Decoding the Behavior of a Queenless Colony Using Sound Signals

Honeybees are reported to be the most vital pollinators of agricultural and horticultural crops. However, their widespread decline has raised great attention to the need to monitor their activity in order to identify the causes and implement countermeasures. The recording and analysis of signals used by honeybees for their communication in their hive can be a very helpful tool to the beekeepers for the remote control of the hives. Thus, in the present study, we used a set of sound recording data taken inside the hives to automatically detect the sounds of the bees over a certain period, to distinguish between queenright and queenless states, and to find the gradual changes in the queenless state. Unlike what was commonly believed, noticeable changes in the sound signals of all experimental colonies were observed just one hour after the queens' removal from the hive, while the sound signals were intensified over a period of 5 h, after which the transmitted signal stabilized to the equivalent of a queenless state. The colonies seemed to return to their normal sounds 9-10 days after the reintroduction of the queens in the hives. Our study concluded that timely intervention of the queen's absence combined with the immediate intervention of the beekeeper may be a determining factor in mitigating the adverse effects that occur from the queen's loss.

A chemometric approach for the differentiation of 15 monofloral honeys based on physicochemical parameters

BACKGROUND

Although the main method for authentication of monofloral honey is pollen analysis, other classification approaches have been also applied. However, the majority of the existing classification models so far have utilized a few honey types or a few honey samples of each honey type, which can lead to inaccurate results. Aiming at addressing this, the goal of the present study was to create a classification model by analysing in total 250 honey samples from 15 different monofloral honey types in ten physicochemical parameters and then, multivariate analysis [multivariate analysis of variance (MANOVA), principal component analysis (PCA) and multi-discriminant analysis (MDA)] was applied in an effort to distinguish and classify them.

RESULTS

Electrical conductivity and colour were found to have the highest discriminative power, allowing the classification of monofloral honey types, such as oak, knotgrass and chestnut honey, as well as the differentiation between honeydew and nectar honeys. The classification model had a high predictive power, as the 84.4% of the group cases was correctly classified, while for the cases of chestnut, strawberry tree and sunflower honeys the respective prediction was correct by 91.3%, 95% and 100%, allowing further determination of unknown honey samples.

CONCLUSION

It seems that the characterization of monofloral honeys based on their physicochemical parameters through the proposed model can be achieved and further applied on other honey types. The results could contribute to the development of methodologies for the determination of honey's botanical origin, based on simple techniques, so that these can be applied for routine analysis. © 2021 Society of Chemical Industry.

The determination of the botanical origin in honeys with over-represented pollen: combination of melissopalynological, sensory and physicochemical analysis

BACKGROUND

Pollen analysis of honey is the basic method for the determination of its botanical origin. However, the presence of over-represented pollen in honeys may lead the analysis to false results. This can be more severe if this pollen is present in unifloral under-represented honeys of commercial importance (e.g. thyme honey). In the present study, we investigated the abundance of over-represented pollen grains on several quality characteristics in honey samples. In particular, we mixed honeys characterised as over-represented, specifically chestnut and eucalyptus, with thyme honeys in different analogies, and we also analysed the melissopalynological, organoleptic, physicochemical (water content, electrical conductivity, colour) and volatile characteristics of the blends.

RESULTS

The most sensitive parameters were the microscopic characteristics, followed by the organoleptic ones. Blends of thyme honey with an originally low percentage of thyme pollen were the most influenced and could not be characterised as unifloral regarding their melissopalynological characteristics, even when they were mixed with small quantities of honeys with over-represented pollen (i.e. 5%).

CONCLUSION

The present study confirms that, in the case of presence of over-represented pollen in honeys, pollen analysis alone cannot give trustworthy results for the determination of the botanical origin, even though their exclusion during pollen analysis, when they are present in percentages of up to 30%, could provide more accurate results. Consequently, pollen analysis should also be combined with the other analyses, especially in honeys with under-represented and over-represented pollens, to give safer results for the botanical characterisation of honeys. © 2017 Society of Chemical Industry.