Evidence map›Paper›PMID 36140893›Full record

ArticleFoods (Basel, Switzerland)2022

Physico-Chemical Properties Prediction of Flame Seedless Grape Berries Using an Artificial Neural Network Model.

Adel M Al-Saif, Mahmoud Abdel-Sattar, Abdulwahed M Aboukarima, Dalia H Eshra, Krzysztof Górnik

Open access · goldAbstract read
In one paragraph

Article in Foods (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
2.7field-weighted citation impact, top 10% of its field
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

3 citing papers in PubMed, 14 citations in OpenAlex.

  1. Article
  2. Article
  3. 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

5 authors at 3 institutions in 3 countries.

Adel M Al-SaifDepartment of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia.
Mahmoud Abdel-SattarDepartment of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia.
Abdulwahed M AboukarimaDepartment of Agricultural Engineering, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, Riyadh 11451, Saudi Arabia.ORCID 0000-0002-2729-362X
Dalia H EshraFood Science and Technology Department, Faculty of Agriculture, Alexandria University, Alexandria 21545, Egypt.
Krzysztof GórnikThe National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland.
King Saud University · SAAlexandria University · EGInstytut Ogrodnictwa – Państwowy Instytut Badawczy · PL

Funding

This research was funded by Researchers Supporting Project (number: RSP-2021/334), King Saud University, Riyadh, Saudi Arabia RSP-2021/334
6 · The paper itself

Abstract

The grape is a very well-liked fruit that is valued for its distinct flavor and several health benefits, including antioxidants, anthocyanins, soluble sugars, minerals, phenolics, flavonoids, organic acids, and vitamins, which significantly improve the product's overall quality. Today's supply chain as a whole needs quick and easy methods for evaluating fruit quality. Thus, the objective of this study was to estimate the quality attributes of Flame Seedless grape berries cultivated under various agronomical management and other practices using color space coordinates (berry L*, berry a*, and berry b*) as inputs in an artificial neural network (ANN) model with the best topology of (3-20-11). Satisfactory predictions based on the R

Indexed as

agricultural practiceschemical propertiesgrapemachine learningmodelingphysical propertiesquality

Identifiers

PMID36140893
PMCPMC9498147
OpenAlexW4296087425

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.