Artificial neural network-based model for the prediction of optimal growth and culture conditions for maximum biomass accumulation in multiple shoot cultures of Centella asiatica

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• 作者：Archana Prasad ; Om Prakash ; Shakti Mehrotra ; Feroz Khan ; Ajay Kumar Mathur&#8230
• 关键词：Centella asiatica ; Multiple shoot cultures ; Media optimization ; Artificial neural network modelling
• 刊名：Protoplasma
• 出版年：2017
• 出版时间：January 2017
• 年：2017
• 卷：254
• 期：1
• 页码：335-341
• 全文大小：
• 刊物类别：Biomedical and Life Sciences
• 刊物主题：Cell Biology; Plant Sciences; Zoology;
• 出版者：Springer Vienna
• ISSN：1615-6102
• 卷排序：254

文摘

An artificial neural network (ANN)-based modelling approach is used to determine the synergistic effect of five major components of growth medium (Mg, Cu, Zn, nitrate and sucrose) on improved in vitro biomass yield in multiple shoot cultures of Centella asiatica. The back propagation neural network (BPNN) was employed to predict optimal biomass accumulation in terms of growth index over a defined culture duration of 35 days. The four variable concentrations of five media components, i.e. MgSO₄ (0, 0.75, 1.5, 3.0 mM), ZnSO₄ (0, 15, 30, 60 μM), CuSO₄ (0, 0.05, 0.1, 0.2 μM), NO₃ (20, 30, 40, 60 mM) and sucrose (1, 3, 5, 7 %, w/v) were taken as inputs for the ANN model. The designed model was evaluated by performing three different sets of validation experiments that indicated a greater similarity between the target and predicted dataset. The results of the modelling experiment suggested that 1.5 mM Mg, 30 μM Zn, 0.1 μM Cu, 40 mM NO₃ and 6 % (w/v) sucrose were the respective optimal concentrations of the tested medium components for achieving maximum growth index of 1654.46 with high centelloside yield (62.37 mg DW/culture) in the cultured multiple shoots. This study can facilitate the generation of higher biomass of uniform, clean, good quality C. asiatica herb that can efficiently be utilized by pharmaceutical industries.