Origin Of Honey Research Articles

Differential Mobility Spectrometry Authenticates the Botanical Origins of Five Monofloral Honeys.

Differential mobility spectrometry (DMS) enables the detection and separation of volatile organic compounds, exploiting differences in ion mobility under varying electric fields, which enhance the resolution of complex mixtures. In this study, DMS, upon integration with chemometrics, enabled the differentiation of the botanical origins of Italian honeys. Utilizing a benchtop differential mobility spectrometer connected to pure air, 219 monofloral honeys of five different botanical origins (acacia, chestnut, citrus, eucalyptus, and linden) were simultaneously analyzed in positive and negative ion modes and with the analysis duration of less than 2 min. Repeatability of the resultant volatilomic fingerprints was verified by cosine similarity with the subsequent elimination of the nonrepeatable data. Afterward, the data for both polarities were concatenated, and an exploratory investigation was carried out into the discrimination capabilities of DMS by principal component analysis. The merged data were then submitted to a statistical analysis for classification. The performance of the resultant random forest (RF) classifier was evaluated by repeated cross-validation and resubstitution into the training set, and finally, this classifier was validated on a withheld subset of data. The performances of the classification model for each test were evaluated by calculating the contingency table-derived parameters of accuracy, Matthew's correlation coefficient (MCC), sensitivity, and specificity. The RF classifier produced high-performance values when predicting the botanical origin of the honey in both training (MCC 84.8%, accuracy 88.0%, sensitivity 87.5%, and specificity 96.9%) and validation subsets (MCC 82.6%, accuracy 86.3%, sensitivity 85.7%, and specificity 96.5%). Some limitations, expected to be mitigated by further research, were encountered in the correct classification of acacia honey.

Identifying bee species origins of Philippine honey using X-ray fluorescence elemental analysis coupled with machine learning

Identifying bee species origins of Philippine honey using X-ray fluorescence elemental analysis coupled with machine learning

Characterization of Lacticaseibacillus rhamnosus of Honey Origin from Saudi Arabia: Potential Probiotic Strains with Antibacterial Effects

Background: We recently isolated five strains of lactic acid bacteria (LAB) from honey and identified them via 16S ribosomal RNA gene sequencing. The sequences were deposited in the NCBI GenBank database and given the corresponding accession numbers: OL539410 to OL539414. Since LAB are well known for their probiotic properties, in this study, we characterized the probiotic potential and safety of these isolates.Methods: Five LAB strains, including two strains of Lacticaseibacillus rhamnosus and three strains of Enterococcus faecium isolated from honey samples, were examined for their probiotic potential through acid and bile salt resistance assays, bacterial cell surface hydrophobicity, autoaggregation, and coaggregation assays. In addition, cell free culture supernatant antibacterial activity of mentioned LAB strains was tested against E. coli (ATCC 25922), P. aeruginosa (ATCC 27853), K. pneumoniae (ATCC 13883), S. aureus (ATCC 25923), S. epidermidis (ATCC 12228), and S. pneumoniae (ATCC 6303) Further tests, such as those measuring hemolytic activity and antibiotic susceptibility, were also performed to evaluate their safety profile.Results: All the LAB strains tolerated and survived the simulated gastrointestinal conditions: pepsin at acidic pH (3.0) and bile salt concentration (0.3%) at alkaline pH (8.0), with different levels of viability. According to antibacterial assays, L. rhamnosus (P0-2 L) and E. faecium (P0-4 L) exhibited the strongest inhibitory activities. Also, L. rhamnosus (P0-2 L) demonstrated high adhesive and aggregative properties. All strains tested negative for hemolytic activity, indicating safety for human consumption.Conclusion: This study revealed that L. rhamnosus derived from Sidr honey may be used as a potential probiotic. This study also showed that honey from different regions can contain probiotics.Keywords: Lacticaseibacillus rhamnosus, Sidr honey, lactic acid bacteria, probiotics

COI Metabarcoding as a Novel Approach for Assessing the Honey Bee Source of European Honey

