Evidence map›Paper›PMID 40210627›Full record

ArticleScientific reports2025

Incorporation of visible/near-infrared spectroscopy and machine learning models for indirect assessment of grape ripening indicators.

Osama Elsherbiny, Salah El-Hendawy, Salah Elsayed, Abdallah Elshawadfy Elwakeel, Abdullah Alebidi, Xianlu Yue, Wael Mohamed Elmessery, Hoda Galal

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. 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

8 authors.

Osama ElsherbinyAgricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt. osama_algazeery@mans.edu.eg.
Salah El-HendawyDepartment of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi Arabia.
Salah ElsayedEvaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menoufia, 32897, Egypt.
Abdallah Elshawadfy ElwakeelAgricultural Engineering Department, Faculty of Agriculture and Natural Resources, Aswan University, Aswan, 81528, Egypt.
Abdullah AlebidiDepartment of Plant Production, College of Food and Agriculture Sciences, King Saud University, P.O. Box 2460, 11451, Riyadh, Saudi Arabia.
Xianlu YueInstitute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Zhongguancun South Road 12, Haidian District, Beijing, 100081, China.
Wael Mohamed ElmesseryAgricultural Engineering Department, Faculty of Agriculture, Kafrelsheikh University, Kafrelsheikh, 33516, Egypt.
Hoda GalalEvaluation of Natural Resources Department, Environmental Studies and Research Institute, University of Sadat City, Menoufia, 32897, Egypt.

Funding

Researchers Supporting Project number (RSPD2024R730), King Saud University, Riyadh, Saudi Arabia. RSPD2024R730
6 · The paper itself

Abstract

The assessment of grape ripeness is pivotal for optimizing harvest timing and ensuring high-quality fruit production. Traditional methods, relying on manual sampling and chemical analysis, are laborious and expensive. This study proposes an innovative approach combining Visible/Near-Infrared (VIS/NIR) spectroscopy with machine learning techniques-specifically, decision trees (DT) and gradient boosting regression (GBR)-to facilitate a rapid, non-destructive, and cost-effective prediction of key grape ripening indicators such as anthocyanin (An), total acidity (TA), total soluble solids (TSS), and the TSS/TA ratio. The performance of spectral reflectance indices (SRIs) in correlating with these ripening metrics across different grape ripening stages was examined. The study findings revealed notable variations in ripening indicators across stages, with the newly developed SRIs outperforming the existing indices. The application of dual and triple-band SRIs yielded strong correlations with An (R

Indexed as

ChemometricsDecision treeFruit qualityGradient boosting regressionPrecision agriculture

Identifiers

PMID40210627
PMCPMC11986167

What OpenQuestion holds

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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.