Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling
| العنوان: | Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling |
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| المؤلفون: | Tabarak Malik, Romaan Nazir, Aditya Khampariya, Babita Pandey, Vijay Kumar, Abhijit Dey, Padmanabh Dwivedi, Devendra Kumar Pandey |
| المصدر: | PLoS ONE PLoS ONE, Vol 16, Iss 7, p e0253617 (2021) |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | 02 engineering and technology, Plant Science, Chemical Fractionation, chemistry.chemical_compound, Mathematical and Statistical Techniques, Animal Cells, Medicinal Plants, Mathematics, Neurons, 0303 health sciences, Multidisciplinary, Artificial neural network, biology, Molecular Structure, Dioscorea, Organic Compounds, Plant Anatomy, Experimental Design, Statistics, Temperature, Eukaryota, Diosgenin, Plants, 021001 nanoscience & nanotechnology, Solvent, Plant Tubers, Chemistry, Research Design, Calibration, Physical Sciences, Medicine, Regression Analysis, Chloroform, Cellular Types, 0210 nano-technology, Research Article, Optimization, Computer and Information Sciences, Dioscoreaceae, Science, Research and Analysis Methods, Time, 03 medical and health sciences, Artificial Intelligence, Response surface methodology, Particle Size, Statistical Methods, Artificial Neural Networks, 030304 developmental biology, Computational Neuroscience, Chromatography, Plants, Medicinal, Tubers, Extraction (chemistry), Endangered Species, Organic Chemistry, Organisms, Chemical Compounds, Biology and Life Sciences, Computational Biology, Cell Biology, Models, Theoretical, biology.organism_classification, chemistry, Yield (chemistry), Cellular Neuroscience, Solvents, Particle size, Neural Networks, Computer, Neuroscience |
| الوصف: | Introduction Dioscorea deltoidea var. deltoidea (Dioscoreaceae) is a valuable endangered plant of great medicinal and economic importance due to the presence of the bioactive compound diosgenin. In the present study, response surface methodology (RSM) and artificial neural network (ANN) modelling have been implemented to evaluate the diosgenin content from D. deltoidea. In addition, different extraction parameters have been also optimized and developed. Materials and methods Firstly, Plackett-Burman design (PBD) was applied for screening the significant variables among the selected extraction parameters i.e. solvent composition, solid: solvent ratio, particle size, time, temperature, pH and extraction cycles on diosgenin yield. Among seven tested parameters only four parameters (particle size, solid: solvent ratio, time and temperature) were found to exert significant effect on the diosgenin extraction. Moreover, Box-Behnken design (BBD) was employed to optimize the significant extraction parameters for maximum diosgenin yield. Results The most suitable condition for diosgenin extraction was found to be solid: solvent ratio (1:45), particle size (1.25 mm), time (45 min) and temperature (45°C). The maximum experimental yield of diosgenin (1.204% dry weight) was observed close to the predicted value (1.202% dry weight) on the basis of the chosen optimal extraction factors. The developed mathematical model fitted well with experimental data for diosgenin extraction. Conclusions Experimental validation revealed that a well trained ANN model has superior performance compared to a RSM model. |
| تدمد: | 1932-6203 |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::da895236b2ca876555be7e17c471fdf0 https://pubmed.ncbi.nlm.nih.gov/34288904 |
| حقوق: | OPEN |
| رقم الأكسشن: | edsair.doi.dedup.....da895236b2ca876555be7e17c471fdf0 |
| قاعدة البيانات: | OpenAIRE |
| تدمد: | 19326203 |
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