Honey is a widely consumed food product frequently subjected to adulteration, with the mislabeling of its botanical or geographical origin being one of the most common practices. Determining the entomological origin of honey is particularly challenging but of high relevance for ensuring its authenticity, especially for products with protected designation of origin (PDO) labels. This study presents a novel DNA metabarcoding approach targeting a highly informative 406 bp fragment of the cytochrome c oxidase I (COI) gene to differentiate among the three major mitochondrial lineages (A, M, and C) of honey bees (Apis mellifera L.) native to Europe. The target region was selected based on the calculated fixation index (FST), which is frequently used in Population Genetics as a measure of differentiation between populations. The approach was validated with 11 honey samples of known entomological origin and applied to 44 commercial honeys from 13 countries. The approach demonstrated high sensitivity, accurately identifying the entomological origin of honey, including samples produced by honey bees of varying ancestries, which could not be resolved by previous methods based on real-time PCR coupled with high-resolution melting (PCR-HRM) analysis. The results demonstrate the effectiveness of COI metabarcoding in verifying honey authenticity and highlight the predominance of C-lineage honey bees in the production of commercial honeys from northwestern Europe. This finding suggests a limited presence of the native M-lineage ancestry, underscoring the need for conservation efforts.

Study of the botanical origin of bee honey of Ukraine

The purpose of this study was to analyze Ukrainian bee honey according to organoleptic characteristics and species composition of pollen grains, to establish their origin, species and botanical homogeneity. Methods. The study of organoleptic indicators and pollen composition of bee honey was carried out using the methods specified in DSTU 4497:2005 "Natural honey. Technical conditions". Botanical homogeneity, i.e. monofloral or polyfloral, was determined in accordance with the Order on the Approval of the Requirements for Honey (MAPPU Order No. 330 on the Approval of the Requirements for Honey, 2019). The results. 156 samples of bee honey obtained from different regions of Ukraine were analyzed. Compliance with the requirements for the quality of natural honey according to organoleptic indicators has been established. The variety of botanical origin has been specified. The study of the species composition of pollen grains proved monofloral in 33 honey samples and polyfloral in 121. In addition, according to the results of the tests, 2 samples were classified as honeydew honey. Conclusions. Our tests have confirmed the unique pollen composition of each individual bee honey, on which the organoleptic properties depend. The pollen analysis of bee honey can be used to determine not only the type of raw material, botanical composition of honey, but also its geographical origin. Given the complexity of the microscopic method of studying the species composition of pollen grains of bee honey, there is a need to find new research methods.

Exploring consumer preferences and policy implications in local food systems: Does taste or labeling matter in honey?

This study analyses the influence of geographical origin and taste on honey consumer behavior. First, we explore the influence of geographical origin on consumers’ hedonic evaluation of honey. We then assess the influence of geographical origin and taste on their willingness to pay (WTP) for honey. We conducted a field experiment at a real supermarket. The participants were exposed to two treatments (blind and informed treatment). The findings showed that knowledge about the geographical origin of honey influences consumers’ hedonic evaluations and that the WTP for honey is more strongly influenced by geographical origin than by taste.

Identification of pollen types of beekeeping interest by non‐targeted mass spectrometry

AbstractBackgroundThe identification of pollen is important in the field of beekeeping for the determination of the botanical origin of bee products and investigations of bee diet. Until now, it has been performed by melissopalynology, the microscopic examination of pollen grains. However, this technique has some limitations, such as the necessity of experienced analysts and identification restricted to the family level for some pollen types. Although many techniques have been proposed as alternatives or complements to melissopalynology and omics techniques have been explored to gather information on the botanical origin of honey, no study has yet been conducted on a large set of pollen types.ResultsThe study dataset consisted of 34 different pollen types of pellets collected by honeybees in Switzerland and analyzed in multiple biological replications, leading to 150 observations. The pollen samples were analyzed after tryptic digestion using a non‐targeted mass spectrometry‐based method. Liquid chromatography coupled with mass spectrometry (LC–MS) was employed to identify pollen, and melissopalynology was used as a reference method for the identification. We built an OPLS‐DA prediction model for the 34 pollen types. The model clearly identified new samples in their membership group (Acer sp., n = 10) and a new pollen type at the species‐specific level for Quercus sp. Less predictable results were achieved for Composita H and pollen collected directly from the plant.ConclusionThe use of a non‐targeted mass spectrometry‐based method and chemometrics resulted in a promising tool for pollen identification as a replacement/supplement method to traditional melissopalynology.

