Artificial neural network (ANN) model for prediction and optimization of bacoside A content in <i>Bacopa monnieri</i>: a statistical approach and experimental validation

Padhiari, Bhuban Mohan; Ray, Asit; Champati, Bibhuti Bhusan; Jena, Sudipta; Sahoo, Ambika; Kuanar, Ananya; Halder, Tarun; Ghosh, Biswajit; Naik, Pradeep Kumar; Patnaik, Jeetendranath; Mohanty, Sujata; Panda, Pratap Chandra; Nayak, Sanghamitra

doi:10.1080/11263504.2022.2048278

Cited by 4 publications

(2 citation statements)

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“…Each neuron in the layer below calculates a linear combination of the data from the input layer’s neurons, and then adds weight values associated with certain nodes to it.The neurons in the hidden layer combine the linear information from the input layer with a transfer function (a particular non-linear function), which results in the output being the projected model [ 14 ]. About the environment and edaphic factors, the ANN model has been used to predict the bioactive content of Podophyllum hexandrum’s podophyllotoxin [ 16 ], Hypericum perforatum L’s hyperforin, hypericin, and pseudohypericin [ 11 ], and Bacopa monnieri’s bacoside A content [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery

et al. 2023

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Propolis is a promising natural product that has been extensively researched and studied for its potential health and medical benefits. The lack of requisite high oil-containing propolis and existing variation in the quality and quantity of essential oil within agro-climatic regions pose a problem in the commercialization of essential oil. As a result, the current study was carried out to optimize and estimate the essential oil yield of propolis. The essential oil data of 62 propolis samples from ten agro-climatic areas of Odisha, as well as an investigation of their soil and environmental parameters, were used to construct an artificial neural network (ANN) based prediction model. The influential predictors were determined using Garson’s algorithm. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. The results revealed that the most suited model was multilayer-feed-forward neural networks with an R2 value of 0.93. According to the model, altitude was found to have a very strong influence on response, followed by phosphorous & maximum average temperature. This research shows that using an ANN-based prediction model with a response surface methodology technique to estimate oil yield at a new site and maximize propolis oil yield at a specific site by adjusting variable parameters is a viable commercial option. To our knowledge, this is the first report on the development of a model to optimize and estimate the essential oil yield of propolis.

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Section: Introductionmentioning

confidence: 99%

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery

et al. 2023

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“…The projected model is the outcome of the neurons in the hidden layer combining the linear data from the input layer with a transfer function (a specific non-linear function) [16]. The ANN model has been used to predict the bioactive content of the compounds podophyllotoxin from Podophyllum hexandrum [18]; hyperforin, hypericin, and pseudohypericin from Hypericum perforatum L. [13]; and bacoside A from Bacopa monnieri [19].…”

Section: Introductionmentioning

confidence: 99%

Artificial Neural Network and Response Surface-Based Combined Approach to Optimize the Oil Content of Ocimum basilicum var. thyrsiflora (Thai Basil)

Sahu

Nayak

Bhuyan

et al. 2023

Plants

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Ocimum basilicum var. thyrsiflora is valuable for its medicinal properties. The barriers to the commercialization of essential oil are the lack of requisite high oil-containing genotypes and variations in the quantity and quality of essential oils in different geographic areas. Thai basil’s essential oil content is significantly influenced by soil and environmental factors. To optimize and predict the essential oil yield of Thai basil in various agroclimatic regions, the current study was conducted. The 93 datasets used to construct the model were collected from samples taken across 10 different agroclimatic regions of Odisha. Climate variables, soil parameters, and oil content were used to train the Artificial Neural Network (ANN) model. The outcome showed that a multilayer feed-forward neural network with an R squared value of 0.95 was the most suitable model. To understand how the variables interact and to determine the optimum value of each variable for the greatest response, the response surface curves were plotted. Garson’s algorithm was used to discover the influential predictors. Soil potassium content was found to have a very strong influence on responses, followed by maximum relative humidity and average rainfall, respectively. The study reveals that by adjusting the changeable parameters for high commercial significance, the ANN-based prediction model with the response surface methodology technique is a new and promising way to estimate the oil yield at a new site and maximize the essential oil yield at a particular region. To our knowledge, this is the first report on an ANN-based prediction model for Ocimum basilicum var. thyrsiflora.

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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

Mohapatra

Ray

Jena

et al. 2023

South African Journal of Botany

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Artificial neural network (ANN) model for prediction and optimization of bacoside A content in Bacopa monnieri: a statistical approach and experimental validation

Cited by 4 publications

References 50 publications

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery

Developing a computational toolbased on an artificial neural network for predicting and optimizing propolis oil, an important natural product for drug discovery

Artificial Neural Network and Response Surface-Based Combined Approach to Optimize the Oil Content of Ocimum basilicum var. thyrsiflora (Thai Basil)

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

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