Fresh fenugreek leaves (methi) are a bitter leafy herb used extensively in South Asian cooking. They provide vitamin K, beta-carotene, vitamin C, iron, and calcium. The characteristic bitter flavour comes from alkaloids including trigonelline. They are used in Indian flatbreads (methi paratha), curries, and as a vegetable green.
Blanche briefly in salted water to reduce bitterness; or salt and rest for 10 minutes then squeeze out moisture before cooking. Pairs well with potato, paneer, and lamb to balance their richness.
Where Fenugreek leaves, raw Stands Out
Macronutrients per 100g
Vitamins & Minerals
| Nutrient | Per 100g | % Daily Value* | Level |
|---|---|---|---|
| Vitamin A (RE) | 893 µg | 128% | |
| Chloride | 170 mg | 21% | |
| Calcium | 140 mg | 18% | |
| Vitamin C | 91.0 mg | 114% | |
| Magnesium | 59.0 mg | 16% | |
| Phosphorus | 45.0 mg | 6% | |
| Potassium | 31.0 mg | 2% | |
| Iron | 8.80 mg | 63% | |
| Niacin (B3) | 1.40 mg | 9% | |
| Riboflavin (B2) | 0.280 mg | 20% | |
| Copper | 0.260 mg | 26% | |
| Thiamin (B1) | 0.120 mg | 11% | |
| Vitamin B12 | 0.000 µg | 0% | |
| Vitamin D | 0.000 µg | 0% | |
| Maltose | 0.000 g | — | |
| Cholesterol | 0.000 mg | — | |
| Beta-carotene | 5,360 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 →