Distinguishing the botanical origins of rare honey through untargeted metabolomics and machine learning interpreting flavonoid profiles.

Distinguishing the botanic origins of monofloral honey is the foremost concern in ensuring its authentication. In this work, an innovative, green, and comprehensive approach was developed to distinguish the botanic origins of four types of rare honey, and the strategy involved in the following aspects: Based on theoretical design, suitable natural deep eutectic solvent (NADES) was screened to extract flavonoids from honey samples; after NADES extracts were directly analyzed by high-resolution mass spectrometry, the discrimination models of monofloral honey were established by untargeted metabolomics combined with machine learning. Based on the comparison of various models, the Random Forest algorithm had higher prediction accuracy for four types of monofloral honey, and characteristic compounds for each rare monofloral honey were screened based on SHapley Additive exPlanations values. This work provides a new perspective on the use of AI technology and green chemistry to control the quality of honey.

An Enhancement of Honey Encryption Algorithm Applied in Online Patient Portals

The Honey Encryption Algorithm is a novel encryption scheme designed to encrypt messages using low-entropy keys, such as passwords. When an incorrect key is used for decryption, it returns a honeyword—a fictitious message intended to mislead attackers into thinking they have accessed the correct data. However, in password-based authentication systems, a usability issue arises when legitimate users accidentally input their password in a way that results in a honeyword. This study addresses the problem of typo-safety in Honey Encryption Algorithm by employing Long Short-Term Memory (LSTM) models combined with Damerau-Levenshtein distance metric to generate honeywords that are less likely to be typed by legitimate users, while still being effective against adversarial attacks. The Enhanced Honey Encryption Algorithm achieved a typo rate of 29%, with only 4.57% of typos producing a honeyword candidate. In comparison, the original Honey Encryption Algorithm using a Take-a-Tail method had a 71% typo rate, with 27.71% of typos resulting in honeyword candidates. A password survey was conducted on 100 participants, asking them to re-enter 20 passwords under timed and environmental constraints for ecological validity. Both algorithms were tested using honeyword sets generated from a set of passwords sourced from the phpBB password list. The Enhanced Honey Encryption Algorithm’s 29% typo rate and 4.57% honeyword rate indicate a lower probability of legitimate users’ typos resulting in a honeyword, resulting in a significant improvement in usability in password-based authentication systems.

Mexican Bee Honey Identification Using Sugar Crystals’ Image Histograms

Second harmonic generation (SHG) microscopy is applied to image honey sugar crystals, with the advantage of avoiding the surrounding signal of the liquid honey. It is a non-contact method where the specialized preparation of the honey is not required. Since the crystallization process of honey depends upon its floral origin, among other conditions, analyzing its crystallographic behavior results in a challenging task as the crystals are mixed and overlapped. This work introduces a simple, multi-variable data analysis requiring only one SHG image, where its gray-level histogram was retrieved and processed from eight Mexican bee honey samples. These honey samples represent a broad range of regions from the central–east part of Mexico, describing different environments and climates. The results obtained with this simplified methodology can differentiate among the botanical origin of the honey samples at different crystallization times and storage conditions. A repeatability test was performed using three honey samples with different crystal morphologies to expand the method to all of the samples. The results show that the proposed methodology could be a helpful alternative to identifying the botanical origin of honey despite its crystallization time.

An Analysis of Longan Honey from Taiwan and Thailand Using Flow Cytometry and Physicochemical Analysis.

