Evidence map›Paper›PMID 34288904›Full record

ArticlePloS one2021

Optimization of diosgenin extraction from Dioscorea deltoidea tubers using response surface methodology and artificial neural network modelling.

Romaan Nazir, Devendra Kumar Pandey, Babita Pandey, Vijay Kumar, Padmanabh Dwivedi, Aditya Khampariya, Abhijit Dey, Tabarak Malik

RetractedAbstract readComparative StudyValidation StudyRetracted Publication
PubMed Full text at PMC Publisher
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It has been retracted, and should not be counted. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

  1. Review
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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

8 authors.

Romaan NazirDepartment of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India.
Devendra Kumar PandeyDepartment of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India.
Babita PandeyDepartment of Computer Science and Information Technology, Babasaheb Bhimrao Ambedkar University, Lucknow, Uttar Pradesh, India.
Vijay KumarDepartment of Biotechnology, Lovely Faculty of Technology and Sciences, Lovely Professional University, Phagwara, Punjab, India.
Padmanabh DwivediDepartment of Plant Physiology, Banaras Hindu University, Varanasi, Uttar Pradesh, India.
Aditya KhampariyaSchool of Computer Science and Engineering, Lovely Professional University, Phagwara, Punjab, India.
Abhijit DeyDepartment of Life Sciences, Presidency University, Kolkata, India.
Tabarak MalikDepartment of Biochemistry, College of Medicine and Health Sciences, University of Gondar, Gondar, Ethiopia.ORCID 0000-0002-8332-7927

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionDioscorea deltoidea var. deltoidea (Dioscoreaceae) is a valuable endangered plant of great medicinal and economic importance due to the presence of the bioactive compound diosgenin. In the present study, response surface methodology (RSM) and artificial neural network (ANN) modelling have been implemented to evaluate the diosgenin content from D. deltoidea. In addition, different extraction parameters have been also optimized and developed. MATERIALS AND

methodsFirstly, Plackett-Burman design (PBD) was applied for screening the significant variables among the selected extraction parameters i.e. solvent composition, solid: solvent ratio, particle size, time, temperature, pH and extraction cycles on diosgenin yield. Among seven tested parameters only four parameters (particle size, solid: solvent ratio, time and temperature) were found to exert significant effect on the diosgenin extraction. Moreover, Box-Behnken design (BBD) was employed to optimize the significant extraction parameters for maximum diosgenin yield.

resultsThe most suitable condition for diosgenin extraction was found to be solid: solvent ratio (1:45), particle size (1.25 mm), time (45 min) and temperature (45°C). The maximum experimental yield of diosgenin (1.204% dry weight) was observed close to the predicted value (1.202% dry weight) on the basis of the chosen optimal extraction factors. The developed mathematical model fitted well with experimental data for diosgenin extraction.

conclusionsExperimental validation revealed that a well trained ANN model has superior performance compared to a RSM model.

Indexed as

Models, TheoreticalNeural Networks, ComputerCalibrationChemical FractionationDioscoreaDiosgeninEndangered SpeciesMolecular StructureParticle SizePlants, MedicinalPlant TubersSolventsTemperatureTimeDiosgeninSolvents

Identifiers

PMID34288904
PMCPMC8294507

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.