Physicochemical Properties and Pollen Profile of Oak Honeydew and Evergreen Oak Honeydew Honeys from Spain: A Comparative Study

Multivariate Statistical Approach for the Discrimination of Honey Samples from Galicia (NW Spain) Using Physicochemical and Pollen Parameters

Raw honey is a food with a close relation to the territory in which it is produced because of factors such as soil conditions, weather patterns, and plant communities living in the area together. Furthermore, beekeeping management affects the properties of honey. Protected Geographical Indication Miel de Galicia protects the honey produced in Galicia (Northwest Spain). Various types of honeys (362 samples) from this geographical area were analyzed using chemometric techniques. Principal component analysis was favorable to analyzing the physicochemical and pollen variables with the greatest weight in the differentiation of honey. The linear discriminant analysis correctly classified 89.8% of the samples according to the botanical origin using main pollen spectra and physicochemical attributes (moisture, pH, electrical conductivity, diastase content, phenols, flavonoids, and color). Regarding unifloral honey, blackberry, eucalyptus, and heather honeys were correctly grouped, while five chestnut honeys and fourteen samples of honeydew honeys were misclassified. The chestnut and honeydew honeys have similar physicochemical properties and frequently similar pollen spectra profiles complicating the differentiation. Experimental evidence suggests the potential of multivariate statistics in the characterization of honey of the same geographical origin. Therefore, the classification results were good, with electrical conductivity, total phenol content, total flavonoid content and dominant pollens Eucalyptus, Erica, Rubus and Castanea sativa as the variables of higher importance in the differentiation of botanical origin of honeys.

Microscopic (LM and SEM) visualization of pollen ultrastructure among honeybee flora from lower Margalla Hills and allied areas

Microscopic visualization of micro-morphological characters were analyzed using a scanning electron microscopic (SEM) tool, which has proven to be very successful to analyze the pollen surface peculiarities. The significant goal of this research was to perform microscopic examination of pollen of some of the most frequently visited honeybee floral species around apiaries. Micro-morphological characterization of frequented honeybees foraged plants were discussed. A total of 15 species, belonging to 11 different families were identified for the foraging activities of honeybees, namely, Lantana camara, Jatropha integerrima, Helianthus annuus, Tecoma stans, Lagerstroemia indica, Duranta erecta, Cosmos sulphureus, Hymenocollis littoralis, Moringa oleifera, Cestrum nocturnum, Parthenium hysterophorus, Volkameria inermis, Catharanthus roseus, Malvastrum coromandelianum, and Citharexylum spinosum. The microscopic slides were prepared using the acetolysis method, and the qualitative and quantitative features were measured and described using microscopic tools. The pollen type sculpture varies from psilate scabrate to echinate and colpi from tricolpate to tetracolpate. Quantitative parameters such as polar diameter, mesocolpium distance, equatorial dimensions, aperture size, spine diameter, and exine thickness were calculated using IBM SPSS Statistics 20. The exine thickness was measured at its maximum in C. roseus (3.85 μm), whereas it was at its minimum of 0.90 μm in L. indica and D. erecta. Pollen fertility was estimated to be highest in H. annus (88%). The current research validates scanning microscopic features of pollen of the honeybee floral species, which is helpful for the accurate identification and commercializing of honey production to generate revenue for beekeepers through the exploration of bee floral diversity.

Evaluating the Presence and Contents of Phytochemicals in Honey Samples: Phenolic Compounds as Indicators to Identify Their Botanical Origin

Honey is a natural product well known for its beneficial properties. It contains phytochemicals, a wide class of nutraceuticals found in plants, including compounds with highly demonstrated antimicrobial and antioxidant capacities as phenolic compounds and flavonoids. The main goal of this work is the development of a miniaturized and environmentally friendly methodology to obtain the phenolic profile of Galician honeys (Northwest Spain) from different varieties such as honeydew, chestnut, eucalyptus, heather, blackberry and multi-floral. The total phenolic content (TPC) and antioxidant activity (AA) were also evaluated. As regards sample preparation, miniaturized vortex (VE) and ultrasound assisted extraction (UAE) employing aqueous-based solvents were performed. Individual quantification of 41 target phenolic compounds was carried out by liquid chromatography-tandem mass spectrometry (LC-MS/MS). Results revealed the presence of 25 phenolic compounds in the 91 analyzed samples, reaching concentrations up to 252 µg g⁻¹. Statistical tools such as analysis of variance (ANOVA) and principal component analysis (PCA) were employed to obtain models that allowed classifying the different honeys according to their botanical origin. Obtained results, based on TPC, AA and ∑phenolic compounds showed that significant differences appeared depending on the honey variety, being several of the identified phenol compounds being responsible of the main differentiation.