The increase in honey fraud in the global market has highlighted the importance of pollen analysis in determining or confirming the botanical and geographical origins of honey. Numerous studies are currently underway to develop efficient and rapid methods for the determination of the quality, botanical, and geographical origin of honey. Typically, the physicochemical analysis of honey is used to evaluate its quality and geographical source. In this study, flow cytometry, a technique extensively employed in immunology and hematology, was first applied to analyze and characterize pollen from longan honeys from Taiwan and Thailand. The flow cytometry was employed for forward scatter (FSC), side scatter (SSC), Y610-A, and NUV450 to analyze longan honey samples from Taiwan and Thailand. Taiwan's longan honeys were rich in pollens; however, based upon the FSC and SSC analyses, the pollens from Thai longan honeys were larger and more granular. The Y610/20 emission area was greatest in Thai pollens. The NUV450 measured in the near UV laser was also greater in Thai pollen. Additionally, honey samples were also analysed for physiochemical properties including moisture content, pH, ash content, viscosity, and hydroxymethylfurfural (HMF) for physiochemical properties of longan honey samples from both countries. The moisture content of honey from Taiwan varied between 20.90% and 23.40%, whereas honey from Thailand ranged from 19.50% to 23.50%. A total of 60% of Taiwan's longan honey was found to have a dark amber color, and only 20% of Thai longan honey exhibited a dark amber color. Furthermore, the pH range of honey from Taiwan was found to be between 4.00 and 4.16, and the pH of Thai honey ranged from 4.01 to 4.12. The ash content of honey samples from Taiwan ranged from 0.05% to 0.23%, and Thai honey had a range of 0.01% to 0.9%. All samples were negative for the Fiehe's test, indicating the absence of HMF. This analysis lays the groundwork for rapid identification the origins of the honey, applying flow cytometry in conjunction with physicochemical analysis to assess its quality.

Comparison of the Physicochemical Properties, Microbial Communities, and Hydrocarbon Composition of Honeys Produced by Different Apis Species.

The chemical composition and quality of honey are influenced by its botanical, geographic, and entomological origins, as well as climatic conditions. In this study, the physicochemical characteristics, microbial communities, and hydrocarbon compounds of honey produced by Apis mellifera, Apis cerana, Apis laboriosa, Apis dorsata, and Apis florea were elucidated. The physicochemical profile of the honey exhibited significant differences across species, including moisture content (18.27-23.66%), fructose (33.79-38.70%), maltose (1.10-1.93%), electrical conductivity (0.37-0.74 mS/cm), pH (3.36-3.72), diastase activity (4.50-29.97 diastase number), and color (37.90-102.47 mm). Microbial analysis revealed a significant abundance of lactic acid bacteria, particularly the Apilactobacillus genus in A. laboriosa honey and the Lactobacillus in A. florea honey, indicating significant probiotic potential. Chemometric methods, principal component analysis, hierarchical cluster analysis, and orthogonal partial least squares discriminant analysis (OPLS-DA) were used to classify the honey samples based on the 12 beeswax-derived hydrocarbons. The OPLS-DA model demonstrated 100% accuracy in predicting the entomological origin of honey, indicating that specific hydrocarbons are reliable markers for honey classification.

METHOD VALIDATION FOR DETERMINATION OF KYNURENIC ACID CONTENT IN NATURAL PRODUCTS BY RP-HPLC-UV

Kynurenic acid (KYNA) is a metabolite with pharmacoactive properties found primarily in chestnut honey, linden, and other honeys. Considering the anti-inflammatory and immunosuppressive functions of KYNA, it can be seen that it has bidirectional effects on biological pathways. For this reason, determining and knowing the amount of honey, an important natural product can have a high impact on health. Studies on the detection of this metabolite in both natural products and animal tissues are ongoing, and it is important to develop fast and easily applicable methods. Within the scope of this study, a new method was created using an ultraviolet detector (RP-HPLC-UV) in reverse-phase high-performance liquid chromatography to determine the KYNA content of natural products (honey, chestnut pollen, and chestnut flowers) quantitatively in a short time. According to the data obtained, LOD and LOQ values were found to be 0.030 µg/mL and 0.092 µg/mL, respectively. At the same time, solutions of KYNA prepared in ultrapure water (UPW), 70% EtOH, EtOH and MeOH solvents were analyzed in this method, and it was found that UPW was the best solvent. The findings of this research can contribute significantly, particularly in the application of measuring KYNA, to distinguishing the botanical origin of chestnut honey.

