Where Pizza, tomato, homemade Stands Out
Macronutrients per 100g
Vitamins & Minerals
| Nutrient | Per 100g | % Daily Value* | Level |
|---|---|---|---|
| Chloride | 570 mg | 71% | |
| Potassium | 230 mg | 12% | |
| Vitamin A (RE) | 63.0 µg | 9% | |
| Phosphorus | 57.0 mg | 8% | |
| Calcium | 41.0 mg | 5% | |
| Folate | 22.0 µg | 11% | |
| Magnesium | 14.0 mg | 4% | |
| Vitamin C | 5.00 mg | 6% | |
| Niacin equivalents (NE) | 1.60 mg | 10% | |
| Vitamin E (alpha-TE) | 1.35 mg | 11% | |
| Selenium | 1.00 µg | 2% | |
| Niacin (B3) | 1.00 mg | 6% | |
| Iron | 1.00 mg | 7% | |
| Zinc | 0.300 mg | 3% | |
| Pantothenic Acid (B5) | 0.230 mg | 4% | |
| Manganese | 0.200 mg | 10% | |
| Vitamin B6 | 0.110 mg | 8% | |
| Thiamin (B1) | 0.110 mg | 10% | |
| Copper | 0.090 mg | 9% | |
| Riboflavin (B2) | 0.040 mg | 3% | |
| Vitamin B12 | 0.000 µg | 0% | |
| Vitamin D | 0.000 µg | 0% | |
| Iodine | 4.00 µg | 3% | |
| Monounsaturated fat | 7.10 g | — | |
| Polyunsaturated fat (PUFA) | 1.40 g | — | |
| Fructose | 1.10 g | — | |
| Glucose (dextrose) | 1.00 g | — | |
| Sucrose | 0.800 g | — | |
| Maltose | 0.000 g | — | |
| Cholesterol | 0.000 mg | — | |
| Beta-carotene | 380 µg | — | |
| Biotin (Vitamin B7) | 2.70 µg | 5% | |
| Retinol (vitamin A) | 0.000 µg | 0% |
* % 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 →