When a Tritrophic Interaction Goes Wrong to the Third Level: Xanthoxylin From Trees Causes the Honeybee Larval Mortality in Colonies Affected by the River Disease

The "River Disease" (RD), a disorder impacting honeybee colonies located close to waterways with abundant riparian vegetation (including Sebastiania schottiana, Euphorbiaceae), kills newly hatched larvae. Forager bees from RD-affected colonies collect honeydew excretions from Epormenis cestri (Hemiptera: Flatidae), a planthopper feeding on trees of S. schottiana. First-instar honeybee larvae fed with this honeydew died. Thus, we postulated that the nectars of RD-affected colonies had a natural toxin coming from either E. cestri or S. schottiana. An untargeted metabolomics characterization of fresh nectars extracts from colonies with and without RD allowed to pinpoint xanthoxylin as one of the chemicals present in higher amounts in nectar from RD-affected colonies than in nectars from healthy colonies. Besides, xanthoxylin was also found in the aerial parts of S. schottiana and the honeydew excreted by E. cestri feeding on this tree. A larva feeding assay where xanthoxylin-enriched diets were offered to 1^st instar larvae showed that larvae died in the same proportion as larvae did when offered enriched diets with nectars from RD-colonies. These findings demonstrate that a xenobiotic can mimic the RD syndrome in honeybee larvae and provide evidence of an interspecific flow of xanthoxylin among three trophic levels. Further, our results give information that can be considered when implementing measures to control this honeybee disease.

Comparative Study of Several Machine Learning Algorithms for Classification of Unifloral Honeys

Unifloral honeys are highly demanded by honey consumers, especially in Europe. To ensure that a honey belongs to a very appreciated botanical class, the classical methodology is palynological analysis to identify and count pollen grains. Highly trained personnel are needed to perform this task, which complicates the characterization of honey botanical origins. Organoleptic assessment of honey by expert personnel helps to confirm such classification. In this study, the ability of different machine learning (ML) algorithms to correctly classify seven types of Spanish honeys of single botanical origins (rosemary, citrus, lavender, sunflower, eucalyptus, heather and forest honeydew) was investigated comparatively. The botanical origin of the samples was ascertained by pollen analysis complemented with organoleptic assessment. Physicochemical parameters such as electrical conductivity, pH, water content, carbohydrates and color of unifloral honeys were used to build the dataset. The following ML algorithms were tested: penalized discriminant analysis (PDA), shrinkage discriminant analysis (SDA), high-dimensional discriminant analysis (HDDA), nearest shrunken centroids (PAM), partial least squares (PLS), C5.0 tree, extremely randomized trees (ET), weighted k-nearest neighbors (KKNN), artificial neural networks (ANN), random forest (RF), support vector machine (SVM) with linear and radial kernels and extreme gradient boosting trees (XGBoost). The ML models were optimized by repeated 10-fold cross-validation primarily on the basis of log loss or accuracy metrics, and their performance was compared on a test set in order to select the best predicting model. Built models using PDA produced the best results in terms of overall accuracy on the test set. ANN, ET, RF and XGBoost models also provided good results, while SVM proved to be the worst.

The Influence of Chemical Contaminants on the Physicochemical Properties of Unifloral and Multifloral Honey

The aim of this study was to evaluate and compare the effect of antibiotic and pesticide residues on the physicochemical properties of unifloral and multifloral honey. The mineral elements content of honey was analyzed and correlated with antibiotic and pesticide residues, and a positive correlation was found between manganese and neonicotinoids. Potassium was found to be the most abundant mineral compound. Correlations were found between mineral content, color, and the content of antibiotic and pesticide residues of honey. In meadow honey, residues of antibiotics and pesticides were undetectable. In some of the other types of honey, the maximum residue limits regulated by European legislation were exceeded. Endosulfan residue was found in mint and rapeseed, honey with 0.42 and 5.14 ng/g, respectively. Neonicotinoids were found in 27% of the analyzed honey samples. Chloramphenicol was identified only in rapeseed honey, with concentrations ranging from 0.2 ng/g to 0.8 ng/g. Nitrofurans were found in 14%, and nitroimidazoles were found in 6% of the analyzed samples. According to EU legislation that is in force, the use of antibiotics in beekeeping is not allowed. The MRLs for neonicotinoids are 50 ng/g, and for coumaphos, the maximum limit is 100 ng/g. For the other pesticide residues, the maximum limit is 10 ng/g. The results of statistical analysis obtained using principal component analysis (PCA) showed a major difference in the levels of contamination of raspberry and meadow honey and the other types of honey.