Low-field 1H-NMR spectroscopy allied with chemometrics for recognition of botanical origin and adulteration of honeys

Low-field 1H-NMR spectroscopy allied with chemometrics for recognition of botanical origin and adulteration of honeys

Intra-regional classification and quality evaluation of honey from Mendoza (Argentina) based on multi-elemental analysis and chemometrics

Multi-elemental analysis of honey samples from Mendoza (Argentina) was performed with the aim of developing a reliable method for tracing honey provenance. The concentrations of twenty-six elements (Li, Na, Mg, Al, Ca, Ti, V, Cr, Mn, Fe, Co, Ni, Cu, Zn, As, Se, Rb, Sr, Mo, Pd, Ag, Cd, Sn, Sb, Hg and Pb) were determined by inductively coupled plasma mass spectrometry (ICP-MS), considering the most abundant isotopes. Subsequently, a comparative machine learning approach for classification and for variable selection was applied to evaluate the possibility of using them as relevant markers to predict the region where honey was produced. Our results clearly demonstrate the potential of decision tree classifiers, such as Random Forest (RF), C5.0, recursive partitioning (rpart) and conditional inference tree (ctree), as simple and agile chemometric tools for honey origin identification. Moreover, the variable selection tools reduced the elemental data matrix to only six elements (Co, Sr, Zn, Na, Rb and Li) which were identified as the most important for predicting honey origin.

Chromatography-Based Metabolomics as a Tool in Bioorganic Research of Honey.

This review presents the latest research on chromatography-based metabolomics for bioorganic research of honey, considering targeted, suspect, and untargeted metabolomics involving metabolite profiling and metabolite fingerprinting. These approaches give an insight into the metabolic diversity of different honey varieties and reveal different classes of organic compounds in the metabolic profiles, among which, key metabolites such as biomarkers and bioactive compounds can be highlighted. Chromatography-based metabolomics strategies have significantly impacted different aspects of bioorganic research, including primary areas such as botanical origins, honey origin traceability, entomological origins, and honey maturity. Through the use of different tools for complex data analysis, these strategies contribute to the detection, assessment, and/or correlation of different honey parameters and attributes. Bioorganic research is mainly focused on phytochemicals and their transformation, but the chemical changes that can occur during the different stages of honey formation remain a challenge. Furthermore, the latest user- and environmentally friendly sample preparation methods and technologies as well as future perspectives and the role of chromatography-based metabolomic strategies in honey characterization are discussed. The objective of this review is to summarize the latest metabolomics strategies contributing to bioorganic research onf honey, with emphasis on the (i) metabolite analysis by gas and liquid chromatography techniques; (ii) key metabolites in the obtained metabolic profiles; (iii) formation and accumulation of biogenic volatile and non-volatile markers; (iv) sample preparation procedures; (v) data analysis, including software and databases; and (vi) conclusions and future perspectives. For the present review, the literature search strategy was based on the PRISMA guidelines and focused on studies published between 2019 and 2024. This review outlines the importance of metabolomics strategies for potential innovations in characterizing honey and unlocking its full bioorganic potential.

Botanical origins of honeys from pollen analysis during the main honey flow across agro-ecologies in kelala district, South Wollo, Ethiopia

This study aimed to identify the botanical origins of honey through pollen analysis across agro-ecologies of Kelala district, South Wollo, Ethiopia. Fifteen honey samples were collected from traditional beehives, with 5 samples from each of the highland, midland, and lowland agro-ecologies. Qualitative and quantitative pollen analyses revealed that 22 honeybee plants belonging to 8 families, with no families of poisonous origin, and 3 growth forms were identified. Among these plant species, 18 were found in the midland and 13 were recorded in each of the highland and lowland agro-ecologies. The family Fabaceae, with 7 (31.81 %) of the identified honeybee plants, was highly dominant (p < 0.03) compared to the other families. Herbs, with 18 plant species, were highly dominant (p < 0.001) compared to shrubs and trees. In terms of plant species diversity, 10 plant species (Bidens pachyloma, Guizotia scabra, Becium grandiflorum, Eleusine floccifolia, Lens culinaris, Lippia adoensis, Medicago polymorpha, Ocimum basilicum, Trifolium steudneri, and Zea mays) were found in more than 50 % of the honey samples, with the first 2 in all the samples studied. Analysis of each honey sample showed that 8 (53.33 %) of the samples were monofloral, 3 (20 %) were bifloral, and the rest were multifloral. However, all the honeys produced due to agro-ecology (geographical origin) were monofloral. Guizotia scabra in the highland and Bidens pachyloma in the midland and lowland agro-ecologies were the predominant pollen producing species and contributors of monofloral honey. In conclusion, the safe and healthy monofloral honey produced across agro-ecologies suggests the suitability of the honey for human consumption and can potentially attract investors.

