Characterization of artisanal honeys from Madrid (Central Spain) on the basis of their melissopalynological,physicochemical and volatile composition data

Forty-six artisanal honey samples, from different places of Madrid province (Central Spain), were characterized on the basis of their melissopalynological, physico-chemical and volatile composition data. Results were submitted to principal component analysis and stepwise discriminant analysis in order to evaluate the existence of data patterns and the possibility of differentiation of Madrid honey samples according to their botanical source (honeydew, nectar) and geographical collection place (mountain, plain). Colour, electrical conductivity, acidity, ash content and pH were the physicochemical parameters with higher discrimination power in the differentiation of nectar and honeydew honeys while, among the volatile components, concentrations of borneol, 1-(2-furanyl)-ethanone and 3-hydroxy-2-butanone were the most discriminant variables. In the differentiation of honey samples from mountain and plain zones, 2,3-butanediol and 1-(2-furanyl)-ethanone were the most significant volatiles, while physicochemical data were not useful for distinguishing between collection places. The honeydew percentage in a honey sample (HD%) was estimated from physicochemical measures and also from volatile concentrations; 2,3-butanediol, 3-hydroxy-2-butanone, 1-hydroxy-2-propanone and 1-(2-furanyl)-ethanone were found to be related to HD%.     

Some aspects of dynamic headspace analysis of volatile components in honey

Purge and Trap (P&T) has been evaluated as a fractionation technique for the GC–MS analysis of the most volatile compounds in honey. The volatile fraction of twenty-two commercial honeys of eight different botanical sources (eucalyptus, thyme, citrus, rosemary, heather, lavender, multiflower and honeydew) was characterized by P&T–GC–MS. Hundred volatile compounds were characterized, 18 of them being determined for the first time in honey. Compounds detected included volatiles derived from the floral nectar or honeydew source such as terpenes, furan derivatives from honey processing and storage and other compounds whose origin could be related to microbial or environmental contamination. This method has also been validated with regard to data reproducibility; with relative data (percentage of total volatile composition) showing a better precision over quantitative results calculated using an internal standard. Application of multivariate statistical analysis to P&T–GC–MS data has shown to be very promising to classify samples according to their botanical origin.     

Composition of volatile compounds of honey of various floral origin and beebread collected in Lithuania

Honey is collected from various flowering plants and its composition, particularly volatile flavour compounds to some extent depends on the nectar source. Therefore, some volatile constituents may be indicators of honey origin. In this study the volatile profiles of 15 honey samples of different botanical origin and one beebread sample are characterised. Volatiles were collected by means of SPME and analysed by GC/MS. Botanical source of honey samples was established by the melissopalynological method: 11 of analysed samples were unifloral rape honeys, 1 clover, 1 caraway and 2 polyfloral. In total 93 compounds in honey and 32 in beebread were identified. They involve different classes of chemical compounds, including alcohols, ketones, aldehydes, acids, terpenes, hydrocarbons, benzene, and furan derivatives. Benzaldehyde and benzenacetaldehyde were the only compounds found in all 15 honey sample. Dimethyl sulphide, pentanenitrile, benzylnitrile were identified in 14 honeys; isobutane, octanoic and nonanoic acids in 13 samples; furfural, linalool and nonanal in 12 samples; octanal, lilac aldehyde C, hotrienol and decanal in 11 samples and finally 2-methylbutanenitrile in 10 honey volatile fractions. Remarkable variations were observed in the composition of volatiles in honey from different sources. In addition, volatile profiles of honey samples were analysed after 3 months of storage and it was found that the amount of headspace volatiles in the majority of samples decreased.     

