Where Curry, vegetable, retail, with rice Stands Out
Macronutrients per 100g
Vitamins & Minerals
| Nutrient | Per 100g | % Daily Value* | Level |
|---|---|---|---|
| Chloride | 284 mg | 36% | |
| Potassium | 163 mg | 8% | |
| Vitamin A (RE) | 60.0 µg | 9% | |
| Phosphorus | 52.0 mg | 7% | |
| Folate | 33.0 µg | 17% | |
| Calcium | 30.0 mg | 4% | |
| Magnesium | 14.0 mg | 4% | |
| Selenium | 4.00 µg | 7% | |
| Niacin equivalents (NE) | 2.20 mg | 14% | |
| Niacin (B3) | 1.50 mg | 9% | |
| Vitamin E (alpha-TE) | 1.23 mg | 10% | |
| Iron | 0.510 mg | 4% | |
| Pantothenic Acid (B5) | 0.420 mg | 7% | |
| Zinc | 0.300 mg | 3% | |
| Manganese | 0.220 mg | 11% | |
| Copper | 0.120 mg | 12% | |
| Vitamin B6 | 0.080 mg | 6% | |
| Thiamin (B1) | 0.070 mg | 6% | |
| Riboflavin (B2) | 0.030 mg | 2% | |
| Vitamin C | 0.000 mg | 0% | |
| Vitamin B12 | 0.000 µg | 0% | |
| Vitamin D | 0.000 µg | 0% | |
| Iodine | 2.00 µg | 1% | |
| Monounsaturated fat | 1.39 g | — | |
| Polyunsaturated fat (PUFA) | 1.22 g | — | |
| Trans fat | 0.090 g | — | |
| Sucrose | 0.600 g | — | |
| Glucose (dextrose) | 0.500 g | — | |
| Fructose | 0.500 g | — | |
| Maltose | 0.000 g | — | |
| Cholesterol | 1.90 mg | — | |
| Beta-carotene | 313 µg | — | |
| Retinol (vitamin A) | 8.00 µg | 1% | |
| Biotin (Vitamin B7) | 1.90 µg | 4% |
* % 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 →