Application of artificial neural network (ANN) model for prediction and optimization of coronarin D content in Hedychium coronarium

Abdipour, 2018, Modeling oil content of sesame (Sesamum indicum L.) using artificial neural network and multiple linear regression approaches, J. Am. Oil Chem. Soc., 95, 283, 10.1002/aocs.12027

Abdipour, 2019, Artificial neural networks and multiple linear regression as potential methods for modeling seed yield of safflower (Carthamus tinctorius L.), Ind. Crops Prod., 127, 185, 10.1016/j.indcrop.2018.10.050

Ahmadi, 2014, Modeling root length density of field grown potatoes under diff ;erent irrigation strategies and soil textures using artificial neural networks, Field Crops Res., 162, 99, 10.1016/j.fcr.2013.12.008

Akbar, 2016, Development of prediction model and experimental validation in predicting the curcumin content of turmeric (Curcuma longa L.), Front. Plant Sci., 7, 1507, 10.3389/fpls.2016.01507

Akbar, 2018, Application of Artificial Neural Network modeling for optimization and prediction of essential oil yield in turmeric (Curcuma longa L.), Comput. Electron. Agr., 148, 160, 10.1016/j.compag.2018.03.002

Alam, 2009, Impact of soil nutrients and environmental factors on podophyllotoxin content among 28 Podophyllum hexandrum populations of northwestern Himalayan region using linear and nonlinear approaches, Comm. Soil. Sci. Plant. Ana., 40, 2485, 10.1080/00103620903111368

Alvarez, 2009, Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach, Eur. J.Agron., 30, 70, 10.1016/j.eja.2008.07.005

Baş, 2007, Modeling and optimization II: comparison of estimation capabilities of response surface methodology with artificial neural networks in a biochemical reaction, J. Food Eng., 78, 846, 10.1016/j.jfoodeng.2005.11.025

Bray, 1945, Determination of total, organic, and available forms of phosphorus in soils, Soil Sci., 59, 39, 10.1097/00010694-194501000-00006

Bremner, 1982, Nitrogen - total, 595

Céline, 2009, Medicinal plants from the Yanesha (Peru): evaluation of the leishmanicidal and antimalarial activity of selected extracts, J. Ethnopharmacol., 123, 413, 10.1016/j.jep.2009.03.041

Chan, 2015, Phytochemistry and pharmacology of ornamental gingers, Hedychium coronarium and Alpinia purpurata: a review, J. Integr. Med., 13, 368, 10.1016/S2095-4964(15)60208-4

Chen, 2013, New labdane-type diterpenoids and anti-inflammatory constituents from Hedychium coronarium, Int. J. Mol. Sci., 14, 13063, 10.3390/ijms140713063

Chen, 2017, Coronarin D induces reactive oxygen species-mediated cell death in human nasopharyngeal cancer cells through inhibition of p38 MAPK and activation of JNK, Oncotarget., 8, 108006, 10.18632/oncotarget.22444

Chimnoi, 2009, Phytochemical reinvestigation of labdane-type diterpenes and their cytotoxicity from the rhizomes of Hedychium coronarium, Phytochem. Lett., 2, 184, 10.1016/j.phytol.2009.06.003

Dai, 2011, Simulation for response of crop yield to soil moisture and salinity with artificial neural network, Field. Crop. Res., 121, 441, 10.1016/j.fcr.2011.01.016

Desai, 2008, Comparison of artificial neural network (ANN) and response surface methodology (RSM) in fermentation media optimization: case study of fermentative production of scleroglucan, Biochem. Eng. J., 41, 266, 10.1016/j.bej.2008.05.009

Donipati, 2015, In vitro anticancer activity of Hedychium coronarium against human breast cancer cell line MCF-7, Int. J. Adv. Res., 3, 1497

Emamgholizadeh, 2015, Seed yield prediction of sesame using artificial neural network, Eur. J. Agron., 68, 89, 10.1016/j.eja.2015.04.010

