Honey Authenticity Research Articles

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.

Identification of 3-methoxytyramine as a specific biomarker for beet-sugar-fed honey: A two year surveillance study in South Korea

Honey is highly vulnerable to food fraud, and there are growing concerns about product authenticity. The commonly used stable carbon isotope ratios in the Calvin (C3) and Hatch–Slack (C4) photosynthesis cycles in plant feed cannot distinguish between beet-sugar-fed honey and natural honey. However, 3-methoxytyramine (3-MT) can be used as specific biomarker for identifying adulteration of beet-sugar-fed honey. In this study, we developed an isotope dilution-liquid chromatography/tandem mass spectrometry method to quantify 3-methoxytyramine (3-MT) in beet-sugar-fed honey. Solid phase extraction was used to quantify 3-MT and to reduce the matrix interference from honey samples. The limit of quantification was 1 μg/kg. The accuracy (recovery, %) and precision (relative standard deviation) values, determined in three laboratories, were 100–114 % and 1.25–9.07, respectively. Moreover, 3-MT was detected (1.07–10.7 μg/kg) in 144 out of 3,172 samples collected from different parts of Republic of Korea over 2021 and 2022. The mean 3-MT concentration of seven honey samples fed with 100 % beet sugar was 5.32 μg/kg. The results of our study validated the accuracy and sensitivity of the proposed analytical method was established for quantification of 3-MT in honey samples. Additionally, our study demonstrated that the 3-MT as a reliable biomarker for beet-sugar-fed honey adulteration. Thus, our study presents a novel mass spectrometric approach for authentic honey discrimination with potential application in honey authenticity testing.

Precision apiculture system: honey traceability from hive to spoon

Honey is one of the most adulterated food substances and is also prone to mislabeling and fraudulent mixing with low-quality honey. Due to this, severe health issues and economic losses to authentic beekeepers have been reported. Though several technologies and systems have been applied in different parts of the world to achieve complete transparency in the honey traceability mechanism, honey adulteration remains a pressing issue among its various stakeholders. This article discusses various issues that arise due to fraudulent honey, different honey traceability systems implemented across the world, drawbacks of the existing systems and a novel app “Sumadhu” that has been developed to serve as a comprehensive honey traceability system based on data science, machine learning, GPS, across the honey supply chain to track the honey right from its harvest from the bee hive to the spoon of the end consumer in a methodical manner. The Sumadhu app was applied in India and data were collected at various supply chain stages through the app and analyzed to assess impacts on traceability, stakeholder connectivity, economic benefits, operational efficiency, etc. The Sumadhu app addresses critical issues in honey traceability by using data science, GPS, blockchain, and machine learning. The app’s potential for scalability and integration with government systems positions it as a groundbreaking solution for global honey traceability challenges. This study emphasizes the importance of developing advanced traceability systems to ensure the purity and authenticity of honey, benefiting all stakeholders and providing critical information to government entities for better industry regulation.

The Melliferous Potential of the Mountainous Region of Azad Kashmir, Pakistan: Pollen Profiling of Honey Using Microscopy.

Pollen content analysis of honey provides insight into the diversity of pollen grains and the development of a seasonal calendar to identify the diversity and availability of melliferous flora around the year. Melissopalynology is the most primitive and widely used technique for the qualitative and quantitative pollen profiling of honey. The honey of Azad Kashmir Pakistan has never been analyzed for pollen content despite the production at an industrial scale. A total of 60 samples were analyzed for the types and frequency of melliferous flora by the use of light microscopy and scanning electron microscopy. A wide diversity in morphological features differentiated 92 plant species belonging to 48 families. The most frequent plant families observed were Fabaceae, Poaceae, Asteraceae, Myrtaceae, Rosaceae, Betulaceae, and Buxaceae. Asteraceae showed the maximum species contribution. The obtained percentages of representative pollens were classified in frequency classes as follows: D: Predominant pollen (45%), S: Secondary Pollen (15%-45%), I: Important minor pollen (3%-15%), and M: Minor pollen (1%-3%). Most of the samples were multifloral containing not one dominant pollen, while one was bifloral containing two dominant pollen types and one was unifloral containing only one dominant pollen. The results reflect the melliferous potential of the native flora in the region. Indigenous floral resources from tropical vegetation to alpine meadows sustain bee colonies even during dearth periods. This work will benefit consumers, beekeepers, and regulatory bodies to maintain the authenticity of honey by the provenance of geographical and botanical origin.

