NUTRITIONAL LOG

The Truth About Wine

A Deep-Research Journal

Dr. Aria Vance
Dr. Aria Vance Lead Nutrition Data Scientist
Last Reviewed: Jun 3, 2026 • Data Sources: USDA FoodData Central, NutriSnap Volumetric Models

Structured Nutritional Data & Citations

Food Profile: Wine (Red Table Wine, Dry)

Nutritional Composition

Nutrient Per 100g Per Standard Serving (147g / 5 fl oz) Notes
Calories 85 kcal 125 kcal Primarily from ethanol and residual sugars. Varies significantly with alcohol content (ABV) and sweetness (residual sugar).
Macros
Protein 0.1 g 0.15 g Minimal.
Carbohydrates 2.6 g 3.8 g Composed mainly of residual sugars (glucose, fructose). Dry wines have <4 g/L, while sweeter wines can have much more. Alcohol is metabolically distinct from carbohydrates, contributing its own caloric load (7 kcal/g).
Fat 0 g 0 g Negligible.
Alcohol (Ethanol) 10.6 g (approx 13.5% ABV) 15.6 g (approx 13.5% ABV) Percentage by volume (ABV) is critical for caloric calculation. 1g of ethanol = ~7 kcal.

Key Micronutrients & Bioactive Compounds

Functional Impact

Physical Properties

Citations & References

Field Notes: Dr. Aria Vance

Subject: Wine
Focus: Volumetric expansion/contraction, historical context, tracking challenges.

Why Wine Is Difficult to Track

Another week, another deep dive into the elusive liquid diet. Wine. Ah, the nectar of the gods, the solvent of inhibitions, the bane of precise calorie tracking. For centuries, it's been more than just a drink; it's culture, ritual, medicine, status. From ancient Mesopotamian tablets detailing wine production to the Roman bacchanals, its allure is undeniable. But as a nutritional data scientist? A shimmering, ruby-red deceiver.

Trying to manually log wine consumption is a fool's errand. Seriously, it's a quantum physics problem in a stem glass. How many milliliters did you actually pour? Was that a four-ounce pour, as recommended? Or did the host, in a moment of convivial exuberance, fill it to a generous six? Perhaps seven? Eyeballing liquid volume in oddly shaped glassware is notoriously inaccurate. It’s not like weighing out a perfectly diced avocado. This fluid medium, this ghost in the machine of your dietary goals, slides down, leaving metabolic traces but zero certainty in your logbook.

The context doesn't help. Wine isn't typically consumed from a perfectly measured container. Restaurants have varied "standard" pours, often subject to the server's discretion or the establishment's particular glassware. At home? Forget it. We pour with feeling, with intuition, with "just enough." A half-glass. A top-up. These seemingly innocuous gestures add up. Exponentially. And a barcode scan? On a bottle of 2018 Barolo, after it's been uncorked and half-consumed? Impractical. Tedious. Laughable, even. The specific vintage, the alcohol by volume, the residual sugar—all crucial factors for accurate data—are often ignored in the hurried reality of social consumption. People simply guess. And guesses, my dear colleagues, are the enemy of data.

This constant imprecision, this silent sabotage of dietary adherence, drove me to distraction. It's a gaping hole in every manual food diary I've ever reviewed. The behavioral aspect is fascinating: the more enjoyable the food or drink, the less likely we are to accurately quantify it. A cognitive bias. A liquid paradox. But then, NutriSnap. The AI photo tracker. It was a revelation. Snap a photo. The AI, with its forensic visual analysis, sees the pour. Measures it against the glass, analyses the meniscus. No more guessing. No more cognitive bias. Finally, true clarity for the liquid enigma.

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