Chemical characterization and bioactive properties of Prunus avium L.: The widely studied fruits and the unexplored stems

The aim of this study was to characterize sweet cherry regarding nutritional composition of the fruits, and individual phytochemicals and bioactive properties of fruits and stems. The chromatographic profiles in sugars, organic acids, fatty acids, tocopherols and phenolic compounds were established. All the preparations (extracts, infusions and decoctions) obtained using stems revealed higher antioxidant potential than the fruits extract, which is certainly related with its higher phenolic compounds (phenolic acids and flavonoids) concentration. The fruits extract was the only one showing antitumor potential, revealing selectivity against HCT-15 (colon carcinoma) (GI50~74 μg/mL). This could be related with anthocyanins that were only found in fruits and not in stems. None of the preparations have shown hepatotoxicity against normal primary cells. Overall, this study reports innovative results regarding chemical and bioactive properties of sweet cherry stems, and confirmed the nutritional and antioxidant characteristics of their fruits.

A novel approach for honey pollen profile assessment using an electronic tongue and chemometric tools

Nowadays the main honey producing countries require accurate labeling of honey before commercialization, including floral classification. Traditionally, this classification is made by melissopalynology analysis, an accurate but time-consuming task requiring laborious sample pre-treatment and high-skilled technicians. In this work the potential use of a potentiometric electronic tongue for pollinic assessment is evaluated, using monofloral and polyfloral honeys. The results showed that after splitting honeys according to color (white, amber and dark), the novel methodology enabled quantifying the relative percentage of the main pollens (Castanea sp., Echium sp., Erica sp., Eucaliptus sp., Lavandula sp., Prunus sp., Rubus sp. and Trifolium sp.). Multiple linear regression models were established for each type of pollen, based on the best sensors sub-sets selected using the simulated annealing algorithm. To minimize the overfitting risk, a repeated K-fold cross-validation procedure was implemented, ensuring that at least 10-20% of the honeys were used for internal validation. With this approach, a minimum average determination coefficient of 0.91 ± 0.15 was obtained. Also, the proposed technique enabled the correct classification of 92% and 100% of monofloral and polyfloral honeys, respectively. The quite satisfactory performance of the novel procedure for quantifying the relative pollen frequency may envisage its applicability for honey labeling and geographical origin identification. Nevertheless, this approach is not a full alternative to the traditional melissopalynologic analysis; it may be seen as a practical complementary tool for preliminary honey floral classification, leaving only problematic cases for pollinic evaluation.