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World desk3 min

Can AI Explain Why Some White Wines Get Higher Ratings?

A study of 89 white wines found chemical markers associated with consumer-app scores, but it did not prove cause or show AI can predict personal taste.
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A 2025 study found associations between white wines’ chemical measurements and consumer-app ratings, but it does not show that a particular compound causes a high score—or that AI can tell any individual which wine they will enjoy. The analysis covered 89 wines, with Vivino ratings available for 64.

What the researchers analyzed

Researchers in Denmark and Germany combined chemical measurements with user-submitted scores from Vivino, a wine-rating app. Chemistry World reported the study on July 18, 2025, citing a paper by Hjertholm et al. in Food Chemistry (DOI: 10.1016/j.foodchem.2025.145376). The report describes a set of 89 white wines; 64 had Vivino scores.

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The researchers trained a machine-learning model using mass and infrared spectra. The measurements included information on 145 volatile organic compounds identified in the air above the wines, as well as ethanol concentration, density, sugars, and acids. The model examined how these chemical data related to the available consumer scores.

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Which chemical markers were associated with ratings?

In the reported sample, certain esters correlated with lower-rated wines. Some terpenoids, reducing sugars, and lactic acid appeared associated with higher consumer ratings. These are relationships observed in the data, not proof that adding or removing a compound would change a wine’s score.

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A rating reflects more than measured composition. The report cautions that chemical markers do not automatically determine a wine’s rating and that multiple variables may contribute to the relationship between composition and perceived quality. The findings therefore do not establish a preferred chemical recipe for white wine.

Can AI predict which wine tastes better?

Not on the evidence reported. This study is best understood as an exploratory analysis of chemical measurements and app scores in a limited set of wines. Chemistry World’s account does not provide the model architecture, validation design, rating distribution, or predictive-performance results. Without those details, it is not possible to judge how well the model might generalize to other wines or predict ratings reliably.

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Nor does a relationship with crowd-sourced scores establish what a particular person will like. Vivino ratings are user-submitted consumer scores; they are not the same kind of evidence as a controlled tasting panel or an individual preference test. The study does not demonstrate a universal wine-quality detector, a validated bottle recommender, or a replacement for tasting.

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How this differs from other wine-quality research

A separate 2024 paper, “Causal Artificial Intelligence Models of Food Quality Data,” analyzed Portuguese Vinho Verde samples assessed by professional tasters. Its dataset and evaluators differ from the 2025 white-wine study, which examined Vivino consumer ratings. Findings from those two studies should not be treated as one result or used interchangeably.

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More broadly, chemical measurements, consumer-app scores, expert judgments, and sensory-panel results answer different questions. Chemical analysis describes composition; ratings record judgments by particular groups under particular conditions. A claim that a model can predict quality requires evidence that it performs on appropriate data beyond simply identifying associations in the sample used to develop it.

What researchers proposed next

The researchers suggested that future versions could incorporate text analysis of user reviews, expert ratings, and sensory analysis. These were proposed additions, not capabilities shown by the reported model. Combining different forms of evidence could provide a fuller picture, but the available report does not establish that such a system has been built or validated.

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What the findings mean for wine drinkers

  • The reported associations offer clues about which measured compounds varied alongside Vivino ratings in this sample.
  • They do not show that any listed compound causes a higher or lower score.
  • They do not establish that the model can predict an individual’s preferences or reliably rank bottles outside the wines studied.

The primary paper’s model details and performance metrics are not established in the available account. The findings should be read as an early investigation into links between composition and consumer ratings, not as a practical guide to choosing a bottle.

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