The Optimisation of Bitter Gourd-Grape Beverage Fermentation Using a Consolidated Response Surface Methodology (RSM) and Artificial Neural Network (ANN) Approach.

Tintswalo Lindi Maselesele, Tumisi Beiri Jeremiah Molelekoa, Sefater Gbashi, Oluwafemi Ayodeji Adebo

Abstract read

In one paragraph

Article in Plants (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. 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

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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.

Optimization of Ethanol Extraction Conditions From Sun-Dried Apricot and Prediction of Antioxidant, Phenolic, and Flavonoid Contents and Their Effects on HCT116 Cells Using Artificial Neural Networks.Food science & nutrition · 2025
Article
Ultrasound-assisted extraction, enrichment, and hypolipidemic potential of triterpenes from Grifola frondosa mycelia.BMC chemistry · 2025
Article

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Tintswalo Lindi MaseleseleFood Innovation Research Group, Department of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, P.O. Box 17011, Johannesburg 2028, South Africa.ORCID 0000-0003-0608-3444

Tumisi Beiri Jeremiah MolelekoaDepartment of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, Doornfontein Campus, P.O. Box 17011, Johannesburg 2028, South Africa.ORCID 0000-0003-1090-7514

Sefater GbashiDepartment of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, Doornfontein Campus, P.O. Box 17011, Johannesburg 2028, South Africa.ORCID 0000-0001-7869-0484

Oluwafemi Ayodeji AdeboFood Innovation Research Group, Department of Biotechnology and Food Technology, Faculty of Science, University of Johannesburg, P.O. Box 17011, Johannesburg 2028, South Africa.ORCID 0000-0002-3757-5137

Funding

National Research Foundation 121826University of Johannesburg Global Excellence and Stature (GES) 4.0 Catalytic Initiative Grant

6 · The paper itself

Abstract

The present study adopted a response surface methodology (RSM) approach validated by artificial neural network (ANN) models to optimise the production of a bitter gourd-grape beverage. Aset of statistically pre-designed experiments were conducted, and the RSM optimisation model fitted to the obtained data, yielding adequately fit models for the monitored control variables R

Indexed as

ANNbitter gourd beveragefermentationoptimisationRSM

Identifiers

PMID37836213

DOI10.3390/plants12193473

PMCPMC10575144

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LicenceCC BY

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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.