Antioxidant capacity of Spanish honeys and its correlation with polyphenol content and other physicochemical properties

Thirty‐six Spanish honeys of different floral origins (nectars and honeydews) were assessed to estimate their radical‐scavenging capacity against the stable free radical DPPH (1,1‐diphenyl‐2‐picrylhydrazyl), their ability to inhibit enzymatic browning in apple homogenate and their clarifying effect on apple juice. These capacities were evaluated spectrophotometrically. Physicochemical parameters such as pH, acidity, net absorbance, electrical conductivity and total polyphenol content of the samples were also evaluated. It was observed that all these parameters presented a strong correlation with radical‐scavenging capacity, whereas antibrowning capacity had a low correlation with conductivity and no correlation with honey pH. When data of nectar and honeydew honeys were evaluated separately, net absorbance and honey acidity seemed to be good parameters to evaluate the antioxidant activity of nectar honeys; however, in honeydew honeys there were only relationships between radical‐scavenging activity and honey conductivity and between browning inhibition of homogenates and total acidity. In general, honeydew honeys showed higher antioxidant capacities than nectar honeys. Copyright © 2007 Society of Chemical Industry     

Application of Selected Ion Flow Tube Mass Spectrometry Coupled with Chemometrics to Study the Effect of Location and Botanical Origin on Volatile Profile of Unifloral American Honeys

Abstract: Ten Ohio and Indiana honey samples from star thistle (Centaurea Americana), blueberry (Vaccinium spp.), clover (Trifolium spp.), cranberry (Vaccinium spp.), wildflower, and an unknown source were collected. The headspace of these honeys was analyzed by selected ion flow tube mass spectrometry and soft independent modeling of class analogy (SIMCA). SIMCA was utilized to statistically differentiate between honeys based on their composition. Ohio honeys from star thistle, blueberry, and clover were similar to each other in volatile composition, while Ohio wildflower honey was different. Indiana honeys from star thistle, blueberry, and wildflower were different from each other in volatile composition, while clover and cranberry honeys were similar. Honeys from Ohio and Indiana with the same floral origins were different in volatile composition. Furfural, 1‐octen‐3‐ol, butanoic, and pentanoic acids were the volatiles with the highest discriminating power between types of floral honey. Methanol and ethanol followed by acetic acid were at the highest levels in most honeys, though furfural was at the highest concentration in Indiana blueberry honey, while 1‐octen‐3‐ol was at the highest concentration in Indiana wildflower honey. The highest concentration of volatile compounds was in Indiana wildflower honey followed by Ohio wildflower honey, while the lowest concentration of volatile compounds was observed in Ohio clover honey followed by Indiana clover honey. Practical Application: Using chemometrics, concentrations of volatile compounds in different honeys can be used to determine the influence of botanical and geographical origins on aroma, which is important for the quality of honey. Characterization of volatile compounds can also be a useful tool for assessing honey quality.     

Effect of Adulteration versus Storage on Volatiles in Unifloral Honeys from Different Floral Sources and Locations

High fructose corn syrup (HFCS) was added at 5% to 40% to Indiana wildflower honey and added at 40% to Ohio and Indiana honeys from blueberry, star thistle, clover and wildflower, and an unknown source to simulate honey adulteration. Unadulterated honeys were also stored at 37 ºC from 1 to 6 mo. The volatile composition was measured by Selected Ion Flow Tube Mass Spectrometry (SIFT‐MS). Most volatiles decreased in concentration with both increasing HFCS and storage time. Furfural significantly increased in concentration in all adulterated honeys and 1,3‐butanediol, acetonitrile, and heptane in some adulterated honeys. During storage, the volatiles that increased were maltol, furfural, 5‐methylfurfural, and 5‐hydroxymethyl furfural in all honeys and also acetic acid and 1‐octen‐3‐ol levels in some honeys. Soft independent modeling by class analogy (SIMCA) was used to differentiate the volatile profiles of adulterated honeys from fresh and stored honeys. The volatile profiles of honeys in accelerated storage for up to 4 mo and the honeys adulterated with 40% HFCS were significantly different. Acetic acid had the most discriminating power in Ohio star thistle and blueberry honeys and unknown honey while furfural had the greatest discriminating power in Indiana blueberry, star thistle, and clover honeys. Adulteration and storage of honey both reduced the volatile levels, but since they changed the volatile composition of the fresh honey differently, SIMCA was able to differentiate adulteration from storage. Practical Application: Analysis of adulterated and stored honeys determined that both decrease volatile levels, and no clear indicator volatiles were found. However, SIMCA can be used to distinguish the volatile profiles of fresh or stored honeys, from adulterated honeys.     

