Beekeeping Dotaz Zobrazit nápovědu

Přesná shoda Sémantické

BACKGROUND: Honeybee viruses have been recognized as being among the most important factors leading to colony losses worldwide. Colony food and faeces are regarded as possible sources of infectious viruses able to contaminate the environment and equipment of apiaries. Thus, methods for elimination of viruses are required. No cell culture assay for testing the effect of disinfectants on honeybee viruses is yet available. Therefore, surrogate virus was employed for testing of the efficacy of iodophor- and peracetic acid-based disinfectants in combination with six organic contaminants at +6 °C and +22 °C. Moreover, we evaluated the persistence of the surrogate in honey at +6 °C, +22 °C, and +50 °C. RESULTS: Iodophor-based disinfectant showed a maximum reduction of virus titre of 3.4 log10. Peracetic acid reduced the titre (≥4 log10) only at 22 °C and without yeast extract/bovine serum albumin. After 25 days of incubation of the virus - honey mix, no decrease of virus titre was observed at +6 °C, whereas a significant reduction (3.5 log10) was found at +50 °C already after 1 day. CONCLUSIONS: Both tested disinfectants can serve as appropriate virucides in apiaries. The effect of peracetic acid significantly depended on temperature and organic contaminants. The iodophor-based disinfectant showed a stable antiviral effect at different temperatures and with different contaminants. © 2017 Society of Chemical Industry.

Článek online

Identification of pollen taxa by different microscopy techniques

PLoS One. 2021 ; 16 (9) : e0256808. [pub] 20210901

ISSN 1932-6203
Medvik
Zdroj

Melissopalynology is an important analytical method to identify botanical origin of honey. Pollen grain recognition is commonly performed by visual inspection by a trained person. An alternative method for visual inspection is automated pollen analysis based on the image analysis technique. Image analysis transfers visual information to mathematical descriptions. In this work, the suitability of three microscopic techniques for automatic analysis of pollen grains was studied. 2D and 3D morphological characteristics, textural and colour features, and extended depth of focus characteristics were used for the pollen discrimination. In this study, 7 botanical taxa and a total of 2482 pollen grains were evaluated. The highest correct classification rate of 93.05% was achieved using the phase contrast microscopy, followed by the dark field microscopy reaching 91.02%, and finally by the light field microscopy reaching 88.88%. The most significant discriminant characteristics were morphological (2D and 3D) and colour characteristics. Our results confirm the potential of using automatic pollen analysis to discriminate pollen taxa in honey. This work provides the basis for further research where the taxa dataset will be increased, and new descriptors will be studied.