Papri beans are flat, broad pods related to hyacinth beans (Lablab purpureus), commonly used in South Asian cooking. They provide plant protein, fibre, and minerals but contain antinutritional factors including HCN precursors when raw that are destroyed by thorough cooking. The pods and seeds are used fresh in stir-fries and curries.
Always cook papri beans thoroughly — boil or stir-fry until completely soft. Young flat pods are most tender; mature pods with large seeds require longer cooking.
Where Beans, papri, raw Stands Out
Macronutrients per 100g
Vitamins & Minerals
| Nutrient | Per 100g | % Daily Value* | Level |
|---|---|---|---|
| Potassium | 440 mg | 22% | |
| Phosphorus | 100 mg | 14% | |
| Calcium | 100 mg | 13% | |
| Magnesium | 74.0 mg | 20% | |
| Chloride | 66.0 mg | 8% | |
| Iron | 2.40 mg | 17% | |
| Niacin equivalents (NE) | 1.70 mg | 11% | |
| Zinc | 1.00 mg | 10% | |
| Niacin (B3) | 0.800 mg | 5% | |
| Manganese | 0.700 mg | 35% | |
| Copper | 0.350 mg | 35% | |
| Riboflavin (B2) | 0.220 mg | 16% | |
| Thiamin (B1) | 0.160 mg | 15% | |
| Vitamin D | 0.000 µg | 0% | |
| Vitamin A (RE) | 0.000 µg | 0% | |
| Vitamin B12 | 0.000 µg | 0% | |
| Maltose | 0.000 g | — | |
| Cholesterol | 0.000 mg | — | |
| 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 →