A Low-Cost, Low-Power, Multisensory Device and Multivariable Time Series Prediction for Beehive Health Monitoring.

We present a custom platform that integrates data from several sensors measuring synchronously different variables of the beehive and wirelessly transmits all measurements to a cloud server. There is a rich literature on beehive monitoring. The choice of our work is not to use ready platforms such as Arduino and Raspberry Pi and to present a low cost and power solution for long term monitoring. We integrate sensors that are not limited to the typical toolbox of beehive monitoring such as gas, vibrations and bee counters. The synchronous sampling of all sensors every 5 min allows us to form a multivariable time series that serves in two ways: (a) it provides immediate alerting in case a measurement exceeds predefined boundaries that are known to characterize a healthy beehive, and (b) based on historical data predict future levels that are correlated with hive's health. Finally, we demonstrate the benefit of using additional regressors in the prediction of the variables of interest. The database, the code and a video of the vibrational activity of two months are made open to the interested readers.

Evolution of Physicochemical Properties and Phenolic Maturity of Vilana, Vidiano, Kotsifali and Mandilari Wine Grape Cultivars (Vitis vinifera L.) during Ripening.

Determining the optimum harvest time is a significant factor affecting the quality of the grapes and the wine. Monitoring the evolution of grapes' physicochemical properties and phenolic maturity during ripening could be a valuable tool for determining the optimum harvest time. In this study, the total phenolic content, antioxidant activity, flavonols, flavanols, anthocyanins and resveratrol content were determined during the last weeks of ripening for the white cultivars Vilana and Vidiano, as well as for the red cultivars Kotsifali and Mandilari (Vitis vinifera L.). According to the results, an early harvest for the white cultivars and a late harvest for the red cultivars may increase the total phenolics and trans-resveratrol content in grapes and wine. An early harvest would be desirable to maintain high flavanols content and high levels of antioxidant activity in the grapes' skin and seeds. Conversely, a late harvest for the red cultivars may be desirable to increase the total flavonols and anthocyanin content in grapes and wines.

Pollen Grain Classification Based on Ensemble Transfer Learning on the Cretan Pollen Dataset.

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.

Physicochemical Characterization and Biological Properties of Pine Honey Produced across Greece.

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.

Unifloral Autumn Heather Honey from Indigenous Greek Erica manipuliflora Salisb.: SPME/GC-MS Characterization of the Volatile Fraction and Optimization of the Isolation Parameters.

For long heather honey has been a special variety due to its unique organoleptic characteristics. This study aimed to characterize and optimize the isolation of the dominant volatile fraction of Greek autumn heather honey using solid-phase microextraction (SPME) followed by gas chromatography-mass spectrometry (GC-MS). The described approach pointed out 13 main volatile components more closely related to honey botanical origin, in terms of occurrence and relative abundance. These volatiles include phenolic compounds and norisoprenoids, with benzaldehyde, safranal and p-anisaldehyde present in higher amounts, while ethyl 4-methoxybenzoate is reported for the first time in honey. Then, an experimental design was developed based on five numeric factors and one categorical factor and evaluated the optimum conditions (temperature: 60 °C, equilibration time: 30 min extraction time: 15 min magnetic stirrer velocity: 100 rpm sample volume: 6 mL water: honey ratio: 1:3 (v/w)). Additionally, a validation test set reinforces the above methodology investigation. Honey is very complex and variable with respect to its volatile components given the high diversity of the floral source. As a result, customizing the isolation parameters for each honey is a good approach for streamlining the isolation volatile compounds. This study could provide a good basis for future recognition of monofloral autumn heather honey.

Response Surface Methodology to Optimize the Isolation of Dominant Volatile Compounds from Monofloral Greek Thyme Honey Using SPME-GC-MS.

This study aimed at an experimental design of response surface methodology (RSM) in the optimization of the dominant volatile fraction of Greek thyme honey using solid-phase microextraction (SPME) and analyzed by gas chromatography-mass spectrometry (GC-MS). For this purpose, a multiple response optimization was employed using desirability functions, which demand a search for optimal conditions for a set of responses simultaneously. A test set of eighty thyme honey samples were analyzed under the optimum conditions for validation of the proposed model. The optimized combination of isolation conditions was the temperature (60 °C), equilibration time (15 min), extraction time (30 min), magnetic stirrer speed (700 rpm), sample volume (6 mL), water: honey ratio (1:3 v/w) with total desirability over 0.50. It was found that the magnetic stirrer speed, which has not been evaluated before, had a positive effect, especially in combination with other factors. The above-developed methodology proved to be effective in the optimization of isolation of specific volatile compounds from a difficult matrix, like honey. This study could be a good basis for the development of novel RSM for other monofloral honey samples.

Botanical origin discrimination of Greek honeys: physicochemical parameters versus Raman spectroscopy.

