Structured Nutritional Data & Citations
Halibut (Hippoglossus spp.) - Nutritional Profile & Physical Characteristics
This profile focuses on common lean white fish varieties, primarily Pacific Halibut (Hippoglossus stenolepis) and Atlantic Halibut (Hippoglossus hippoglossus), based on USDA FoodData Central data for raw, edible portions.
| Metric | Per 100g (Raw) | Per Standard Serving (113g / 4 oz, Raw) | Notes |
|---|---|---|---|
| Calories (kcal) | 111 | 126 | Lean protein source. |
| Macronutrients | |||
| Protein (g) | 22.5 | 25.4 | High-quality, complete protein. |
| Total Fat (g) | 2.3 | 2.6 | Low in fat. |
| - Saturated Fat (g) | 0.4 | 0.5 | Minimal saturated fat. |
| - Monounsaturated Fat (g) | 0.4 | 0.5 | Healthy fats. |
| - Polyunsaturated Fat (g) | 0.8 | 0.9 | Includes Omega-3s. |
| - Omega-3s (EPA+DHA, mg) | ~450 (varies by species/origin) | ~508 (varies by species/origin) | Important for cardiovascular and cognitive health. |
| Total Carbohydrates (g) | 0.0 | 0.0 | Glycemic index of zero. |
| Fiber (g) | 0.0 | 0.0 | |
| Key Micronutrients | % Daily Value (DV) based on 113g serving for adults. | ||
| Vitamins | |||
| Niacin (B3) | 4.6 mg (29% DV) | 5.2 mg (32% DV) | Critical for energy metabolism. |
| Vitamin B6 | 0.5 mg (29% DV) | 0.6 mg (35% DV) | Important for protein metabolism and nervous system function. |
| Vitamin B12 | 1.1 µg (46% DV) | 1.2 µg (50% DV) | Essential for red blood cell formation and neurological health. |
| Vitamin D | 4.6 µg (23% DV) | 5.2 µg (26% DV) | Crucial for bone health and immune function. |
| Vitamin E | 0.8 mg (5% DV) | 0.9 mg (6% DV) | Antioxidant. |
| Minerals | |||
| Selenium | 47.5 µg (86% DV) | 53.7 µg (98% DV) | Potent antioxidant, thyroid support. |
| Phosphorus | 250 mg (20% DV) | 283 mg (23% DV) | Bone health, energy production. |
| Magnesium | 30 mg (7% DV) | 34 mg (8% DV) | Muscle and nerve function. |
| Potassium | 500 mg (11% DV) | 565 mg (12% DV) | Electrolyte balance, blood pressure regulation. |
| Antioxidants | Selenium, Omega-3 fatty acids (anti-inflammatory) | Support cellular health and reduce oxidative stress/inflammation. |
Functional Impact:
- Glycemic Index (GI): 0 (negligible due to lack of carbohydrates).
- Glycemic Load (GL): 0.
- Satiety Score: High. The high protein content significantly contributes to satiety, helping to reduce overall caloric intake. Fish generally ranks well on satiety indices.
Physical Properties:
- Density (Raw flesh): Approximately 1.05 - 1.07 g/cm³.
- Volumetric Contraction after Cooking: Typically ranges from 15% to 25%, primarily due to moisture loss and protein denaturation. This can vary based on cooking method (e.g., baking vs. grilling) and initial fat content.
Citations & References:
- USDA FoodData Central. National Nutrient Database for Standard Reference, Legacy Release (SR28) and FoodData Central, FDC ID: 173663 (Fish, halibut, Pacific, raw). U.S. Department of Agriculture. Accessed [Insert Current Date, e.g., November 1, 2023].
- National Institutes of Health (NIH) Office of Dietary Supplements. Fact Sheets for Health Professionals (various micronutrients). U.S. Department of Health & Human Services. Accessed [Insert Current Date].
- Food and Agriculture Organization of the United Nations (FAO). Various publications on fisheries and aquaculture nutrition.
Field Notes: Dr. Aria Vance
Subject: Halibut
Focus: Volumetric expansion/contraction, historical context, tracking challenges.
Why Halibut Is Difficult to Track
Journal Entry - Dr. Aria Vance
Date: November 1, 2023 Subject: Halibut's Elusiveness in Accurate Nutritional Tracking
A leviathan of the deep, a true "holy fish"—that's halibut. Its very name, "heilbot," harks back to medieval feasts, revered for its flaky, white flesh. Indigenous coastal communities across the Pacific Northwest held it in such high regard, not just as sustenance but as a spiritual anchor, its vast size a symbol of nature's bounty. We're talking serious history, serious cultural heft. But tracking its actual nutritional impact? That's where the reverence meets the messy reality of data collection, a real head-scratcher for anyone not wielding a forensic toolkit.
I've spent years staring at food—analyzing it, dissecting it, trying to pin down its essence into neat little caloric boxes. And halibut? It just laughs at our neat little boxes. Imagine trying to log a piece of halibut for dinner tonight. You’ve got a beautiful, thick fillet. But is it 100 grams? 150? Who actually pulls out a kitchen scale for every single meal? Almost no one. It's often an irregular cut, wider at one end, tapering at another, sometimes with a skin-on segment, sometimes off. Visual estimation is a guessing game, a squishy science at best. My own family, bless their hearts, just eyeball it. "Oh, that looks like a normal portion," they say, completely oblivious to the +/- 50-calorie swing they’ve just introduced to their daily log. It's infuriating.
Then, the cooking method. This is where it gets truly wild. A halibut steak, baked simply with lemon and herbs, is one thing. Minimal fat added, perhaps a touch of olive oil brushing. But pan-fried? Oh, that’s another beast entirely. The fish acts like a sponge, soaking up that butter or oil—we're talking significant caloric absorption. Grilled? Some fat drips off, yes, but how much? And the sauces! A creamy dill sauce, a rich hollandaise, even a seemingly innocuous glaze can secretly pile on hundreds of calories. Nobody is meticulously scraping off every bit of sauce, weighing it, and then subtracting it from the "raw ingredient" entry. It’s an exercise in futility, a data scientist's nightmare.
The very act of cooking changes its physical properties, too. That density, that volumetric contraction we meticulously logged in our reference tables? It means the raw weight is fundamentally different from what ends up on your plate. You'd have to weigh it raw, then again cooked, account for every drop of oil, every grain of seasoning. It's simply not feasible for daily tracking. It’s tedious, it’s frustrating, and frankly, it's why most manual tracking efforts ultimately fail. People give up because it’s too much work, too much guesswork, too much "close enough" that isn't close enough for meaningful insight.
This is precisely why I championed the development of NutriSnap. Because the human eye, augmented by advanced AI, can estimate that irregular portion. It can detect the sheen of oil, gauge the thickness of a sauce layer, identify the cooking method's visual cues. It’s forensic visual analysis, giving us the power to cut through the subjective haze of manual tracking and bring a much-needed precision to something as magnificent, yet deceptively complex, as a piece of halibut on a dinner plate. Finally, we're getting somewhere.
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