ArticleLife (Basel, Switzerland)2024
Rapid Detection of Tannin Content in Wine Grapes Using Hyperspectral Technology.
Article in Life (Basel, Switzerland), 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 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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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.
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Who cites it
3 citing papers in PubMed, 12 citations in OpenAlex.
- FTIR-ATR Spectroscopy and Chemometrics for Varietal Screening of PDO Douro Monovarietal Wines: An Exploratory Feasibility Study.Molecules (Basel, Switzerland) · 2026Article
- Using visible and NIR hyperspectral imaging and machine learning for nondestructive detection of nutrient contents in sorghum.Scientific reports · 2025Article
- Chemical composition's effect onOpen life sciences · 2024Article
Corrections and comments
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Authors and funding
6 authors at 2 institutions in 1 country.
Funding
Abstract
Wine grape quality is influenced by the variety and growing environment, and the quality of the grapes has a significant impact on the quality of the wine. Tannins are a crucial indicator of wine grape quality, and, therefore, rapid and non-destructive methods for detecting tannin content are necessary. This study collected spectral data of Pinot Noir and Chardonnay using a geophysical spectrometer, with a focus on the 500-1800 nm spectrum. The spectra were preprocessed using Savitzky-Golay (SG), first-order differential (1D), standard normal transform (SNV), and their respective combinations. Characteristic bands were extracted through correlation analysis (PCC). Models such as partial least squares (PLS), support vector machine (SVM), random forest (RF), and one-dimensional neural network (1DCNN) were used to model tannin content. The study found that preprocessing the raw spectra improved the models' predictive capacity. The SVM-RF model was the most effective in predicting grape tannin content, with a test set R
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Registered trials
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.