BACKGROUND: The authenticity of honey is of high importance since it affects its commercial value. The discrimination of the origin of honey is of prime importance to reinforce consumer trust. In this study, four chemometric models were developed based on the physicochemical parameters according to European and Greek legislation and one using Raman spectroscopy to discriminate Greek honey samples from three commercial monofloral botanical sources. RESULTS: The results of physicochemical (glucose, fructose, electrical activity) parameters chemometric models showed that the percentage of correct recognition fluctuated from 92.2% to 93.8% with cross-validation 90.6-92.2%, and the placement of test set was 79.0-84.3% successful. The addition of maltose content in the previous discrimination models did not significantly improve the discrimination. The corresponding percentages of the Raman chemometric model were 95.3%, 90.6%, and 84.3%. CONCLUSION: The five chemometric models developed presented similar and very satisfactory results. Given that the recording of Raman spectra is simple, fast, a minimal amount of sample is needed for the analysis, no solvent (environmentally friendly) is used, and no specialized personnel are required, we conclude that the chemometric model based on Raman spectroscopy is an efficient tool to discriminate the botanical origin of fir, pine, and thyme honey varieties. © 2020 Society of Chemical Industry.

Parasitic wasps related to Prays oleae (Bernard, 1788) (Lepidoptera, Praydidae) in olive orchards in Greece.

The olive moth, Prays oleae (Bernard, 1788) (Lepidoptera: Praydidae) is categorised among the most devastating insect pests of olives, whose anthophagous and carpophagous generations can cause yield loss up to 581 and 846 kg of fruit per ha, respectively. In this study, results of the captured parasitoids in olive tree (Olea europaea Linnaeus, 1753) orchards, or infested olive plant material in Crete, Greece, is presented. Five of the six identified species captured in trap devices are related to P. oleae, i.e., Chelonus elaeaphilus Silvestri, 1908, Chelonus pellucens (Nees, 1816), Apanteles xanthostigma (Haliday, 1834), Diadegma armillatum (Gravenhorst, 1829), and Exochus lentipes Gravenhorst, 1829. The species Eupelmus urozonus Dalman, 1820 and Pnigalio mediterraneus Ferrière & Delucchi, 1957 were reared from infested P. oleae leaves. Chelonus pellucens is reported for the first time from Greece. According to the international literature, 59 hymenopterous and dipterous parasitoid species are associated with P. oleae in Europe.

Comparison of the volatile composition in thyme honeys from several origins in Greece.

Thyme honey is the most appreciated unifloral Greek honey in Greece as well as around the world. In an effort to investigate the headspace composition of this type of honey, 28 samples were analyzed by means of solid-phase microextraction coupled to a gas chromatography-mass spectrometry system. The botanical origin of the samples was ascertained by pollen analysis, and samples displayed relative frequencies of thyme pollen between 18 and 41%. A total of 62 compounds were isolated, and phenylacetaldehyde was the most abundant (32.9% of the total peak area). Possible botanical markers are 1-phenyl-2,3-butanedione (13.4%), 3-hydroxy-4-phenyl-2-butanone, 3-hydroxy-1-phenyl-2-butanone (14.7%), phenylacetonitrile (4.8%), and carvacrol (0.9%), since these compounds are found only in thyme honey. Additionally, high proportions of phenylacetaldehyde are also characteristic ( F = 12.282, p < 0.001). The average concentrations of seven compounds were significantly different ( p < 0.05), namely phenylacetaldehyde, acetophenone, octanoic acid, carvacrol, phenylethyl alcohol, nonanal, and hexadecane. Applying principal component analysis to the data, six components were extracted, explaining 85.4% of the total variance. The first component explained 46.2% of the total variance and was positively correlated to phenylacetaldehyde, nonanoic acid, acetophenone, decanoic acid, benzaldehyde, phenylacetonitrile, isophorone, and nonanal. The extracted components were used as variables to the discriminant analysis, which showed good discrimination, especially for samples from Crete. A leave-one-out classification showed 85.7% of cross-validated grouped cases correctly classified. These results are promising to establish a discrimination model for these geographical regions. This is crucial for local beekeeper corporations on their effort to produce honey with geographical origin label.

FT-Raman spectroscopic simultaneous determination of fructose and glucose in honey.

A new method for mass percentage determination of fructose and glucose based on FT-Raman spectroscopy is evaluated with a standard HPLC-based method. FT-Raman spectra manipulation is done via the spectrometer software, and a PLS (partial least squares) method is developed with the TQ Analyst software (Ver 1. 1a). The simultaneous quantitative determination uses an input range from 1700 to 700 cm(-1) without correction or baseline factors. The standards used in the PLS method are honey samples previously analyzed by HPLC to obtain their mass percentage concentrations in fructose and glucose. The returned results are statistically tested with those of the HPLC method. Both methods appear to score equally in terms of reproducibility. The honey content of the two sugars in total was found up to 40-74%. The honey samples content in fructose and glucose was determined by HPLC (24.1-42.9% and 16.2-33.1%, respectively) and FT-Raman (24.0-40.8% and 21.1-32.2%, respectively).