Key Points
- Researchers from the University of Copenhagen used gas chromatography-mass spectrometry (GC-MS) along with Fourier transform infrared (FT-IR) spectroscopy to analyze 89 German white wine samples. By applying machine learning algorithms to these instrumental data, they demonstrated that it is possible to predict Vivino user ratings, offering a data-driven method to estimate perceived wine quality.
- ·Vivino's crowd-sourced ratings exhibit central tendency bias but collectively reflect a reliable consensus on wine quality. Studies show that Vivino users often evaluate wines similarly to professionals and can even reveal regional or weather-based patterns in quality perception, underscoring the platform’s value in consumer research.
- This study not only highlights the predictive power of instrumental techniques in assessing consumer preferences but also suggests a roadmap for integrating user reviews, sensory analysis, and expert input. This multidisciplinary approach could evolve into a comprehensive model for evaluating and marketing wine based on its chemical and sensory profile.
Researchers at the University of Copenhagen (Denmark) aimed to conduct a feasibility study to predict Vivino quality ratings, an online platform designed to allow users to rate and describe various wines. The research team used a combination of instrumental platforms and machine learning (ML) algorithms in their analysis. The volatile organic compound (VOC) compositions of 89 commercial German white wine samples were obtained by dynamic headspace extraction (DHE) gas chromatography-mass spectrometry (GC–MS), while, simultaneously, 18 physicochemical parameters were determined using Fourier transform-infrared (FT-IR) spectroscopy. A paper based on their work was published in Food Chemistry (1).
Designed for wine enthusiasts, Vivino allows its users to rate and describe wines on a scale of 1–5, providing an average user rating for each wine surveyed (2). A quality regarding this rating system is the uncommon use of extreme ratings, as users tend to rate the wines near the median, which results in a normal distribution of the scores when the sample size is sufficiently large. This tendency, often referred to as central tendency bias, is well documented in various domains which utilize rating or measurement systems (3). Vivino, on the other hand, leverages more of a “wisdom of the crowd” concept, suggesting that the average of multiple independent estimates is generally more accurate than any single estimate (4,5).
This crowd-sourced data provides a unique viewpoint concerning wine quality. Through the platform, visitors can discover wines new to them, access opinions from fellow enthusiasts, and contribute to a growing database of wine knowledge, thus making wine selection more accessible and informed for everyone (1).
The combination of GC–MS and FT–IR produces information on the wine’s volatile profile, which is crucial for aroma attributes, and the chemical constituents, which are vital for its taste, according to the researchers. These methods have been previously used in combination with chemometrics and ML methods for the identification of key sensory and chemical attributes which drive consumer liking (8); the modelling of quality traits and the making of predictions regarding sensory profiles of Australian Pinot Noir (9); the investigation of Cabernet Sauvignon grape parameters which predict the sensory properties of the finished wine (10); and the characterization of Danish Solaris white wines (11).
Vivino ratings have been used in several studies to investigate consumer perception of wine. For example, one investigation found that Vivino users demonstrate rich knowledge of wine similar to experts, without being heavily influenced by price (6); a second study analyzing regional wine ratings which suggested a community effect on perceived quality, referred to a particular wines popularity within the Vivino community (7); a third found a correlation between Vivino ratings and professional critics, and reported that both respond to weather changes (5).
