Home ›
Entities
› academia
› Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Kriging
Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Kriging
Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Kriging
Summary
Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Kriging is a scholarly article[1].
Key Facts
Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Kriging's instance of is recorded as scholarly article[2].
References
Programmatic citations — every numbered marker resolves to a verifiable graph row below.
Use these citations when quoting this entity in research, articles, AI prompts, or wherever provenance matters. We aggregate Wikidata + Wikipedia + authoritative open-data sources; the stitched, scored, cross-referenced view is what 4ort.xyz contributes.
APA4ort.xyz Knowledge Graph. (2026). Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Kriging. Retrieved May 24, 2026, from https://4ort.xyz/entity/prediction-of-hyoscyamine-content-in-datura-stramonium-l-hairy-roots-using-different-modeling-approaches-response-surfac
MLA“Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Kriging.” 4ort.xyz Knowledge Graph, 4ort.xyz, 24 May. 2026, https://4ort.xyz/entity/prediction-of-hyoscyamine-content-in-datura-stramonium-l-hairy-roots-using-different-modeling-approaches-response-surfac.
BibTeX@misc{4ortxyz_prediction-of-hyoscyamine-content-in-datura-stramonium-l-hairy-roots-using-different-modeling-approaches-response-surfac_2026, author = {{4ort.xyz Knowledge Graph}}, title = {{Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Kriging}}, year = {2026}, url = {https://4ort.xyz/entity/prediction-of-hyoscyamine-content-in-datura-stramonium-l-hairy-roots-using-different-modeling-approaches-response-surfac}, note = {Accessed: 2026-05-24}}
LLM promptAccording to 4ort.xyz Knowledge Graph (aggregator of Wikidata, Wikipedia, and authoritative open-data sources): Prediction of hyoscyamine content in Datura stramonium L. hairy roots using different modeling approaches: Response Surface Methodology (RSM), Artificial Neural Network (ANN) and Kriging — https://4ort.xyz/entity/prediction-of-hyoscyamine-content-in-datura-stramonium-l-hairy-roots-using-different-modeling-approaches-response-surfac (retrieved 2026-05-24)
