Orange-flesh sweet potatoes are particularly rich in beta-carotene for vitamin A, with vitamin C, potassium, fibre, and vitamin B6. Without skin they have a little less fibre than skin-on versions.
Always cook before eating; pair with a little fat to aid absorption of carotenoids.
Where Sweet potatoes, orange flesh, without skin, raw Stands Out
Macronutrients per 100g
Glycemic Index & Load
Medium GI food — moderate effect on blood sugar. Pair with protein, fat, or fibre to lower the overall meal GI.
Glycemic Load (GL = GI × carbs ÷ 100) accounts for portion size. A moderate GL — portion size matters here.
Vitamins & Minerals
| Nutrient | Per 100g | % Daily Value* | Level |
|---|---|---|---|
| Potassium | 486 mg | 24% | |
| Phosphorus | 36.7 mg | 5% | |
| Calcium | 22.3 mg | 3% | |
| Magnesium | 19.1 mg | 5% | |
| Vitamin C | 14.8 mg | 19% | |
| Niacin (B3) | 0.433 mg | 3% | |
| Manganese | 0.417 mg | 21% | |
| Iron | 0.398 mg | 3% | |
| Zinc | 0.337 mg | 3% | |
| Vitamin K | 0.200 µg | 0% | |
| Copper | 0.187 mg | 19% | |
| Vitamin B6 | 0.124 mg | 9% | |
| Thiamin (B1) | 0.045 mg | 4% | |
| Molybdenum | 0.000 µg | 0% | |
| Selenium | 0.000 µg | 0% | |
| Sucrose | 3.06 g | — | |
| Glucose (dextrose) | 0.976 g | — | |
| Fructose | 0.928 g | — |
* % Daily Value is the proportion of an adult
EU Nutrient Reference Value (NRV) supplied by 100g of this food.
NRVs are the labelling standard — the same basis as the “%RI” figures printed on
food packaging — so these percentages are directly comparable with a shop label.
They are a single adult figure and do not vary by age or sex, which is why they
sometimes differ from UK dietary reference values: the NRV for vitamin C is 80mg
where the UK RNI is 40mg. When Nutriofia assesses your own intake in the
reports and trackers, it uses the age- and sex-specific UK values instead.
Fibre is shown without a percentage. Our fibre values use the UK
Englyst NSP method, while the published 30g daily goal is an AOAC figure — the two
are measured differently and a single percentage would mislead.
How we source and normalise our data →