Fluorescence spectroscopy combined with multilayer perceptron deep learning to identify the authenticity of monofloral honey—Rape honey

Honey authenticity is critical to honey quality. The development of a quick, easy, and non-destructive technique for determining the authenticity of honey encourages an improvement in honey quality. Here, the authenticity of monofloral honey—rape honey was determined using fluorescence spectroscopy combined with multilayer perceptron (MLP) deep learning, without the need for any prior feature extraction or pre-processing. A total of 91 raw fluorescence intensity data of the real and adulterated honey samples at a fixed excitation wavelength of 280 nm were first matrixed, and all data were then categorized into a training set, a validation set, and a test set with numbers of 64, 16, and 11, respectively. The connection with dropout was selected to build and link the MLP internal network. The activation function, learning rate, optimizer, and number of epochs were among the hyperparameters of the MLP neural network that were tuned. A good MLP deep learning network model for determining the authenticity of monofloral honey, rape honey, was developed after constant validation and debugging. According to the accuracy curve of the MLP model, the accuracy of the training set increased with the number of epochs and eventually converged to 100 %, while the accuracy of the validation set could be well stabilized at about 100 % after 5000 epochs. Finally, the accuracy of the MLP model on the test set was close to 100 %. According to our findings, the MLP neural network and fluorescence intensity have great potential applications in identifying the authenticity of honey.

Comprehensive honey analysis: A novel HPLC-DAD method for hydroxymethylfurfural quantification and quality evaluation via diastase activity and sugar profile determination

Ensuring the authenticity and quality of honey is essential to safeguarding consumer health and combating food fraud. The primary analyses for honey quality involve determining diastase activity (DA), 5-hydroxymethylfurfural (HMF), and carbohydrate composition. A new high-performance liquid chromatography coupled with diode array detection (HPLC-DAD) method was developed, for the precise quantification of HMF, a key marker of honey quality and degradation. Utilizing a Phenomenex Kinetex RP-18 column (5 µm, 150 × 4.6 mm i.d.), with a mobile phase containing water and methanol (95:5, v/v) in gradient mode, at a flow rate of 1 mL/min, and UV detection at 284 nm, the method achieved rapid and efficient separation within 10 minutes, with a low detection limit of 0.03 mg L−1, and demonstrated good linearity, precision and recovery rates. The carbohydrate composition of honey, specifically glucose, fructose, saccharose, and maltose, was analysed using a HPLC with refractive index detection (HPLC-RI) method, using a Phenomenex Luna Omega Sugar column (3 µm, 250 ×4.6 mm i.d.), with a mobile phase composed of acetonitrile and water (85:15, v/v). This analytical protocol, incorporating the quantification of maltose, a critical yet often overlooked sugar, significantly bolsters the standard evaluation framework for honey authenticity. DA, a crucial enzymatic indicator, was assessed via the Schade method, providing additional insights into the enzymatic integrity and freshness of the honey samples. The methods were applied to analyse 65 commercial Spanish honey samples, revealing significant compliance with EU regulatory standards, yet also uncovering situations of potential adulteration. This research adds to the ongoing discussions on food safety by offering improved and dependable techniques for thoroughly assessing honey samples.

Comprehensive honey authentication in Bangladesh: Profiling physicochemical and bioactive compounds to distinguish floral sources and detect adulteration

This study aims to advance honey authentication in Bangladesh by developing a reliable, cost-effective, and user-friendly system capable of distinguishing floral sources and purity. We analyzed various physicochemical parameters and bioactive compounds in honey samples from diverse floral sources across Bangladesh during different floral seasons, including deliberately adulterated samples. Results showed clear distinctions among the tested parameters. Notably, moisture content exhibited considerable variability, with the highest levels found in kholisha (24.07%–24.99 %) and the lowest in black cumin honey (17.80%–27.46 %). Jujube honey exhibited the highest protein content at 10 % adulteration, whereas mustard honey showed the least at 20 % adulteration. Generally, adulterated honey samples showed a considerable deviation in protein content compared to known standards for pure honey, suggesting adulteration significantly impacts protein levels. Black cumin honey showed the highest electrical conductivity, followed by jujube. Antioxidant levels were highest in litchi honey, followed by ayurvedic and kholisha. Multivariate analysis revealed that two principal components explained 65 % of total variances and could separate the various clusters of honey samples. The pH values, total phenolic, total flavonoid, protein content, electrical conductivity, color intensity at 360 nm and 635 nm, and antioxidant properties of all tested honey samples showed a strong positive correlation. Overall, the manuscript strategically combines experimental findings with detailed analyses to explore the complex nature of honey composition, adulteration issues, and the significance of authenticating honey to maintain quality and ensure consumer trust in the market. This study is the first to employ a comprehensive set of physicochemical and bioactive indicators with multivariate analysis for honey authentication in Bangladesh, offering a novel and systematic approach to identifying purity and floral sources.

