Quality control of marketed herbal products of Asparagus racemosus Willd. through high performance thin layer chromatography (HPTLC) analysis

Asparagus racemosus Willd. is a valuable medicinal plant which is used all over the world. There are several marketed products of A. racemosus. The high demand for this herb has increased the risk of adulteration in its commercial products. The adulterated herbal products might pose serious ill effects on health. Therefore, it is necessary to check the quality of marketed products in terms of the presence of their major bioactive compounds. The present study aimed to carry out the qualitative and quantitative analysis of Shatavarin IV in marketed products of A. racemosus through a validated high performance thin layer chromatography (HPTLC) method. Ten marketed products were analysed and all of them had shown the presence of Shatavarin IV which was quantified. The identification and quantification were done by taking a standard Shatavarin IV as reference. The Shatavarin IV was detected at Rf 0.4±0.05 and showed maximum absorption at 425 nm. The Shatavarin IV was quantified using a 6-point calibration curve having a standard deviation of 3.89 % with an R2 value of 0.9968. The amount of Shatavarin IV varied between 1.47±0.25 to 2.69±0.51 mg/g on a dry weight basis which is a normal range in the raw plant materials. Thus, the present findings would be a simple, reliable and cost-effective method for the quality determination of herbal products of A. racemosus. The developed HPTLC chromatograms would serve as a reference for the quality assessment of commercial products of A. racemosus in future

Application of a Multilayer Perceptron Artificial Neural Network for the Prediction and Optimization of the Andrographolide Content in Andrographis paniculata

Andrographolide, the principal secondary metabolite of Andrographis paniculata, displays a wide spectrum of medicinal activities. The content of andrographolide varies significantly in the species collected from different geographical regions. Therefore, this study aims at investigating the role of different abiotic factors and selecting suitable sites for the cultivation of A. paniculata with high andrographolide content using a multilayer perceptron artificial neural network (MLP-ANN) approach. A total of 150 accessions of A. paniculata collected from different regions of Odisha and West Bengal in eastern India showed a variation in andrographolide content in the range of 0.28–5.45% on a dry weight basis. The MLP-ANN was trained using climatic factors and soil nutrients as the input layer and the andrographolide content as the output layer. The best topological ANN architecture, consisting of 14 input neurons, 12 hidden neurons, and 1 output neuron, could predict the andrographolide content with 90% accuracy. The developed ANN model showed good predictive performance with a correlation coefficient (R2) of 0.9716 and a root-mean-square error (RMSE) of 0.18. The global sensitivity analysis revealed nitrogen followed by phosphorus and potassium as the predominant input variables influencing the andrographolide content. The andrographolide content could be increased from 3.38% to 4.90% by optimizing these sensitive factors. The result showed that the ANN approach is reliable for the prediction of suitable sites for the optimum andrographolide yield in A. paniculata