Analysis of minority honey components: Possible use for the evaluation of honey quality

The minerals (Al, B, Ca, Cu, Mg, Mn, Ni, Zn) of 24 authentic Czech nectar and honeydew honey samples (2003 and 2004 harvests) were determined, to find the relationship between their content and the origin or type of honey. The concentrations of analytes in the digests, obtained via optimised dry decomposition of honey samples, were measured by flame atomic absorption spectrometry (F-AAS), as well as optical emission spectrometry with inductively coupled plasma (ICP-OES). With regard to its speed of analysis and cost, ICP-OES was the more favourable method. By combining the analytical data and electrolytic conductivities of samples, the honey samples could be divided into two groups – honeydew honeys and nectar honeys. Like Slovak and Polish honeys, samples of Czech honeys had higher nickel levels than honeys originating from other parts of the world.     

A contribution to the differentiation between nectar honey and honeydew honey

Thirty genuine honey samples were analyzed for pH, acidity, water, ash, net absorbance, total polyphenols (Folin–Ciocalteau method) and glucose, fructose, melezitose and erlose (as their trimethylsilyl oximes and trimethylsilyl ethers) by capillary gas chromatography. The resulting data were used, along with palynological analysis, to characterize the samples in relation to their possible source (nectar, honeydew and mixture honeys).Some minor components (carboxylic acids and cyclitols), eluting before monosaccharides, were also determined. One of these compounds was quercitol (1,3,4/2,5-cyclohexane-pentol), a deoxyinositol which has been previously determined in Quercus sp. samples. Quercitol was present in a broad concentration range (0.01–1.50 g/100 g) in honeys whose major source was honeydew but it was never higher than 0.01 g/100 g in samples characterized as nectar honeys. Quercitol concentrations appear to be related to the presence and amount of Quercus sp. honeydew as honey source, although further research is required to confirm this.     

The oligosaccharide composition of some New Zealand honeys

The oligosaccharide fraction of samples of manuka (Leptospermum), heather (Calluna), clover (Trifolium) and beech honeydew (Nothofagus) honeys from New Zealand was separated from the monosaccharides and then analysed by high performance anion-exchange chromatography with pulsed amperometric detection (hpaec-pad). Significant oligosaccharide components of manuka honey were isomaltose (or maltulose), kojibiose, turanose (or gentiobiose), nigerose and maltose which was the major component. The composition of clover honey was identical to that of manuka, while heather honey differed from these two only because isomaltose was the major component. Beech honeydew honey was characterised by the complexity of the oligosaccharide composition. The trisaccharides melezitose and panose were the most abundant components. No differences were observed between the oligosaccharide compositions of manuka honeys which did or did not exhibit non-peroxide residual antibacterial activity. Manuka honey was shown to be derived from nectar and not honeydew as has been suggested.     

Carbohydrate composition and physico chemical properties of artisanal honeys from Madrid (Spain): occurrence of Echium sp honey

Artisanal honey samples produced in Madrid province (central Spain) were characterised from their melissopalynological analysis, carbohydrate composition and physicochemical parameters. Melissopalynological analysis showed that Echium sp pollen appeared as the dominant palynomorph in most samples. It was also observed that many honey samples were natural blends from both nectar and honeydew botanical origins. Twenty‐five carbohydrates (glucose, fructose, 16 disaccharides and seven trisaccharides) were detected and quantified. Discriminant analysis was used to describe the variability of compositional data, which was probably associated with the different honey sources analysed (honeydew, Echium sp and other nectar plants), which in turn was related to the physiographical conditions (plain or mountain) occurring in Madrid province. Copyright © 2004 Society of Chemical Industry     

Fungal Honeydew Elements as Potential Indicators of the Botanical and Geographical Origin of Honeys