Fieuzal, 2017, Estimation of corn yield using multi-temporal optical and radar satellite data and artificial neural networks, Int. J. Appl. Earth Obs. Geoinf., 57, 14

Figueiredo, 2008, Factors affecting secondary metabolite production in plants: volatile components and essential oils, Flavour Fragr. J., 23, 213, 10.1002/ffj.1875

Gairola, 2010, Influence of climate change on production of secondary chemicals in high altitude medicinal plants: issues needs immediate attention, J. Med. Plant Res., 4, 1825

Ilaiyaraja, 2015, Optimisation of extraction of bioactive compounds from Feronia limonia (wood apple) fruit using response surface methodology (RSM), Food Chem., 173, 348, 10.1016/j.foodchem.2014.10.035

Kaomongkolgit, 2012, Antifungal activity of coronarin D against Candida albicans, Oral Surg. Oral Med. Oral Pathol. Oral Radiol., 114, 61, 10.1016/j.oooo.2012.01.010

Kazem, 2017, Comparison of prediction methods of photovoltaic power system production using a measured dataset, Energy Convers. Manage., 148, 1070, 10.1016/j.enconman.2017.06.058

Kumar, 2009, Modelling the solid–liquid adsorption processes using artificial neural networks trained by pseudo second order kinetics, Chem. Eng. J., 148, 20, 10.1016/j.cej.2008.07.026

Kunnumakkara, 2008, Coronarin D, a labdane diterpene, inhibits both constitutive and inducible nuclear factor-κB pathway activation, leading to potentiation of apoptosis, inhibition of invasion, and suppression of osteoclastogenesis, Mol. Cancer Ther., 7, 3306, 10.1158/1535-7163.MCT-08-0432

Kwon, 2003, Optimization of microwave-assisted extraction (MAP) for ginseng components by response surface methodology, J. Agric. Food Chem., 51, 1807, 10.1021/jf026068a

Lin, 2018, Coronarin D induces apoptotic cell death through the JNK pathway in human hepatocellular carcinoma, Environ. Toxicol., 33, 946, 10.1002/tox.22579

Lindsay, 1978, Development of a DTPA soil test for zinc, iron, manganese, and copper 1, Soil Sci. Soc. Am. J., 42, 421, 10.2136/sssaj1978.03615995004200030009x

Liu, 2016, Influence of environmental factors on the active substance production and antioxidant activity in Potentilla fruticosa L. and its quality assessment, Sci. Rep., 6, 28591, 10.1038/srep28591

Marschner, 2011

Matsuda, 2002, Labdane-type diterpenes with inhibitory effects on increase in vascular permeability and nitric oxide production from Hedychium coronarium, Bioorg. Med. Chem., 10, 2527, 10.1016/S0968-0896(02)00121-9

Matsumura, 2015, Maize yield forecasting by linear regression and artificial neural networks in Jilin, China, J. Agric. Sci., 153, 399, 10.1017/S0021859614000392

Michelon, 2018, Artificial neural networks to estimate the productivity of soybeans and corn by chlorophyll readings, J. Plant Nutr., 41, 1285, 10.1080/01904167.2018.1447579

Miller, 2019, Prediction of bioconcentration factors in fish and invertebrates using machine learning, Sci. Total Environ., 648, 80, 10.1016/j.scitotenv.2018.08.122

Mohammadi, 2016, Application of artificial neural networks in order to predict Mahabad River discharge, Open J. Ecol., 6, 427, 10.4236/oje.2016.67040

Moradi, 2013, The optimized operational conditions for biodiesel production from soybean oil and application of artificial neural networks for estimation of the biodiesel yield, Renew. Energy, 50, 915, 10.1016/j.renene.2012.08.070

Morikawa, 2002, New farnesane-type sesquiterpenes, hedychiols A and B 8, 9-diacetate, and inhibitors of degranulation in RBL-2H3 cells from the rhizome of Hedychium coronarium, Chem. Pharm. Bull., 50, 1045, 10.1248/cpb.50.1045

