Pine honey is a honeydew honey produced in the East Mediterranean region (Greece and Turkey) from the secretions of the plant sucking insect Marchalina hellenica (Gennadius) (Coccoidea: Marchalini-dae) feeding on living parts of Pinus species. Nowadays, honeydew honey has attracted great attention due to its biological activities. The aim of this study was to study unifloral pine honey samples produced in Greece regarding their physicochemical parameters and antioxidant and antibacterial activity against five nosocomial and foodborne pathogens. These honeys showed physicochemical and microscopic characteristics within the legal limits, except for diastase activity, a parameter known to be highly variable, depending on various factors. Substantially higher levels of H2O2 were estimated compared to other types of honeydew honey, whereas protein content was similar. The total phenolic content was 451.38 ± 120.38 mg GAE/kg and antiradical activity ranged from 42.43 to 79.33%, while FRAP values (1.87 to 9.43 mmol Fe+2/kg) were in general higher than those reported in the literature. Various correlations could be identified among these parameters. This is the first attempt to investigate in depth the antibacterial activity of pine honey from Greece and correlate it with honey quality parameters. All tested honeys exerted variable but significant antibacterial activity, expressed as MIC and MBC values, comparable or even superior to manuka honey for some tested samples. Although honey antibacterial activity is mainly attributed to hydrogen peroxide and proteins in some cases (demonstrated by elevated MICs after catalase and Proteinase K treatment, respectively), no strong correlation between the antibacterial activity and hydrogen peroxide concentration or total protein content was demonstrated in this study. However, there was a statistically significant correlation of moisture, antioxidant and antibacterial activity against Klebsiella pneuomoniae, as well as antioxidant and antibacterial activity against Salmonella ser. Typhimurium. Interestingly, a statistically significant negative correlation has been observed between diastase activity and Staphylococcus aureus antibacterial activity. Overall, our data indicate multiple mechanisms of antibacterial activity exerted by pine honey.
High mountain zones in the Mediterranean area are considered more vulnerable in comparison to lower altitudes zones. Lefka Ori massif, a global biodiversity hotspot on the island of Crete is part of the Global Observation Research Initiative in Alpine Environments (GLORIA) monitoring network. The paper examines species and vegetation changes with respect to climate and altitude over a seven-year period (2001–2008) at a range of spatial scales (10 m Summit Area Section-SAS, 5 m SAS, 1 m2) using the GLORIA protocol in a re-survey of four mountain summits (1664 m–2339 m). The absolute species loss between 2001–2008 was 4, among which were 2 endemics. At the scale of individual summits, the highest changes were recorded at the lower summits with absolute species loss 4 in both cases. Paired t-tests for the total species richness at 1 m2 between 2001–2008, showed no significant differences. No significant differences were found at the individual summit level neither at the 5 m SAS or the 10 m SAS. Time series analysis reveals that soil mean annual temperature is increasing at all summits. Linear regressions with the climatic variables show a positive effect on species richness at the 5 m and 10 m SAS as well as species changes at the 5 m SAS. In particular, June mean temperature has the highest predictive power for species changes at the 5 m SAS. Recorded changes in species richness point more towards fluctuations within a plant community’s normal range, although there seem to be more significant diversity changes in higher summits related to aspects. Our work provides additional evidence to assess the effects of climate change on plant diversity in Mediterranean mountains and particularly those of islands which remain understudied.
Pollen identification is an important task for the botanical certification of honey. It is performed via thorough microscopic examination of the pollen present in honey; a process called melissopalynology. However, manual examination of the images is hard, time-consuming and subject to inter- and intra-observer variability. In this study, we investigated the applicability of deep learning models for the classification of pollen-grain images into 20 pollen types, based on the Cretan Pollen Dataset. In particular, we applied transfer and ensemble learning methods to achieve an accuracy of 97.5%, a sensitivity of 96.9%, a precision of 97%, an F1 score of 96.89% and an AUC of 0.9995. However, in a preliminary case study, when we applied the best-performing model on honey-based pollen-grain images, we found that it performed poorly; only 0.02 better than random guessing (i.e., an AUC of 0.52). This indicates that the model should be further fine-tuned on honey-based pollen-grain images to increase its effectiveness on such data.
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