Dried mixed herbs are a convenient blend of typically thyme, marjoram, oregano, basil, and sometimes rosemary and sage. The combined profile delivers concentrated vitamin K, iron, calcium, and manganese from the constituent herbs. The blend is practical for everyday cooking but less nuanced than choosing individual herbs.
Add to dishes early in cooking; use 1-2 teaspoons per dish. Bloom in oil with onions and garlic before adding liquid for better flavour development. Replace the jar at least annually as dried herbs lose potency.
Where Mixed herbs, dried Stands Out
Macronutrients per 100g
Vitamins & Minerals
| Nutrient | Per 100g | % Daily Value* | Level |
|---|---|---|---|
| Potassium | 1,873 mg | 94% | |
| Calcium | 1,653 mg | 207% | |
| Vitamin A (RE) | 1,350 µg | 193% | |
| Magnesium | 280 mg | 75% | |
| Phosphorus | 235 mg | 34% | |
| Iron | 69.0 mg | 493% | |
| Vitamin C | 43.0 mg | 54% | |
| Manganese | 9.84 mg | 492% | |
| Niacin (B3) | 5.00 mg | 31% | |
| Zinc | 4.60 mg | 46% | |
| Copper | 0.730 mg | 73% | |
| Riboflavin (B2) | 0.340 mg | 24% | |
| Vitamin D | 0.000 µg | 0% | |
| Trans fat | 0.000 g | — | |
| Maltose | 0.000 g | — | |
| Cholesterol | 0.000 mg | — | |
| Beta-carotene | 8,100 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 →