Structured Nutritional Data & Citations
Noodle: Comprehensive Nutritional Profile & Physical Properties
This entry details the nutritional composition and physical characteristics of a representative durum wheat semolina noodle, cooked and unenriched, without additional ingredients unless specified. Data reflects a generalized profile; specific products may vary based on fortification, processing, and ingredients.
1. Nutritional & Functional Data
1.1. Macroscopic Nutritional Profile
| Nutrient Category | Per 100g (Cooked, Drained) | Per Standard Serving (140g Cooked) | Reference |
|---|---|---|---|
| Energy | 158 kcal | 221 kcal | USDA FDC #170942 |
| Protein | 5.8g | 8.1g | USDA FDC #170942 |
| Carbohydrates | 30.6g | 42.8g | USDA FDC #170942 |
| Sugars | 0.9g | 1.3g | USDA FDC #170942 |
| Fiber | 1.8g | 2.5g | USDA FDC #170942 |
| Fat (Total) | 0.9g | 1.3g | USDA FDC #170942 |
| Saturated Fat | 0.2g | 0.3g | USDA FDC #170942 |
| Monounsat. Fat | 0.1g | 0.1g | USDA FDC #170942 |
| Polyunsat. Fat | 0.4g | 0.6g | USDA FDC #170942 |
1.2. Key Micronutrients (Per 100g Cooked, Drained)
Vitamins:
- B-Vitamins: Thiamine (B1) - 0.12 mg (10% DV), Niacin (B3) - 1.4 mg (9% DV), Folate (B9) - 11 µg (3% DV). Note: Enriched varieties will show significantly higher levels, particularly for B1, B3, B2, and B9.
- Other: Trace amounts of Vitamin K and Vitamin E.
Minerals:
- Manganese: 0.24 mg (10% DV)
- Selenium: 26 µg (47% DV)
- Phosphorus: 83 mg (7% DV)
- Iron: 0.6 mg (3% DV) Note: Enriched varieties will show higher levels.
- Magnesium: 25 mg (6% DV)
- Zinc: 0.5 mg (5% DV)
Antioxidants:
- Carotenoids: Primarily lutein and zeaxanthin from durum wheat endosperm.
- Phenolic Compounds: Minor amounts, primarily ferulic acid. Levels are higher in whole wheat varieties.
1.3. Functional Impact
- Glycemic Index (GI): 45-60 (Medium). Varies significantly with cooking time (al dente often lower GI) and specific wheat variety. Reference: University of Sydney GI Database.
- Glycemic Load (GL) per Standard Serving (140g): ~21 (Medium). Calculated as (GI * Carbohydrate content in grams) / 100.
- Satiety Score (Normalized to White Bread = 100%): Approximately 119%. Provides moderate satiety due to complex carbohydrates and structure. Whole wheat varieties offer enhanced satiety due to higher fiber content. Reference: Holt et al., 1995 (European Journal of Clinical Nutrition).
1.4. Physical Properties
- Density (Dry, Uncooked Durum Semolina Noodle): Approximately 1.38 - 1.42 g/cm³. (Based on common grain densities).
- Density (Cooked, Drained Spaghetti Noodle): Approximately 1.05 - 1.15 g/cm³. The density decreases as water is absorbed, making the cooked noodle less dense than its dry counterpart.
- Volumetric Change During Cooking: Significant expansion. Dry noodles typically increase 2.5 to 3.5 times in volume and weight upon cooking due to water absorption. For instance, 50g dry spaghetti yields approximately 150g-180g cooked.
- Volumetric Contraction After Cooking: Negligible. While some minor surface moisture loss can occur upon cooling, leading to slight hardening, there is no significant volumetric contraction in the material structure itself within typical consumption timeframes. The absorbed water remains largely bound.
1.5. Citations & References
- USDA FoodData Central (FDC):
- ID 170942: "Pasta, spaghetti, enriched, cooked, without added salt" (Represents a general, common profile for durum wheat pasta).
- University of Sydney Glycemic Index Database: World's leading GI database for food products.
- Holt, S. H., Brand Miller, J. C., Petocz, P., & Farmakalidis, E. (1995). "A satiety index of common foods." European Journal of Clinical Nutrition, 49(9), 675-690. (For Satiety Score methodology).
Field Notes: Dr. Aria Vance
Subject: Noodle
Focus: Volumetric expansion/contraction, historical context, tracking challenges.
Why Noodle Is Difficult to Track
Dr. Aria Vance, Lead Nutrition Data Scientist at NutriSnap.
Noodles. Oh, the humble, glorious noodle. A culinary chameleon, a global unifier, a dietary enigma. From the ancient wheat strands unearthed in Lajia, China, dating back 4,000 years, to Italy's Roman laganum, to the myriad forms across Asia – ramen, pho, udon, vermicelli – it's a food deeply woven into the fabric of humanity. An artifact of agricultural innovation, really. But for us, for NutriSnap, it's a daily, infuriating, captivating challenge.
Tracking "noodle" for nutritional purposes? Forget it. Absolutely maddening. You might as well try to count grains of sand in a desert. Think about it. Is it semolina pasta? Whole wheat? Rice noodle? Glass noodle? Buckwheat soba? Each, a universe unto itself. Macronutrient profiles swing wildly; fiber content becomes a chasm, not just a gap.
Then there's the preparation. Al dente spaghetti, firm and resisting, holds less water, has a slightly different starch structure. Mushy, overcooked spaghetti? A waterlogged mess, its starches more gelatinized, potentially affecting absorption rates. And that's just the basic cooking. Now, add sauce. Broth. Oil from a stir-fry. Bake it in a casserole. The "noodle" itself is rarely, truly plain. It's a vehicle. A delicious, slippery vehicle.
Portioning? Don't even get me started. "A cup of noodles." Is it dry? Cooked? Packed tightly? Loosely piled? The visual deception is profound. A mound of cooked spaghetti on a plate might look like a single serving, but that innocent twirl, the volumetric illusion of its intertwining strands, can hide twice the caloric load you expect. No one, and I mean no one, is pulling out a kitchen scale for their bowl of pho, meticulously weighing the soaked rice vermicelli after fishing it out of the broth. It's a behavioral black hole for accurate data. A human being simply cannot do this with any consistent accuracy without going utterly insane.
This is precisely why manual tracking fails for complex, shape-shifting foods like noodles. The human eye and imprecise household tools are simply not up to the forensic task required for true dietary insight. It's a losing battle. And that, my friends, is where NutriSnap strides in, a beacon in the carb-laden darkness. Our AI, built upon millions of visual data points, doesn't just see a "noodle." It understands the geometry of the twirl, the sheen of the oil, the density implied by its form factor. It discerns the type, estimates volume with uncanny precision, and slices through the visual ambiguity that baffles the human brain and renders traditional tracking useless. It's not just counting; it's understanding. Finally, accurate data, from a photo. We're solving the noodle problem. We are.
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