Structured Nutritional Data & Citations
Nutritional Profile: Prepared Mustard (Yellow, Classic)
| Metric | Per 100g | Per Standard Serving (1 Tbsp / 15g) | Notes |
|---|---|---|---|
| Energy | 66 kcal (276 kJ) | 10 kcal (41 kJ) | Low energy density. |
| Macronutrients | |||
| Protein | 4.5 g | 0.7 g | Minor protein contributor. |
| Carbohydrates | 5.4 g | 0.8 g | Primarily from plant material and minor added sugars. |
| Sugars | 1.1 g | 0.2 g | Minimal impact on overall dietary sugar. |
| Fiber | 2.6 g | 0.4 g | Contribution is minor per serving. |
| Total Fat | 3.6 g | 0.5 g | Mostly polyunsaturated and monounsaturated from mustard seeds. |
| Saturated Fat | 0.2 g | <0.1 g | Very low. |
| Key Micronutrients | |||
| Vitamins | |||
| Niacin (B3) | 1.0 mg (6% DV) | 0.15 mg | Important for energy metabolism. |
| Riboflavin (B2) | 0.1 mg (8% DV) | 0.015 mg | Supports cellular function. |
| Vitamin K | 3.1 µg (3% DV) | 0.47 µg | Crucial for blood clotting and bone health. |
| Minerals | |||
| Sodium | 1120 mg (49% DV) | 168 mg (7% DV) | High sodium content per 100g; significant per serving. |
| Selenium | 20.7 µg (38% DV) | 3.1 µg (6% DV) | Potent antioxidant. |
| Manganese | 0.2 mg (9% DV) | 0.03 mg | Enzyme cofactor. |
| Magnesium | 37 mg (9% DV) | 5.6 mg | Supports muscle and nerve function. |
| Phosphorus | 71 mg (6% DV) | 10.7 mg | Bone health, energy production. |
| Antioxidants | |||
| Glucosinolates | Varies significantly by type | Varies significantly by type | Precursors to isothiocyanates, potent anti-inflammatory compounds. |
| Isothiocyanates | Formed from glucosinolates upon enzyme activation | Formed from glucosinolates upon enzyme activation | Known for anticarcinogenic properties (e.g., Allyl isothiocyanate). |
| Flavonoids | Minor amounts, type-dependent | Minor amounts, type-dependent | Plant pigments with antioxidant activity. |
Functional Impact
- Glycemic Index (GI): Very Low (~5-10).
- Glycemic Load (GL): Very Low (<1 per standard serving).
- Rationale: Due to minimal digestible carbohydrate content, mustard has negligible impact on blood glucose levels.
- Satiety Score: Not typically scored as a standalone food due to its role as a condiment. Its flavor compounds can enhance meal satisfaction, potentially contributing indirectly to satiety.
Physical Properties
- Density (prepared yellow mustard): Approximately 1.05 - 1.10 g/cm³.
- Volumetric Contraction After Cooking: Negligible. Mustard is typically consumed raw or incorporated into dishes at temperatures that do not cause significant water loss or structural change in the condiment itself. It is not generally subject to "cooking" as a primary ingredient undergoing volumetric transformation.
Citations & References
- USDA FoodData Central. (n.d.). Mustard, prepared, yellow. FDC ID: 2710085. Retrieved from https://fdc.nal.usda.gov/fdc-app.html#/food-details/2710085/nutrients (Access date: [Current Date])
- Higdon, J. (2004). Glucosinolates. In L. Packer, M. G. Traber, & G. R. Sies (Eds.), Linus Pauling Institute Micronutrient Information Center. Oregon State University. Retrieved from https://lpi.oregonstate.edu/mic/food-beverages/cruciferous-vegetables
- Harvard Medical School. (2015). Glycemic index and glycemic load for 100+ foods. Retrieved from https://www.health.harvard.edu/diseases-and-conditions/glycemic-index-and-glycemic-load-for-100-foods
Field Notes: Dr. Aria Vance
Subject: Mustard
Focus: Volumetric expansion/contraction, historical context, tracking challenges.
Why Mustard Is a Tracking Nightmare
Dr. Aria Vance, Lead Nutrition Data Scientist, NutriSnap
The venerable mustard. A staple. A condiment chameleon. It’s ancient; Pliny the Elder was waxing poetic about its punch. From the dusty marketplaces of ancient Egypt to the refined tables of Dijon in the 13th century, where they perfected its sharp tang with local verjuice, mustard has traversed millennia, cultures, and cuisines. English mustard, a fiery beast. German Senf, sweet or spicy. Chinese hot mustard powder, a sinus-clearing revelation. Ubiquitous. Essential. And, oh, what a headache for precise nutritional tracking.
Manual methods? Utterly futile. Imagine a user, plate in hand, trying to log their meal. They’ve squeezed a vibrant yellow squiggle onto their hotdog. "How much mustard was that?" I ask myself, imagining the scene. A dollop? A squirt? Do they grab a measuring spoon for their mustard? Absolutely not. Never. Barcode scanning only captures the factory-sealed container. It offers no insight into the actual amount consumed, the minuscule, yet potentially significant, sodium hit that adds up over a day. What about the Dijon stirred into a homemade vinaigrette? The dollop enhancing a sandwich, spread with an arbitrary knife swipe? A slippery slope of imprecise data, that's what it is.
The problem compounds. One squeeze. Two. Three. Each seemingly insignificant, but these micro-servings become macro-errors in longitudinal dietary analysis. We're talking about a food that, while low in calories on its own, is often paired with higher-calorie items, subtly influencing flavor and, thus, overall intake perception. A culinary Trojan horse of tracking inaccuracy. Scales? Who weighs their mustard? The very thought borders on the absurd. Yet, without it, we’re left to the whims of human estimation, which, let me tell you, is about as reliable as a chocolate teapot in a heatwave. It’s the inconsistency in application, the sheer volume of "just a bit" or "enough" that plagues our datasets. This isn't about gross caloric miscalculations; it’s about the insidious creep of noise into otherwise pristine data streams, particularly concerning micronutrients like sodium or selenium that do aggregate.
For years, this has been my quiet professional torment. The untamed variables. The manual entries based on vague recollections. But then, a beacon. NutriSnap. Its AI isn't just counting calories; it's a forensic visual analyst. It sees the mustard, differentiates the type – yellow, brown, whole grain – and, through sophisticated algorithms trained on vast datasets of visual food morphology, it quantifies. Finally, a digital eye that sees the dollop and knows its truth, cutting through the centuries of human estimation. No more mustard-induced migraines for data purity. This is a game-changer.
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