ArticleFoods (Basel, Switzerland)2023
Assessment of Variability Sources in Grape Ripening Parameters by Using FTIR and Multivariate Modelling.
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.
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Who cites it
5 citing papers in PubMed.
- A Comprehensive Review on Minimally Destructive Quality and Safety Assessment of Agri-Food Products: Chemometrics-Coupled Mid-Infrared Spectroscopy.Foods (Basel, Switzerland) · 2025Review
- Incorporation of visible/near-infrared spectroscopy and machine learning models for indirect assessment of grape ripening indicators.Scientific reports · 2025Article
- Research of 2D-COS with metabolomics modifications through deep learning for traceability of wine.Scientific reports · 2024Article
- Exploring New Horizons for Wine Grapes: Modulating Functional Effects by Varying Harvest Timing and Solar Exposure.Foods (Basel, Switzerland) · 2024Article
- Non-destructive quantification of key quality characteristics in individual grapevine berries using near-infrared spectroscopy.Frontiers in plant science · 2024Article
Corrections and comments
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Authors and funding
9 authors.
Funding
Abstract
The variability in grape ripening is associated with the fact that each grape berry undergoes its own biochemical processes. Traditional viticulture manages this by averaging the physicochemical values of hundreds of grapes to make decisions. However, to obtain accurate results it is necessary to evaluate the different sources of variability, so exhaustive sampling is essential. In this article, the factors "grape maturity over time" and "position of the grape" (both in the grapevine and in the bunch/cluster) were considered and studied by analyzing the grapes with a portable ATR-FTIR instrument and evaluating the spectra obtained with ANOVA-simultaneous component analysis (ASCA). Ripeness over time was the main factor affecting the characteristics of the grapes. Position in the vine and in the bunch (in that order) were also significantly important, and their effect on the grapes evolves over time. In addition, it was also possible to predict basic oenological parameters (TSS and pH with errors of 0.3 °Brix and 0.7, respectively). Finally, a quality control chart was built based on the spectra obtained in the optimal state of ripening, which could be used to decide which grapes are suitable for harvest.
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