Romaan Nazir1, Devendra Kumar Pandey1, Babita Pandey2, Vijay Kumar1, Padmanabh Dwivedi3, Aditya Khampariya4, Abhijit Dey5, Tabarak Malik6. 1. Department of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India. 2. Department of Computer Science and Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, Uttar Pradesh, India. 3. Department of Plant Physiology, Banaras Hindu University, Varanasi, Uttar Pradesh, India. 4. School of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India. 5. Department of Life Sciences, Presidency University, Kolkata, India. 6. Department of Biochemistry, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.
Abstract
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.
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.
Authors: Astrid Kosters; Raoul J J M Frijters; Cindy Kunne; Edwin Vink; Marit S Schneiders; Frank G Schaap; Catherina P Nibbering; Shailendra B Patel; Albert K Groen Journal: Hepatology Date: 2005-01 Impact factor: 17.425
Authors: Muhammad Nabeel Ghayur; Syed Faraz Kazim; Huma Rasheed; Asaad Khalid; Maliha Iqbal Jumani; Muhammad Iqbal Choudhary; Anwarul Hassan Gilani Journal: Zhong Xi Yi Jie He Xue Bao Date: 2011-06