Revisiting Greek Propolis: Chromatographic Analysis and Antioxidant Activity Study

Abstract

Propolis is a bee product that has been extensively used in alternative medicine and recently has gained interest on a global scale as an essential ingredient of healthy foods and cosmetics. Propolis is also considered to improve human health and to prevent diseases such as inflammation, heart disease, diabetes and even cancer. However, the claimed effects are anticipated to be correlated to its chemical composition. Since propolis is a natural product, its composition is consequently expected to be variable depending on the local flora alignment. In this work, we present the development of a novel HPLC-PDA-ESI/MS targeted method, used to identify and quantify 59 phenolic compounds in Greek propolis hydroalcoholic extracts. Amongst them, nine phenolic compounds are herein reported for the first time in Greek propolis. Alongside GC-MS complementary analysis was employed, unveiling eight additional newly reported compounds. The antioxidant activity study of the propolis samples verified the potential of these extracts to effectively scavenge radicals, with the extract of Imathia region exhibiting comparable antioxidant activity to that of quercetin.

Fig 1. Magnified HPLC-UV chromatogram of a standard solution (500 ng/mL), at 254 nm.

Fig 2. HPLC-UV chromatogram (at 254 nm) of standard solution mix (500 ng/mL) with delayed elution of compounds (indicative marking).

Fig 3. HPLC-UV chromatogram of Crete propolis extract at 280 nm.

Fig 4. SIM chromatogram showing quantitation ions for kaempferol and pinocembrin (Crete sample).

Fig 5. SIM chromatogram showing quantitation and confirmation ions for CAPE (Crete sample).

Fig 6. Magnified HPLC-UV chromatogram (at 280 nm) of Imathia propolis extract.

Fig 7. Distribution of chemical classes of compounds in propolis of different Greek regions.

Fig 8. Distribution of individual compounds in propolis of different Greek regions.

Fig 9. Magnified GC-MS chromatogram of Crete propolis sample.

Fig 10. First three component plot in rotated space.

Fig 11. Plot of location’s scores in first three principal components.

Fig 12. Component plot in rotated space.

Fig 13. Plot of location’s scores in two principal components.