Studies on the representativeness of sampling large lots of dried herbs and dried spices for their pyrrolizidine alkaloid content and results of the subsequent homogenisation of the laboratory sample.

Abstract

Since 2022, maximum levels for pyrrolizidine alkaloids (PAs) in herbs and spices have been in force in the EU. In this context, the representativeness of sampling and sample processing (including the homogeneity of the laboratory sample) poses a major challenge because even a very small proportion of PA-containing plant material is already sufficient to exceed the maximum levels. This study evaluated the representativeness of sampling of large lots (15 to 25 tonnes) of dried herbs (here oregano and sage) and dried spices (here cumin) for their PA contents, depending on the size of the aggregate sample. Based on these results, the methods of sampling for the control of plant toxins (which are also applicable to mycotoxins) in dried herbs, herbal infusions (dried product), teas (dried product) and powdered spices, as originally laid down in Commission Implementing Regulation (EU) 2023/2782, were amended, with the aggregate sample size increased from 2 to 4 kg (Commission Regulation (EU) 2024/885). This study also investigated the influence of the size and number of incremental samples taken to generate the aggregate sample. The data confirmed that larger aggregate samples generally result in a more reliable estimate of the PA content. Given the high volume-to-weight ratio of herbs, large aggregate samples also result in a tremendous workload for samplers and laboratories, so the best compromise between effort and precision has to be found for control purposes. Taking all data into account, an aggregate sample of around 4 kg appears to be sufficient to determine the PA content of dried herbs and powdered spices in lots of 15-25 t as reliable as possible. In terms of representativeness, no difference was found between aggregate samples generated by taking smaller incremental sample more frequently or by taking larger incremental samples less frequently.