The Use of Fluorescence Spectrometry Combined with Statistical Tools to Determine the Botanical Origin of Honeys.

At a time when the botanical origin of honey is being increasingly falsified, there is a need to find a quick, cheap and simple method of identifying its origin. Therefore, the aim of our work was to show that fluorescence spectrometry, together with statistical analysis, can be such a method. In total, 108 representative samples with 10 different botanic origins (9 unifloral and 1 multifloral), obtained in 2020-2022 from local apiaries, were analyzed. The fluorescence spectra of those samples were determined using a F-7000 Hitachi fluorescence spectrophotometer, Tokyo, Japan. It is shown that each honey variety produces a unique emission spectrum, which allows for the determination of its botanical origin. Taking into account the difficulties in analyzing these spectra, it was found that the most information regarding botanical differences and their identification is provided by synchronous cross-sections of these spectra obtained at Δλ = 100 nm. In addition, this analysis was supported by discriminant and canonical analysis, which allowed for the creation of mathematical models, allowing for the correct classification of each type of honey (except dandelion) with an accuracy of over 80%. The application of the method is universal (in accordance with the methodology described in this paper), but its use requires the creation of fluorescence spectral matrices (EEG) characteristic of a given geographical and botanical origin.

Examining the Use of Polyphenols and Sugars for Authenticating Honey on the U.S. Market: A Comprehensive Review

The rise in honey production and imports into the United States necessitates the need for robust methods to authenticate honey origin and ensure consumer safety. This review addresses the scope of honey authentication, with a specific focus on the exploration of polyphenols and sugar markers to evaluate honeys in the U.S. In the absence of comprehensive federal standards for honey in the United States, challenges related to authenticity and adulteration persist. Examining the global landscape of honey authentication research, we observed a significant gap in the literature pertaining to U.S. honeys. While honeys from Europe, Australia, New Zealand, and Asia have been extensively studied, the decentralized nature of the U.S. honey market and the lack of comprehensive standards have limited the number of investigations conducted. This review consolidates the findings of global honey studies and emphasizes the need for further research studies on honey authenticity markers within the United States. We also explore previous studies on the U.S. that focused on identifying potential markers for honey authenticity. However, the inherent variability in polyphenol profiles and the lack of extensive studies of the sugar contents of honey on a global scale pose challenges to establishing universal markers. We conclude that by addressing these challenges, the field of research on polyphenols and sugars in honey can move toward more reliable and standardized methods. This advancement will enhance the use of polyphenols and other constituents like sugars as authenticity markers, ultimately benefiting both researchers and the honey industry in ensuring honey quality.

Detection of Entomological Origin of Honey Sold in Indonesia Based on &lt;i&gt;16S rRNA&lt;/i&gt; Gene Analysis

Honey is known for its various benefits for health, cosmetic ingredients, and other industrial materials. Especially, during the Covid-19 pandemic, many people consume honey to maintain body endurance. In Indonesia, the honey produced is dominated by Apis mellifera honey. With a cheaper price and a larger quantity, A. mellifera honey is often offered as forest honey or stingless bee honey to get more profit. Therefore, this study aims to determine the entomological origin of honey claimed as forest honey and stingless bee honey sold in the Indonesian market using the detection of 16S rRNA gene amplicon. This study tested 30 samples of forest honey and 30 samples of stingless bee honey. DNA that has been isolated from honey samples was amplified by PCR using 16S rRNA primers. The results from the sequence analysis showed that nine of honey samples were identified as honey fraud. Two samples were confirmed as falsification of the origin of honey-producing bees and four honey samples were confirmed as honey mislabelling. From this study it can be concluded that, it is possible to determine the entomological origin of honey molecularly by sequencing the 16S rRNA gene. Therefore, this method can be used to identify honey fraud that may occur on the market.