The present study applied indicator value analysis as a novel approach to estimate honey authenticity: microscopic indicators of honey origin were identified based on the distinct fungal content of honeys from different sources. The abundance and the IndVal index of 34 selected fungal spore types were quantified in 82 melissopalynological honey samples originating from various honeydew (Pinus, Abies) or nectar sources using multivariate statistical approaches. A dissimilarity matrix of honey samples was obtained by computing Bray-Curtis coefficients, and the distances were visualized using non-metric multidimensional scaling. K-means clustering was applied to sample coordinates to create a classification based on the frequency of selected fungal taxa. Strikingly, the resulting clusters were on a high level of agreement with the melissopalynological or geographical classification of samples. Various fungal taxa were shown to characterize groups of honey samples with a significant indicator value: floral honeys (Metschnikowia reukaufii), Pinus honeydew honeys (Capnobotrys sp., Antennatula sp.), Abies honeydew honeys from Greece (staurospore and scolecospore types) and honeydew honeys from Italy (Tripospermum spp. and Excipularia fusispora). Having revealed that the mere presence of distinct fungal taxa can indicate differences in the botanical and geographical source of honeys, the present findings encourage the confirmation of honey origin also by recording the occurrence of given honeydew elements during routine melissopalynological analysis.     

Differentiation of blossom and honeydew honeys using multivariate analysis on the physicochemical parameters and sugar composition

Multivariate analysis was applied on physicochemical parameters (moisture, water activity, electric conductivity, colour, hydroxymethyl furfural, acidity, pH, proline, diastase and invertase), sugar composition (fructose, glucose, sucrose, maltose, isomaltose, trehalose, turanose and melezitose) and palinological parameters determined in blossom and suspected honeydew honeys in order to differentiate them. The majority of the physicochemical, sugar composition and palinological parameters evaluated presented significant differences in the mean values between the suspected honeydew and blossom honeys, with the exception of moisture, water activity, diastase, fructose and maltose. Blossom honey samples tend to differentiate from the suspected honeydew honeys after applying factor analysis on the physicochemical parameters and sugar composition. Stepwise linear discriminant analysis allows the correct classification of all blossom honeys, and only one honeydew honey was erroneously included as blossom honey. So, the use of multivariate analysis on physicochemical parameters and sugar composition can be a useful tool to differentiate these types of honeys.     

Nature and Origin of the Antibacterial Substances in Honey

The nonperoxide antibacterial activity of honey and honey fractions was tested withStaphylococcus aureusandMicrococcus luteusbacterial species. Antibacterial activity correlated significantly with the honey acidity but did not correlate with honey pH. There were small differences between the antibacterial activities of different honey types: rhododendron, eucalyptus and orange honeys had a relatively low activity, whereas dandelion, honeydew and rape honeys had a relatively higher activity. These results suggest that a part of the antibacterial activity might be of plant origin. However, the antibacterial activity of sugar-adulterated honeys was the same as that of control honeydew honeys produced in the same apiary suggesting that the major part of the antibacterial activity of honeydew honey is of bee origin.Ten different honeys were fractionated into four fractions using column chromatography or vacuum distillation: acidic; basic; nonvolatile, nonpolar; and volatile. The antibacterial activity of the different fractions tested was: acids > bases = nonpolar, nonvolatiles > volatiles. This order was the same using eitherStaph. aureusorMicrococcus luteusas test strains.An exception was manuka honey from New Zealand where almost the entire activity was found in the acidic fraction.     

Classification of honeys by principal component analysis on the basis of chemical and physical parameters

Summary Eighteen chemical and physical parameters of nectar and honeydew honeys were determined and the results were analysed statistically by the method of principal component analysis. On the plots of principal component loadings, the honeys were divided into the following groups: (1) acacia honey; (2) rape honey; (3) linden, floral (nectar coming from various plants) and heather honeys; (4) honeydew honey. The samples which were a blend of nectar and honeydew honeys may be considered as a separate group between 3 and 4. The most important first principal component was strongly associated with the value of electrical conductivity, the contents of ash, free acids and proline, as well as with the pH and the diastase number. The principal component loadings and linear correlation suggested that these parameters contributed much more to the classification of honeys than apparent reducing sugars, apparent sucrose, mono-, di-, and trisaccharides, glucose and fructose. The classification of honeys by the method of principal component analysis may serve as an additional tool in specifying samples on the basis of their chemical composition.