Pollen morphology and its taxonomic significance in the genus euphorbia L. (Euphorbiaceae), from northern Pakistan: Insights for honeybees‐plant interactions

AbstractEuphorbia L. includes 2055 species with native range of this genus being cosmopolitan. In the present study, we assessed pollen morphology of 18 species of this genus occurring mostly in northern Pakistan. Pollen morphology of Euphorbia species is poorly investigated for its taxonomic significance. Here, we examined quantitative characters, including polar diameter, equatorial diameter, P/E ratio. Exine thickness, colpi length, and width were also examined. Among the qualitative characters, pollen shape, type, exine sculpturing, and aperture features were determined. The main aim of this study is to determine and investigate the foraging behavior of the honeybees that interact with Euphorbia species. The current finding will be helpful in melissopalynological analysis for developing beekeeping practices and can be used in the authentication of honey. Pollen quantitative traits applied through a multivariate analysis and diverse sampling also improve botanical and geographical characterization of a particular region. In addition, it is concluded that some palynomorphs of Euphorbia species may contain secondary compounds and can promote signs of toxicity in honeybees. However, the beekeepers wrongly correlate the fact that milky latex from Euphorbia taxa has burning and skin irritation effects on other animals, particularly humans, with honeybee deaths, which might be due to other factors. Our observations indicate with the sense that actual forage plants may not poison their pollinators and honeybees might occasionally be dead due to unidentified factors. We have suggested that beekeepers should attempt to identify the root causes for occasional bee deaths in the area.

Rapid Convolutional Algorithm for the Discovery of Blueberry Honey Authenticity Markers via Nontargeted LC-MS Analysis.

Bees produce honey through the collection and transformation of nectar, whose botanical origin impacts the taste, nutritional value, and, therefore, the market price of the resulting honey. This phenomenon has led some to mislabel their honey so that it can be sold at a higher price. Metabolomics has been gaining popularity in food authentication, but rapid data mining algorithms are needed to facilitate the discovery of new authenticity markers. A nontargeted high-resolution liquid chromatography-mass spectrometry (HR/LC-MS) analysis of 262 monofloral honey samples, of which 50 were blueberry honey, was performed. Data mining methods were demonstrated for the discovery of binary single-markers (compound was only detected in blueberry honey), threshold single-markers (compound had the highest concentration in blueberry honey), and interval ratio-markers (the ratio of two compounds was within a unique interval in blueberry honey). A novel convolutional algorithm was developed for the discovery of interval ratio-markers, which trained 14× faster and achieved a 0.2 Matthews correlation coefficient (MCC) units higher classification score than existing open-source implementations. The convolutional algorithm also had classification performance similar to that of a brute-force search but trained 1521× faster. A pipeline for shortlisting candidate authenticity markers from the LC-MS spectra that may be suitable for chemical structure identification was also demonstrated and led to the identification of niacin as a blueberry honey threshold single-marker. This work demonstrates an end-to-end approach to mine the honey metabolome for novel authenticity markers and can readily be applied to other types of food and analytical chemistry instruments.

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.

Анализ отношений стабильных изотопов легких элементов в отдельных компонентах мёда

Honey possesses excellent sensory and nutritional properties, which makes it a valuable food ingredient. However, the same qualities make it one of the most often adulterated products in the world. Constant violations of processing technology threaten the beekeeping industry. In this regard, new authenticity criteria are a popular area of honey studies. The article introduces a method for establishing ratios of light stable isotopes in honey. The study featured 36 samples of honey of various geographical origins and botanical profiles, as well as five samples of sugar syrups from various raw materials. The quantitative profiles were obtained using a Delta Advantage V isotope mass spectrometer (USA – Germany) with additional Flash IRMS and Conflo IV modules. The experiment involved the ratios of stable carbon isotopes in honey δ13C (gross) and nitrogen δ15N in its protein fraction, as well as the values of δ13C, δ18O, and δ2H of ethanol isolated from fermented honey. The values of δ13C (gross) and δ13C of honey proteins made it possible to calculate the amount of added sugar of corn and cane origin. Exogenous sugars in the amounts of 6.5% and 18% cane sugar were detected in two samples. Isotope mass spectrometry was able to identify honey samples with exogenous sugars of C4-plant origin. However, the method failed to detect sugar-containing substances from C3-type plants. The δ18O index demonstrated some prospects as an identification criterion for sugars from C3-plants in honey. Nitrogen isotope ratios in honey proteins proved to be an efficient tool for determining honey authenticity and an additional criterion for identifying bee products. The research resulted in a patent (RU2809285C1) for a new method of determining exogenous sugar-containing substances in honey

