Analytical Concentrations of Some Elements in Seeds and Crude Extracts from Aesculus hippocastanum, by ICP-OES Technique

The metal content in some samples of horse chestnut seeds (Aesculus hippocastanum) was monitored over time (years 2016-2019) considering the two most common and representative Mediterranean varieties: the pure species (AHP, which gives white flowers) and a hybrid one (AHH, which gives pink flowers). The selected elemental composition of the samples was determined by applying the Inductively Coupled Plasma-Optical Emission Spectroscopy (ICP-OES) technique. Several samples obtained from different preliminary treatments of the peeled seeds were examined, such as: (i) floury samples (wild-type) mineralized with the wet method; (ii) the ashes of both AHP and AHH varieties; (iii) the fraction of total inorganic soluble salts (TISS). Furthermore, the hydroalcoholic crude extracts (as a tincture) were obtained according to the official Pharmacopoeia methods, and the relevant results were compared with those of a commercial sample, an herbal product-food supplement of similar characteristics. The main characteristics of this research work underline that the two botanical varieties give different distinctive characters, due to the Fe content (80.05 vs. 1.42 mg/100 g d.s., for AHP and AHH wild-type flour samples, respectively), along with K, Ca, Mn, Ni and Cu, which are more abundant in the AHP samples. Furthermore, the Principal Component Analysis (PCA) was applied to the experimental dataset in order to classify and discriminate the samples, in relation to their similar botanical origin, but different for the color of the bloom. These results can be useful for the traceability of raw materials potentially intended for the production of auxiliary systems of pharmacological interest.

Automated summary

By monitoring the metal content in horse chestnut seeds from different varieties over time, it was found that the Fe, K, Ca, Mn, Ni, and Cu levels varied between the pure species and hybrid species. Principal Component Analysis (PCA) was used to classify samples based on their elemental composition, providing insights for traceability of raw materials in pharmaceutical auxiliary systems.