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Klassifizierung von Honigen durch chemische und physikalische Parameter mit Hilfe der Hauptkomponentenanalyse

Zusammenfassung 18 chemische und physikalische Parameter der Nektar- und Honigtauhonige wurden bestimmt und die Ergebnisse statistisch mit Hilfe der Hauptkomponentenanalyse ausgewertet. Danach konnten die Honige in folgende Gruppen unterteilt werden: 1) Akazienhonige, 2) Rapshonige, 3) Linden-, Blüten-und Heidehonige, 4) Honigtauhonige. Zwischen dem Nektarhonig und Honigtauhonig lagen wahrscheinlich die Proben von Mischhonigen aus Blüten- und Waldtracht. Die bedeutendste erste Hauptkomponente war mit folgenden Parametern verbunden: elektrischer Leitfähigkeitswert und Aschegehalt, Gehalt an freien Säuren und Prolin sowie der pH-Wert und Diastasezahl. Aufgrund der Hauptkomponentenladungen und der linearen Korrelation kann angenommen werden, daß diese Parameter für die Honigklassifizierung von größerer Bedeutung sind als der Gehalt an reduzierenden Zuckern, scheinbarer Saccharose, Mono-, Di- und Trisaccharide, Glucose und Fructose. Die Klassifizierung der Honige mit Hilfe der Hauptkomponentenanalyse kann bei der Spezifikation einer Honigprobe aufgrund ihrer chemischen Zusammensetzung von Nutzen sein.

     

Buckwheat honeys: Screening of composition and properties

The quality of 10 buckwheat honeys, collected from Italian and est European beekeepers declaring to produce monofloral honey, were evaluated by means of their pollen, physicochemical, phenolic and volatile composition data. The results of the traditional analyses and in particular electrical conductivity, optical rotation, pH and sugar composition revealed some poorly pure samples that could not fit in the buckwheat tipology. Honey volatiles, analysed by solid phase microextraction (SPME) and gas chromatography–mass-spectrometry (GC/MS), showed more than 100 volatile compounds, most of them present in all honey samples but with quantitative variation. Besides many furfural derivates, 3-methylbutanoic acid was the main volatile compound found in most of honeys. Also the presence of 2- and 3-methylbutanal and pheynalcetaldehyde confirmed the typical buckwheat aroma of some studied samples, corroborating physicochemical data. The HPLC phenolic profile was similar across the samples and p-hydroxybenzoic and p-coumaric acids proved to be the main components.     

Antioxidant activity,color chromaticity coordinates,and chemical characterization of monofloral honeys from Morocco

The bioprospecting of several monofloral Moroccan honeys was carried out. The antiradical activity expressed as mmol Trolox equivalents/kg of honey and evaluated by 2,2-diphenyl-1-picrylhydrazyl assay, ranged from 0.15 for euphorb honey to 1.08 for citrus honey. The antioxidant activity expressed as mmol Fe²⁺/kg and evaluated by ferric ion reducing antioxidant power assay, ranged from 0.96 for euphorb honey to 4.74 for orange honey. The total phenol content was evaluated by colorimetric assay, while the color attributes were evaluated as transmittance data. Significant Pearson correlation factors were found between total polyphenol amount and antioxidant activity and between color attributes and antioxidant activity. Furthermore the chemical composition of volatile organic compounds was determined. The volatile organic compounds chemical composition of the studied honeys was mainly represented by terpene and benzene derivatives, Maillard reaction products, isoprenoids, and hydrocarbons. The volatiles fingerprint, as well as a targeted high-performance liquid chromatography analysis of the polar components, was used to tentatively confirm the declared botanical origin of the samples studied.     

