Background: Volatile fraction profile and physicochemical parameters were studied with the aim of evaluating their effectiveness for the differentiation between lemon blossom honey (Citrus limon L.) and orange blossom honey (Citrus spp.). They would be useful complementary tools to the traditional analysis based on the percentage of pollen.
Results: A stepwise discriminant analysis constructed using 37 volatile compounds (extracted by purge and trap and analysed by gas chromatography-mass spectrometry), and physicochemical and colour parameters (diastase, conductivity, Pfund colour and CIE L a b) together provided a model that permitted the correct classification of 98.3% of the original and 96.6% of the cross-validated cases, indicating its efficiency and robustness. This model proved its effectiveness in the differentiation of both types of honey with another set of batches from the following year.
Conclusion: This model, developed from the volatile compounds, physicochemical and colour parameters, has been useful for the differentiation of lemon and orange blossom honeys. Furthermore, it may be of particular interest for the attainment of a suitable classification of orange honey in which the pollen count is very low. These capabilities imply an evident marketing advantage for the beekeeping sector, since lemon blossom honey could be commercialized as unifloral honey and not as generic citrus honey and orange blossom honey could be correctly characterized.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1002/jsfa.4520 | DOI Listing |
Sensors (Basel)
November 2024
Department of Analytical Chemistry, Faculty of Sciences, University of Cadiz, Agrifood Campus of International Excellence (ceiA3), IVAGRO, 11510 Puerto Real, Spain.
This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R values above 0.
View Article and Find Full Text PDFHardwareX
December 2024
School of Electrical Engineering and Computer Science, The Pennsylvania State University, University Park, 16802, PA, USA.
Honey bee foraging is a complex behavior because it involves tens of thousands of organisms making decisions about where to collect pollen and nectar based on the quality of resources and the distance to flowers. Studying this aspect of their biology is possible through direct observations but the large number of individuals involved in this behavior makes the implementation of technologies ideal to scale up this type of study. Consequently, there is a need for instruments that can facilitate accurate assessments of honey bee foraging at the colony level.
View Article and Find Full Text PDFNeotrop Entomol
December 2024
Programa de Pós-Graduação em Fitotecnia, Universidade Federal Rural do Rio de Janeiro (UFRRJ), Seropédica, RJ, Brazil.
Open Biol
December 2024
Department of Food Science, Faculty of Agrobiology, Food and Natural Resources, Czech University of Life Sciences Prague, Prague, Czech Republic.
Intensive agricultural practices impact the health and nutrition of pollinators like honey bees (). Rapeseed ( L.) is widely cultivated, providing diverse nutrients and phytochemicals, including -methyl-L-cysteine sulfoxide (SMCSO).
View Article and Find Full Text PDFSci Rep
December 2024
Department of Entomology, College of Agriculture and Natural Resources, National Chung Hsing University, Taichung, Taiwan.
This study investigates the impact of bee pollen nutrition on the royal jelly production of honey bees (Apis mellifera). Results demonstrate that pollen diet significantly impacts hypopharyngeal gland (HPG) development and the expression of genes associated with royal jelly biosynthesis. Bees fed Brassica napus pollen exhibited superior HPG development, and increased mrjp1 expression (encoding a key royal jelly protein).
View Article and Find Full Text PDFEnter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!