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How Science is Changing the Way We Understand Wine Perception

This article is a summary of a longer story published on Unite.AI called: How Tastry “Taught a Computer How to Taste.” To read the full story, click here.

 

When we embarked on our research journey six years ago, we had a simple yet ambitious hypothesis: the key to understanding how people perceive wine—its taste, aroma, texture, and color—lies in the realm of chemistry. What was missing was a more comprehensive method of analysis that could unlock the secrets hidden within the chemical makeup of wine.

Wine chemistry, as a science, often employs a quantification mindset—measuring how much of a particular compound exists in a mixture—to guide product development decisions in winemaking. However, it falls very short of providing any level of guidance as to how humans perceive flavor and the degree to which consumers will find a wine enjoyable.

Wine, to the average consumer palate, is not a collection of isolated compounds, but rather a complex, dynamic interplay of hundreds of chemicals, creating what can be described as a “chemical soup”.

The Current Approach and Its Limitations

The current standard approach in sensory science and chemistry involves quantifying specific compounds in wine and then conducting sensory panels with experts to gauge if certain concentrations of these compounds elicit specific perceptions, such as “cherry” flavor.

However, this method encounters three fundamental problems:

  1. Inadequate Predictability: Predicting flavor solely based on the quantification of a single compound is unreliable. The perception of a compound like benzaldehyde as “cherry” can vary significantly between different wines due to the presence of numerous other compounds that can mask or enhance its effects. Humans do not experience each compound in isolation; instead, they sense all compounds simultaneously.
  2. Subjectivity of Perception: Flavor perception is highly subjective. Even expert panels can produce divergent descriptions, with one group identifying a taste as “apple” and another as “pear.” The average consumer introduces even more unpredictability, as personal biology and cultural experiences influence their flavor descriptions. For instance, what Americans perceive as “cherry” Europeans might describe as “marzipan” when tasting the same wine.
  3. Lack of Correlation with Preference: Surprisingly, our research indicates that the flavors perceived in a glass of wine do not necessarily correlate with whether consumers like it or not. Ultimately, consumers make purchasing decisions based on their overall experience and whether they find the wine enjoyable, not simply because it has a particular flavor note.

The Wider Implications

The challenges faced in understanding consumer preferences are not unique to the wine industry. We’ve encountered similar dilemmas in other sectors, such as flavor and fragrance companies struggling to create products that resonate with their target audiences.

Tastry’s Innovative Approach

To address these challenges, our research has taken a different path. Rather than attempting to predict what flavors consumers will perceive, we focus on predicting the flavor matrix that consumers will prefer.

Our journey involved years of investment, culminating in the development of a chemistry method that captures the volatile and nonvolatile compounds, dissolved solids, and spectral data of a wine in a single snapshot. Each wine sample analyzed yields over a million data points, which we process using machine learning algorithms to decode the intricate relationships among these analytes.

Creating a Comprehensive Flavor Matrix

Once we validated the efficacy of this method, we expanded our scope to analyze thousands of wines worldwide, leading to the creation of a comprehensive flavor matrix database.

Relating Consumer Preferences to Chemistry

 

The next crucial step was to relate consumer preferences to the chemistry of the wines. We conducted double-blind tasting panels with diverse groups, including newcomers to wine, regular wine drinkers, experts, winemakers, and sommeliers. An intriguing aspect of our research is that we pay equal attention to the extent to which consumers dislike a wine, recognizing that negative preferences are as critical as positive ones.

By employing machine learning, we’ve gained insights into consumers’ unique preferences for various flavor matrices in wine. This groundbreaking work allows us to predict individually and in aggregate their preferences for wines they haven’t tasted, a significant achievement. However, it’s essential to acknowledge that consumer preference is profoundly individual, akin to a fingerprint.

Our journey over the past six years has fundamentally changed the use of wine chemistry in winemaking. We’re now able to look beyond the quantification of individual compounds for quality assurance, and Tastry’s breakthroughs are employed by leading winemaking teams to improve confidence and certainty their efforts align the preferences of their target consumers.

Curious to learn why 7 out of the top 10 wineries work with Tastry? Send a quick note to hello@tastry.com.

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