TY - JOUR
T1 - Statistical and neural intelligence modeling for basil seed mucilage extraction optimization
T2 - Implications for sustainable and cost-effective industrial plant products
AU - Hasan, Sara
AU - Hasan, Muhammad Amish
AU - Hassan, Muhammad Umair
AU - Amin, Muhammad
AU - Noreen, Sobia
AU - Anwar, Asim
AU - Abbas, Nazia Shahana
N1 - Publisher Copyright:
© 2023
PY - 2023/11/15
Y1 - 2023/11/15
N2 - The upsurge in exigency of environmental-friendly and vigorous plant-based products has spurred a substantial increase in the use of plant-based biopolymers, most conspicuously mucilage and gums. Plant extracted mucilage encompasses a group of complex macromolecules and is renowned for its stabilization, thickening and gelling properties, besides its drug delivery potential. Basil (Ocimum basilicum L.) seed mucilage embodies a polysaccharide of plant origin and is often characterized by its branched carbohydrate structure. Its consumption not only offers prospective health advantages but also aligns with an eco-friendly paradigm. In this study, the optimization of the extraction yield of basil seed mucilage (BSM) was done using response surface methodology and artificial neural network. The experimental design encompassed four parameters, namely pH, temperature, contact time and seed/water ratio, using a 3-level central composite design. The response surface methodology (RSM) and genetic algorithm feedforward neural network (ANN) were employed to predict and evaluate the optimal extraction conditions. The optimal conditions for the extraction yield of BSM were determined to be 7 pH, 56 °C temperature, 6 h of contact time and a 1:30 (w/v%) seed/water ratio. These conditions resulted in a BSM extraction actual yield of 9.94%, which was close to the RSM and ANN predicted values, demonstrating the effectiveness of this approach for optimizing the plant-based polymer extraction process parameters.
AB - The upsurge in exigency of environmental-friendly and vigorous plant-based products has spurred a substantial increase in the use of plant-based biopolymers, most conspicuously mucilage and gums. Plant extracted mucilage encompasses a group of complex macromolecules and is renowned for its stabilization, thickening and gelling properties, besides its drug delivery potential. Basil (Ocimum basilicum L.) seed mucilage embodies a polysaccharide of plant origin and is often characterized by its branched carbohydrate structure. Its consumption not only offers prospective health advantages but also aligns with an eco-friendly paradigm. In this study, the optimization of the extraction yield of basil seed mucilage (BSM) was done using response surface methodology and artificial neural network. The experimental design encompassed four parameters, namely pH, temperature, contact time and seed/water ratio, using a 3-level central composite design. The response surface methodology (RSM) and genetic algorithm feedforward neural network (ANN) were employed to predict and evaluate the optimal extraction conditions. The optimal conditions for the extraction yield of BSM were determined to be 7 pH, 56 °C temperature, 6 h of contact time and a 1:30 (w/v%) seed/water ratio. These conditions resulted in a BSM extraction actual yield of 9.94%, which was close to the RSM and ANN predicted values, demonstrating the effectiveness of this approach for optimizing the plant-based polymer extraction process parameters.
KW - Basil seed mucilage
KW - Biopolymer
KW - Branched carbohydrates
KW - Extraction optimization
KW - Predictive modeling
UR - https://www.scopus.com/pages/publications/85167806545
U2 - 10.1016/j.indcrop.2023.117258
DO - 10.1016/j.indcrop.2023.117258
M3 - Article
AN - SCOPUS:85167806545
SN - 0926-6690
VL - 204
JO - Industrial Crops and Products
JF - Industrial Crops and Products
M1 - 117258
ER -
