ME2-GFP plasmid vector. There was no significant difference in the expression of β-actin and EF1-α before and after treatment, suggesting that they can be used as internal reference genes in protoplast experiments. And SaNADP-ME2 localized in chloroplasts. Conclusion The current study validated and evaluated the effectiveness and results of RSM and ANN in optimizing the conditions for protoplast preparation using S. laricifolia as materials. These two methods can be used independently of experimental materials, making them suitable for isolating protoplasts from other plant materials. The selection of the number of viable cells as an evaluation index for protoplast experiments is based on its ability to consider both protoplast yield and viability. The findings of this study provide an efficient single-cell system for future genetic experiments in S. laricifolia and can serve as a reference method for preparing protoplasts from other materials.

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