Where Pakora/bhajia, aubergine, fried in vegetable oil, homemade Stands Out
Macronutrients per 100g
Vitamins & Minerals
| Nutrient | Per 100g | % Daily Value* | Level |
|---|---|---|---|
| Potassium | 247 mg | 12% | |
| Phosphorus | 82.0 mg | 12% | |
| Chloride | 56.0 mg | 7% | |
| Folate | 36.0 µg | 18% | |
| Magnesium | 28.0 mg | 7% | |
| Calcium | 27.0 mg | 3% | |
| Vitamin A (RE) | 22.0 µg | 3% | |
| Vitamin E (alpha-TE) | 5.38 mg | 45% | |
| Vitamin C | 2.00 mg | 3% | |
| Selenium | 2.00 µg | 4% | |
| Niacin equivalents (NE) | 1.70 mg | 11% | |
| Iron | 1.13 mg | 8% | |
| Niacin (B3) | 0.700 mg | 4% | |
| Manganese | 0.600 mg | 30% | |
| Zinc | 0.600 mg | 6% | |
| Pantothenic Acid (B5) | 0.350 mg | 6% | |
| Vitamin B6 | 0.160 mg | 11% | |
| Thiamin (B1) | 0.120 mg | 11% | |
| Copper | 0.100 mg | 10% | |
| Riboflavin (B2) | 0.060 mg | 4% | |
| Vitamin B12 | 0.000 µg | 0% | |
| Vitamin D | 0.000 µg | 0% | |
| Monounsaturated fat | 12.5 g | — | |
| Polyunsaturated fat (PUFA) | 7.07 g | — | |
| Trans fat | 0.010 g | — | |
| Sucrose | 0.800 g | — | |
| Glucose (dextrose) | 0.800 g | — | |
| Fructose | 0.600 g | — | |
| Maltose | 0.000 g | — | |
| Cholesterol | 0.000 mg | — | |
| Beta-carotene | 129 µg | — | |
| 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 →