Prediction of Physicochemical Properties in Honeys with Portable Near-Infrared (microNIR) Spectroscopy Combined with Multivariate Data Processing

There is an increase in the consumption of natural foods with healthy benefits such as honey. The physicochemical composition contributes to the particularities of honey that differ depending on the botanical origin. Botanical and geographical declaration protects consumers from possible fraud and ensures the quality of the product. The objective of this study was to develop prediction models using a portable near-Infrared (MicroNIR) Spectroscopy to contribute to authenticate honeys from Northwest Spain. Based on reference physicochemical analyses of honey, prediction equations using principal components analysis and partial least square regression were developed. Statistical descriptors were good for moisture, hydroxymethylfurfural (HMF), color (Pfund, L and b* coordinates of CIELab) and flavonoids (RSQ > 0.75; RPD > 2.0), and acceptable for electrical conductivity (EC), pH and phenols (RSQ > 0.61; RDP > 1.5). Linear discriminant analysis correctly classified the 88.1% of honeys based on physicochemical parameters and botanical origin (heather, chestnut, eucalyptus, blackberry, honeydew, multifloral). Estimation of quality and physicochemical properties of honey with NIR-spectra data and chemometrics proves to be a powerful tool to fulfil quality goals of this bee product. Results supported that the portable spectroscopy devices provided an effective tool for the apicultural sector to rapid in-situ classification and authentication of honey.

Botanical Origin, Pollen Profile, and Physicochemical Properties of Algerian Honey from Different Bioclimatic Areas

The palynological and physicochemical analysis of 62 honey samples produced in different biogeographical areas of Algeria was conducted. Results showed high variety in the botanical origin of samples and their physicochemical profile. Twenty-six samples were polyfloral honey, 30 were unifloral honey from different botanical sources such as Eucalyptus, Citrus, Apiaceae, Punica, Erica, Rosmarinus, Eriobotrya, or Hedysarum, and 6 were characterized as honeydew honey. Pollen analysis allowed the identification of 104 pollen types belonging to 51 botanical families, whereas the physicochemical profile showed important variations between samples. Multivariate techniques were used to compare the characteristics of samples from different biogeographical areas, showing significant differences between humid-area samples, located in the northeast of the country, and samples taken in semiarid, subhumid, and arid zones. Principal-component analysis (PCA) extracted nine components explaining 72% of data variance, being 30%, the sum of Component 1 and Component 2. The plot of both components showed samples grouped upon botanical and geographical origin. The results of this paper highlighted the great variability in honey production of Algeria, evidencing the importance of honey characterization to guarantee authenticity and to valorize local production.

Honey: Chemical Composition, Stability and Authenticity

This Special Issue contains innovative research papers on the characterization, chemical composition and physical properties of honey. This constitutes very useful information to avoid frauds and to guarantee the authenticity of this food product. The knowledge of the particularities of honey is increasingly demanded by beekeepers and consumers, and also by labs to typify honeys according to their botanical origin and to check their quality. Melissopalynological, sensorial and physicochemical techniques are being used to study the characteristics of honeys samples from different plant sources and geographical areas. The combination of these analytical techniques with mathematical and statistical methods or chemometrics allows researchers to identify a set of variables or individual parameters that define independent samples, providing a practical solution to classify honey according to the geographical or the botanical origin.

Contribution to the Chromatic Characterization of Unifloral Honeys from Galicia (NW Spain)

Honey color and other physicochemical characteristics depend mainly on the botanical and geographical origin. The study of these properties could make easier a correct classification of unifloral honey. This work determined the palynological characteristics and some physicochemical properties such as pH, electrical conductivity, and color (Pfund scale and the CIELa*b* coordinates), as well as the total content of the bioactive compounds phenols and flavonoids of ninety-three honey samples. Samples were classified as chestnut, blackberry, heather, eucalyptus, and honeydew honey. The study showed a close relationship between the physicochemical variables and the botanical origin. The five types of honey presented different physicochemical properties among them. A principal component analysis showed that Hue, lightness, b*, and Chroma variables were important for the honey types classification, followed by Erica pollen, pH, Cytisus, and Castanea variables. A forward stepwise regression analysis was performed introducing as dependent variables the color (mm Pfund) and the Chroma and the Hue variables. The regression models obtained explained 86%, 74%, and 86% of the variance of the data, respectively. The combination of the chromatic and physicochemical and pollen variables through the use of multivariable methods was optimal to characterize and group the honey samples studied.