Characterization of monofloral honeys with multivariate analysis of their chemical profile and antioxidant activity

Abstract: Various bioactive chemical constituents were quantified for 21 honey samples obtained at Rio de Janeiro and Minas Gerais, Brazil. To evaluate their antioxidant activity, 3 different methods were used: the ferric reducing antioxidant power, the 1,1‐diphenyl‐2‐picrylhydrazyl (DPPH) radical‐scavenging activity, and the 2,2′‐azinobis (3‐ethylbenzothiazolin)‐6‐sulfonate (ABTS) assays. Correlations between the parameters were statistically significant (?0.6684 ≤ r ≤?0.8410, P < 0.05). Principal component analysis showed that honey samples from the same floral origins had more similar profiles, which made it possible to group the eucalyptus, morrão de candeia, and cambara honey samples in 3 distinct areas, while cluster analysis could separate the artificial honey from the floral honeys. Practical Application: This research might aid in the discrimination of honey floral origin, by using simple analytical methods in association with multivariate analysis, which could also show a great difference among floral honeys and artificial honey, indicating a possible way to help with the identification of artificial honeys.     

Determination of trace and minor elements in Slovenian honey by total reflection X-ray fluorescence spectroscopy

Trace and minor elements in Slovenian honey were analysed by total reflection X-ray fluorescence spectroscopy. Upto 16 elements (K, Cl, S, P, Ca, Mn, Rb, Cu, Fe, Ni, Cr, Br, Ti, Pb, Sr and As) were detected, in a range of average content from 1.24 mg kg⁻¹ for Sr to 2590 mg kg⁻¹ for K. Statistically significant differences were established between different types of honey (acacia, floral, lime, chestnut, spruce, fir, forest and Metcalfa pruinosa honeydew honey). The highest content of elements was determined in forest honey and the lowest in acacia honey. Honeys were also separated according to their botanical origin as nectar honey or honeydew honey. Total elemental content and the content of S, Cl, K and Rb in honeydew honey was statistically significantly higher than in nectar honey. Chestnut honey differed in statistically its significantly higher contents of Rb and Ca from nectar and honeydew honeys. The year of honey production proved to have no statistically significant influence on elemental content. A comparison of our data with the literature data showed a relatively large diversity.     

中蜂与意蜂荔枝蜜挥发性成分的测定与对比分析

采用顶空固相微萃取(headspace solid phase microextraction,HS-SPME)-气相色谱质谱联用(gas chromatography-mass spectrometry,GC-MS)技术对我国不同产地采集的中蜂和意蜂荔枝蜜的挥发性成分进行检测,并分析和评价中蜂和意蜂荔枝蜜挥发性成分的差异。在优化后的试验条件下,荔枝蜜样品中共检测出98种挥发性物质,主要包括萜烯类、醇类、醛类、芳香类等,其中含量最高的是芳樟醇氧化物。对比研究发现,正庚醇、正壬醇、乙酸苯乙酯、丁香醇仅在中蜂荔枝蜜中被检出,而癸醛、环氧芳樟醇氧化物和某未知色谱峰(RT:27.98)仅在意蜂荔枝蜜中被检出,这几种差异性成分可以有效区分中蜂和意蜂荔枝蜜。     

Mineral content and electrical conductivity of the honeys produced in Northwest Morocco and their contribution to the characterisation of unifloral honeys

In this study the mineral content, ash content and electrical conductivity of 98 honey samples from Northwest Morocco were studied. Using inductively coupled plasma atomic emission spectrometry (ICP‐AES), six minerals were identified and quantified: K, Mg, Mn, Cu, Fe and Zn. Potassium was the predominant mineral (accounting for 80% of the total minerals quantified), followed by Mg and Fe (9 and 3% respectively). The ash content values were lower than 0.6% in 95 of the samples. The higher electrical conductivity values corresponded to the honeydew honeys ( x = 1734 µS cm^?1). In addition, characterisation of the main unifloral honeys by principal component analysis (PCA), linear discriminant analysis (LDA) and multilayer perceptrons (MLP) was carried out. The PCA showed that the cumulative variance was approximately 67%. On the other hand, the LDA and MLP allowed perfect classification of the honeydew (100% correct classification) and Eucalyptus (92%) honeys. © 2003 Society of Chemical Industry