Enoki mushrooms (Flammulina velutipes) are long, slender, white mushrooms with tiny caps, grown in clusters in low-light conditions. They are lower in calories than most mushrooms and provide niacin, riboflavin, pantothenate, and potassium. Their mild, delicate flavour and crunchy texture suit light cooking; they are popular in East Asian hot pots, soups, and salads.
Trim the base of the cluster and separate; add to hot pot or soup in the final minute of cooking — they need very little heat and become soft quickly. Raw enoki can be used in salads with a light dressing.
Where Mushroom, enoki Stands Out
Macronutrients per 100g
Vitamins & Minerals
| Nutrient | Per 100g | % Daily Value* | Level |
|---|---|---|---|
| Potassium | 402 mg | 20% | |
| Phosphorus | 84.1 mg | 12% | |
| Magnesium | 12.8 mg | 3% | |
| Niacin (B3) | 6.99 mg | 44% | |
| Calcium | 1.43 mg | 0% | |
| Iron | 1.28 mg | 9% | |
| Zinc | 0.482 mg | 5% | |
| Riboflavin (B2) | 0.240 mg | 17% | |
| Vitamin B6 | 0.119 mg | 8% | |
| Copper | 0.093 mg | 9% | |
| Manganese | 0.090 mg | 5% | |
| Thiamin (B1) | 0.012 mg | 1% | |
| Selenium | 0.000 µg | 0% | |
| Vitamin D | 0.000 µg | 0% | |
| Stigmasterol | 0.000 mg | — | |
| Campesterol | 0.000 mg | — | |
| Biotin (Vitamin B7) | 12.5 µg | 25% |
* % 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 →