ArticleFoods (Basel, Switzerland)2023
Quality Assessment and Ripeness Prediction of Table Grapes Using Visible-Near-Infrared Spectroscopy.
Article in Foods (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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Who cites it
5 citing papers in PubMed.
- Incorporation of visible/near-infrared spectroscopy and machine learning models for indirect assessment of grape ripening indicators.Scientific reports · 2025Article
- Hyperspectral technology and machine learning models to estimate the fruit quality parameters of mango and strawberry crops.PloS one · 2025Article
- Nondestructive Determination of Epicarp Hardness of Passion Fruit Using Near-Infrared Spectroscopy during Storage.Foods (Basel, Switzerland) · 2024Article
- Discrimination of Maturity Stages of Cabernet Sauvignon Wine Grapes Using Visible-Near-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2023Article
- Rapid Determination of Wine Grape Maturity Level from pH, Titratable Acidity, and Sugar Content Using Non-Destructive In Situ Infrared Spectroscopy and Multi-Head Attention Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Ripeness significantly affects the commercial values and sales of fruits. In order to monitor the change of grapes' quality parameters during ripening, a rapid and nondestructive method of visible-near-infrared spectral (Vis-NIR) technology was utilized in this study. Firstly, the physicochemical properties of grapes at four different ripening stages were explored. Data evidenced increasing color in redness/greenness (
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Registered trials
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