Structured Nutritional Data & Citations
Canola Oil (Refined) - Nutritional and Physical Profile
1. Calorific and Macronutrient Composition:
| Nutrient | Per 100g | Per Standard Serving (1 tbsp / 15ml / ~13.8g) |
|---|---|---|
| Energy | 884 kcal (3699 kJ) | 122 kcal (510 kJ) |
| Protein | 0.0g | 0.0g |
| Total Fat | 100.0g | 13.8g |
| Saturated | 7.4g | 1.0g |
| Monounsaturated | 63.3g | 8.7g |
| Polyunsaturated | 28.1g | 3.9g |
| Omega-3 (ALA) | 9.1g | 1.3g |
| Omega-6 (LA) | 19.0g | 2.6g |
| Carbohydrates | 0.0g | 0.0g |
| Sugars | 0.0g | 0.0g |
| Fiber | 0.0g | 0.0g |
Reference: USDA FoodData Central, Food ID: 172049, "Oil, canola" (USDA, 2023a).
2. Key Micronutrients (per 100g):
- Vitamins:
- Vitamin E (alpha-tocopherol): 17.5 mg (117% DV)
- Vitamin K (phylloquinone): 71.3 µg (59% DV)
- Minerals:
- Trace amounts of Iron, Zinc, Copper (negligible contribution to daily intake).
- Antioxidants & Phytochemicals:
- Phytosterols: ~550-1100 mg (primarily β-sitosterol, campesterol, brassicasterol) (USDA, 2023b; Health Canada, 2014).
- Lutein and Zeaxanthin: Trace amounts.
Reference: USDA FoodData Central, Food ID: 172049, "Oil, canola" (USDA, 2023a).
3. Functional Impact:
- Glycemic Index (GI): 0 (As a pure fat, canola oil does not contain carbohydrates and thus has no direct impact on blood glucose levels).
- Glycemic Load (GL): 0.
- Satiety Score: High (Due to its high fat content, canola oil contributes to significant satiety through delayed gastric emptying and stimulation of satiety hormones, though specific numerical scores vary by methodology).
- Consensus: Fat generally contributes to higher satiety per calorie compared to carbohydrates or protein in controlled settings (Blundell et al., 2010).
References:
- Blundell, J. E., et al. (2010). Appetite control: methodological aspects of the evaluation of foods. Obesity Reviews, 11(3), 251-274.
- USDA FoodData Central, Food ID: 172049 (USDA, 2023a).
4. Physical Properties:
- Density: Approximately 0.916 - 0.923 g/cm³ at 20°C (68°F).
- Volumetric Contraction After Cooking/Heating: Negligible for practical nutritional tracking purposes. Thermal expansion/contraction of oils typically ranges from 0.0006 to 0.0008 cm³/cm³/°C, meaning a volume change of less than 1% for a typical temperature swing during cooking (e.g., room temperature to frying temperature). This does not significantly affect mass-based nutrient calculations.
- Smoke Point: Refined canola oil: ~204-246°C (400-475°F).
- Viscosity: Varies with temperature, approximately 70-80 cP at 20°C.
References:
- Akhtar, M. J., et al. (2010). Effect of temperature on density and viscosity of vegetable oils. Journal of Engineering Science and Technology Review, 3(1), 1-5.
- The National Canola Council (Canada). (2018). Canola Oil Technical Bulletin.
Citations:
- USDA. (2023a). FoodData Central, Oil, canola. U.S. Department of Agriculture, Agricultural Research Service. Retrieved from https://fdc.nal.usda.gov/fdc-app.html#/food-details/2710188/nutrients
- USDA. (2023b). FoodData Central, Phytosterols. U.S. Department of Agriculture, Agricultural Research Service. Retrieved from https://fdc.nal.usda.gov/fdc-app.html#/nutrient-details/10000000/nutrients
- Blundell, J. E., et al. (2010). Appetite control: methodological aspects of the evaluation of foods. Obesity Reviews, 11(3), 251-274.
- Health Canada. (2014). Food Composition Table - Phytosterols. Retrieved from https://www.canada.ca/en/health-canada/services/food-nutrition/food-nutrition-data/food-composition-tables/phytosterols-table.html
- Akhtar, M. J., et al. (2010). Effect of temperature on density and viscosity of vegetable oils. Journal of Engineering Science and Technology Review, 3(1), 1-5.
- The National Canola Council (Canada). (2018). Canola Oil Technical Bulletin.
Field Notes: Dr. Aria Vance
Subject: Canola Oil
Focus: Volumetric expansion/contraction, historical context, tracking challenges.
The Slippery Truth: Why Tracking Canola Oil Is a Nightmare
Dr. Aria Vance, Lead Nutrition Data Scientist, NutriSnap
October 26, 2023
Canola. A word now synonymous with "vegetable oil," almost generic. But its journey? Fascinating. It started as rapeseed oil, you know, from the Brassica napus plant. Rapeseed. Not exactly appetizing. And honestly, for good reason back in the day; high levels of erucic acid made it… less than ideal for consumption. Toxic, even. Breeders in Canada, absolute heroes of agronomy, managed to cultivate variants with significantly reduced erucic acid. A triumph! So, "Canadian Oil, Low Acid" became "Canola." Brilliant rebranding. Pure genius, really, turning a pariah into a pantry staple.
But tracking this ubiquitous liquid? It's a fool's errand. It truly is. You drizzle, you pour, you sauté. "Just a splash," you think. A splash. What does that even mean? It coats, it clings, it disappears into your food. Trying to get an accurate read using traditional methods – scales, measuring spoons – is an exercise in futility. The sheer viscosity makes precise volumetric measurements a joke. You scoop a tablespoon; how much sticks to the spoon? To the bowl? Every time, it’s different. Every. Single. Time.
And scales? You zero out the plate, add the oil, then what? Pour it into the pan, inevitably leaving a gram or two (or five) clinging to the sides. Those uncounted grams? They add up. Fast. They become a phantom energy source, invisible to your log, yet very real to your metabolism. Barcode scanning? Oh, that’s rich. It tells you what kind of oil, sure, but not the amount you just poured into your stir-fry. It’s not a pre-portioned protein bar. It’s a dynamic, elusive medium, mocking our attempts at precision. Every drop, a tiny, unrecorded calorie ghost haunting our data.
We needed a paradigm shift. We needed to see what the human eye, what the clumsy measuring cup, simply couldn't quantify with necessary accuracy. We needed to transcend the manual, the messy, the inherently flawed. And then, at a hackathon, spurred by my own frustration with oil tracking, the seed for NutriSnap was planted. Computer vision. AI. The ability to forensically analyze a before-and-after photo, to calculate volumetric displacement, to account for surface tension and residual film. It’s not just scanning; it's seeing. Truly seeing. It transforms the guesswork into data. Accurate data. For every single drop. This... this changes everything.
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