Mussatto, 2011, A study on chemical constituents and sugars extraction from spent coffee grounds, Carbohydr. Polym., 83, 368, 10.1016/j.carbpol.2010.07.063

Nelson, 1982, Total carbon, organic carbon, and organic matter 1, 539

Oh, 2006, Synthesis and biological activity of (+)-hedychilactone A and its analogs from (+)-sclareolide, Bioorg. Med. Chem. Lett., 16, 1656, 10.1016/j.bmcl.2005.12.009

Pachurekar, 2017, A review on pharmacognostical phytochemical and ethnomedicinal properties of Hedychium coronarium J. Koenig an endangered medicine, Int. J. Chin. Med., 1, 49

Page, 1982

Parida, 2015, Chemical composition of essential oil from leaf and rhizome of micropropagated and conventionally grown Hedychium coronarium Koen. from Eastern India, J. Essent. Oil. Bear. Pl., 18, 161, 10.1080/0972060X.2014.960268

Park, 2002, Environmental correlation of three-dimensional soil spatial variability: a comparison of three adaptive techniques, Geoderma, 109, 117, 10.1016/S0016-7061(02)00146-5

Rahman, 2010, Modelling of jute production using artificial neural networks, Biosyst. Eng., 105, 350, 10.1016/j.biosystemseng.2009.12.005

Ray, 2017, Assessment of the terpenic composition of Hedychium coronarium oil from Eastern India, Ind. Crop. Prod., 97, 49, 10.1016/j.indcrop.2016.11.063

Ray, 2018, Chemical diversity, antioxidant and antimicrobial activities of the essential oils from Indian populations of Hedychium coronarium Koen, Ind. Crop. Prod., 112, 353, 10.1016/j.indcrop.2017.12.033

Reuk-ngam, 2014, Antimicrobial activity of coronarin D and its synergistic potential with antibiotics, Biomed Res. Int., 2014, 10.1155/2014/581985

Rout, 2015, Role of iron in plant growth and metabolism, Rev. Agric. Sci., 3, 1, 10.7831/ras.3.1

Ruan, 1998, Effect of potassium, magnesium and sulphur applied in different forms of fertilisers on free amino acid content in leaves of tea (Camellia sinensis L), J. Sci. Food Agric., 76, 389, 10.1002/(SICI)1097-0010(199803)76:3<389::AID-JSFA963>3.0.CO;2-X

Singh, 2012, Comparison of soil and water assessment tool (SWAT) and multilayer perceptron (MLP) artificial neural network for predicting sediment yield in the Nagwa agricultural watershed in Jharkhand, India, Agric. Water Manag., 104, 113, 10.1016/j.agwat.2011.12.005

Sinha, 2012, Response surface optimization and artificial neural network modeling of microwave assisted natural dye extraction from pomegranate rind, Ind. Crop. Prod., 37, 408, 10.1016/j.indcrop.2011.12.032

Sodeifian, 2016, Optimization of essential oil extraction from Launaea acanthodes Boiss: Utilization of supercritical carbon dioxide and cosolvent, J. Supercrit. Fluid., 116, 46, 10.1016/j.supflu.2016.05.015

Somaratne, 2005, Prediction of soil organic carbon across different land-use patterns, Soil Sci. Soc. Am. J., 69, 1580, 10.2136/sssaj2003.0293

Van Kiem, 2011, Chemical constituents of the rhizomes of Hedychium coronarium and their inhibitory effect on the pro-inflammatory cytokines production LPS-stimulated in bone marrow-derived dendritic cells, Bioorg. Med. Chem. Lett., 21, 7460, 10.1016/j.bmcl.2011.09.129

Wang, 2018, Establishment of wheat yield prediction model in dry farming area based on neural network, NeuroQuantology, 16, 768

Wang, 2013, The critical role of potassium in plant stress response, Int. J. Mol. Sci., 14, 7370, 10.3390/ijms14047370