Anise seeds are the dried fruit of Pimpinella anisum, with a sweet liquorice flavour from anethole — the same compound that gives star anise and fennel their character. Used as a spice, they provide trace calcium, iron, and manganese per teaspoon. They are traditional in Mediterranean baking, Middle Eastern breads, and liqueurs such as pastis and ouzo.
Lightly crush or toast in a dry pan before use to release the essential oils. Start with half a teaspoon per recipe — anethole is potent and the flavour builds more than expected.
Where Anise seeds Stands Out
Macronutrients per 100g
Vitamins & Minerals
| Nutrient | Per 100g | % Daily Value* | Level |
|---|---|---|---|
| Potassium | 1,440 mg | 72% | |
| Calcium | 650 mg | 81% | |
| Phosphorus | 440 mg | 63% | |
| Magnesium | 170 mg | 45% | |
| Iron | 37.0 mg | 264% | |
| Zinc | 5.30 mg | 53% | |
| Manganese | 2.30 mg | 115% | |
| Copper | 0.910 mg | 91% | |
| Vitamin C | 0.000 mg | 0% | |
| Vitamin B12 | 0.000 µg | 0% | |
| Vitamin D | 0.000 µg | 0% | |
| Vitamin A (RE) | 0.000 µg | 0% | |
| Folate | 0.000 µg | 0% | |
| Monounsaturated fat | 9.80 g | — | |
| Polyunsaturated fat (PUFA) | 3.10 g | — | |
| 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 →