Search

Background: Asparagus racemosus has been attaining its importance due to its high medicinal and pharmaceutical value. This plant resulted in the depletion of its wild population in India. This plant has been placed in the nearly threatened and endangered category of the International Union for Conservation of Nature and Natural Resources (IUCN) in the states of Chhattisgarh, Madhya Pradesh, and West Bengal. The over-demand also poses the problem of adulteration in several ways. Objectives: Selection of elite germplasm for commercial cultivation was done by studying the variation of Shatavarin IV from different geographical regions. The development of simple and reliable high-performance thin layer chromatography (HPTLC) method and chromatographic profile was done for quality control. Materials and Methods: A total of 103 Asparagus racemosus root samples were collected from different agro-climatic zones of Odisha. The methanol root extracts were analyzed qualitatively and quantitatively using HPTLC. The pre-coated silica gel 60 F254 plates were used for the development of chromatograms with ethyl acetate‒methanol-water (7.5:1.5:1, v/v/v) as a mobile phase. Results: The Shatavarin IV was detected at Rf of 0.40 ± 0.05 and showed maximum absorption at 425 nm. The method was validated in terms of linearity, limit of detection (LOD), quantification (LOQ), precision, stability, and recovery test. The amount of Shatavarin IV varied between 0.01-0.40% among different regions. Conclusion: The elite germplasms were identified at Ramagiri Hill sides of Gajapati district, Odisha, having Shatavarin IV content <0.39%. The HPTLC method used here was found to be reliable, precise, and can be used for the quality control assessment of Asparagus racemosus. [ABSTRACT FROM AUTHOR]

The principal bioactive phenolic component of ginger (Zingiber officinale Rosc.) rhizome is 6-gingerol, which is known to have several sensory and therapeutic properties. Due to its antimicrobial, anti-biofilm, anti-cancer, and anti-inflammatory properties, a sizable global market for ginger has been generated. The variation in yield of 6-gingerol is observed with changes in environmental, agronomic, and genetic conditions. The current study aims to examine how environmental and climatic factors affect 6-gingerol content using an artificial neural network (ANN) model and predict a new location to produce 6-gingerol. ANN-based models; MLP (multilayer perceptron) and RBF (radial basis function) with the desirable learning algorithms, transfer functions, number of neurons, and hidden layers were designed. The dataset of 60 unique Zingiber officinale rhizomes obtained from ten agro-climatic zones of Odisha was included in this study. The content of 6-gingerol was ascertained using a high-performance thin-layer chromatography (HPTLC) technique. The experimental dataset was randomly divided into 70 % for training, 15 % for testing, and 15 % for validation to carry out the optimization. In 60 accessions from various locations of Odisha, the concentration of 6-gingerol in ginger rhizomes ranged from 0.203 % to 0.813 % on a wet weight basis. It was revealed that the ANN model with the MLP algorithm was superior to the RBF algorithm. The findings showed that an ANN-MLP architecture with a single hidden layer of 11 neurons and a 16–11–1 topology could accurately predict the content of 6-gingerol, with an R2 (coefficient of determination) of 0.99. Altitude, pH, maximum temperature, and phosphorus were identified as the factors that influenced the model's prediction accuracy, which was 95 %. The content of 6-gingerol was enhanced from 0.363 % to 0.408 % by regulating the present model's sensitive parameters. The current study concluded that the developed ANN model efficiently predicts the best region for optimum 6-gingerol yield. [Display omitted] • Multilayer perceptron and Radial basis function models were compared for 6-gingerol yield in ginger. • First Multilayer perceptron application for optimal 6-gingerol production across Odisha. • Altitude, pH and maximum temperature have prominent impact on the 6-gingerol production. • Identified best agroclimatic zone and conditions for 6-gingerol production in Zingiber officinale plant. • MLP-ANN model was more than 95 % efficient in predicting 6-gingerol production in a new site. [ABSTRACT FROM AUTHOR]

The principal bioactive ingredient of Asparagus racemosus is Shatavarin IV, a steroidal saponin found in roots with a wide range of medicinal properties. A. racemosus roots are used in Ayurveda system of medicine for curing a spectrum of diseases like diarrhea, nervous disorders, dysentery, dyspepsia, tumors, inflammations, neuropathy, hyperdipsia, hepatopathy, cough, hyperacidity, bronchitis and certain infectious diseases. Many of the factors influence the production and accumulation of secondary metabolites including environmental, developmental, and genetic factors. An artificial neural network (ANN) based model was developed in this study to evaluate the influence of abiotic factors (climate and soil) and to forecast a suitable site for collecting and cultivating A. racemosus with high Shatavarin IV concentration. The experimental dataset contains 103 A. racemosus accessions collected from various phytogeographical zones of Odisha. Fourteen input parameters such as soil factors (nitrogen, phosphorus, potassium, sulphur, organic carbon, pH, electrical conductivity) and climatic factors (altitude, relative humidity, UV index, average temperature, minimum temperature, maximum temperature, annual precipitation) were considered for the study. For training, testing and validation, the datasets were randomly divided into 75%, 15%, and 15%, respectively. A validated high performance thin layer chromatography (HPTLC) method was used to determine the Shatavarin IV content. The quantity of Shatavarin IV in A. racemosus root extracts varied from 0.010% to 0.400% on dry weight basis among 103 accessions collected from various regions of Odisha. Two ANN model algorithms namely MLP (multilayer perceptron) and RBF (radial basis function) were used for prediction and optimization of Shatavarin IV content using environmental and soil factors. The ANN model with MLP algorithm was found to be better model as compared to the RBF algorithm. The results demonstrated that an ANN-MLP architecture with a single hidden layer of 8 neurons having 14–8–1 topology could reliably predict Shatavarin IV content with a coefficient of determination (R2) of 0.970. The prediction efficiency of the developed model was 90.740%. Sensitivity analysis revealed that nitrogen and phosphorus have a greater impact on Shatavarin IV biosynthesis than the other parameters. The content of Shatavarin IV could be increased from 0.245% to 0.300% by managing these sensitive factors in the developed model. The developed ANN model would be very useful in predicting the best regions/sites for A. racemosus with optimum Shatavarin IV yield. [Display omitted] • Artificial Neural Network (ANN) model was used in A. racemosus for the first time. • The factors affecting Shatavarin IV content were determined in A. racemosus • Multilayer perceptron was more efficient than Radial basis function algorithm. • Nitrogen & Phosphorus were found to be the most influential parameters. • ANN model could be used for optimization & prediction of Shatavarin IV content. [ABSTRACT FROM AUTHOR]