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• W4385371300 abstract "Centella asiatica consists of centellosides that impart medicinal properties to the plant. Diverse geographical regions lead to variation in centelloside content due to the influence of environment and soil conditions. Therefore it is imperative to analyze C. asiatica from different geographical regions for influence of various environmental factors on the yield of centellosides. Considering these factors, the current work attempted to conduct chemotyping of C. asiatica to find out an elite genotype, design an artificial neural network (ANN) model to optimize and predict the drug yield in C. asiatica from various regions. High Performance Liquid Chromatography (HPLC) was used to analyze the centelloside content in the C. asiatica samples collected from 70 different geographical locations of eastern and north eastern states of India. The elite germplasms were selected based on the higher percentage (>8%) of centellosides (sum of major biomarkers i.e, asiaticoside, madecassoside, asiatic acid, and madecassic acid). HPLC analysis showed a significant variation in the centelloside contents that ranged from 0.37% to 10.91%. Hence, 3 germplasm accessions CA-18 (10.91%), CA-37 (9.60%), and CA-38 (9.04%) were selected as elite germplasms on the basis of their drug yielding potential. To develop the ANN model, the soil and climatic data of all the C. asiatica samples collected from 70 accessions were used. Based on its correlation coefficient, MLP (R2=0.94) was superior to RBF (R2=0.87), as it predicted the centelloside content very precisely. Additionally, the sensitivity analysis showed that soil nitrogen, phosphorus, and maximum temperature were the most influential factors that affected centelloside content. The developed ANN model was tested to forecast the centelloside content of a new site and the prediction efficiency was found to be 94.63%. Therefore, the developed ANN model seems to be a promising approach and could be of great importance in predicting locations for optimizing centelloside content in C. asiatica." @default.
• W4385371300 created "2023-07-29" @default.
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• W4385371300 date "2023-09-01" @default.
• W4385371300 modified "2023-10-01" @default.
• W4385371300 title "Artificial neural network based prediction and optimization of centelloside content in Centella asiatica: A comparison between multilayer perceptron (MLP) and radial basis function (RBF) algorithms for soil and climatic parameter" @default.
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• W4385371300 doi "https://doi.org/10.1016/j.sajb.2023.07.019" @default.
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