MLP (multi-layer perceptron) and RBF (radial basis function) neural network approach for estimating and optimizing 6-gingerol content in Zingiber officinale Rosc. in different agro-climatic conditions

Abstract

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.