HS-SPME-GC-MS Analysis of the Volatile Composition of Italian Honey for Its Characterization and Authentication Using the Genetic Algorithm

Honey’s chemical and sensory characteristics depend on several factors, including its botanical and geographic origins. The consumers’ increasing interest in monofloral honey and honey with a clear indication of geographic origin make these types of honey susceptible to fraud. The aim was to propose an original chemometric approach for honey’s botanical and geographic authentication purposes. The volatile fraction of almost 100 Italian honey samples (4 out of which are from Greece) from different regions and botanical origins was characterized using HS-SPME-GC-MS; the obtained data were combined for the first time with a genetic algorithm to provide a model for the simultaneous authentication of the botanical and geographic origins of the honey samples. A total of 212 volatile compounds were tentatively identified; strawberry tree honeys were those with the greatest total content (i.e., 4829.2 ng/g). A greater variability in the VOCs’ content was pointed out for botanical than for geographic origin. The genetic algorithm obtained a 100% correct classification for acacia and eucalyptus honeys, while worst results were achieved for honeydew (75%) and wildflower (60%) honeys; concerning geographic authentication, the best results were for Tuscany (92.7%). The original combination of HS-SPME-GC-MS analysis and a genetic algorithm is therefore proposed as a promising tool for honey authentication purposes.

Multimodal Interference-Based Fiber Optic Sensors for Glucose and Moisture Content Detection in Honey

Fiber optic sensors (FOSs) have transformed industrial applications with their high sensitivity and precision, especially in real-time monitoring. This study presents a fiber optic sensor based on multimodal interference (MMI) applied to detect honey adulteration. The sensor is built using a non-core multimode fiber (NC-MMF) segment spliced between two standard single-mode fibers (SMFs). We focus on reporting the detection of two main adulterants in honey that modify its refractive index (RI): the presence of glucose and moisture content. Detailed testing was performed with two commercially approved honey brands, named A and B. The sensor successfully detected glucose concentrations from 1% to 5% and moisture content from 0% to 20% for both brands. For glucose detection, we obtained sensitivity values −0.55457 nm/% for brand A and −2.61257 nm/% for brand B. In terms of moisture content in honey, we observed a sensitivity around −0.3154 nm/% and −0.3394 nm/% for brands A and B, respectively. Additionally, temperature tests were performed, showing that the sensor works optimally up to 30 °C. The results were validated using a conventional refractometer, showing a close agreement with the data obtained and confirming the reliability and accuracy of the proposed sensor. Compared to other refractometers, the MMI sensor offers advantages such as real-time monitoring, ease of assembly, cost-effectiveness, and minimal maintenance. Furthermore, the sensor represents an alternative tool to guarantee the quality and authenticity of honey, overcoming the limitations of conventional measurement techniques.

Detection of sugar syrup adulteration in UK honey using DNA barcoding

Honey is a valuable and nutritious food product, but it is at risk to fraudulent practices such as the addition of cheaper syrups including corn, rice, and sugar beet syrup. Honey authentication is of the utmost importance, but current methods are faced with challenges due to the large variations in natural honey composition (influenced by climate, seasons and bee foraging), or the incapability to detect certain types of plant syrups to confirm the adulterant used. Molecular methods such as DNA barcoding have shown great promise in identifying plant DNA sources in honey and could be applied to detect plant-based sugars used as adulterants. In this work DNA barcoding was successfully used to detect corn and rice syrup adulteration in spiked UK honey with novel DNA markers. Different levels of adulteration were simulated (1 – 30%) with a range of different syrup and honey types, where adulterated honey was clearly separated from natural honey even at 1% adulteration level. Moreover, the test was successful for multiple syrup types and effective on honeys with different compositions. These results demonstrated that DNA barcoding could be used as a sensitive and robust method to detect common sugar adulterants and confirm syrup species origin in honey, which can be applied alongside current screening methods to improve existing honey authentication tests.

Application of Spatial Offset Raman Spectroscopy (SORS) and Machine Learning for Sugar Syrup Adulteration Detection in UK Honey.

Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain.

Mass Spectrometry Characterization of Honeydew Honey: A Critical Review.

Honeydew honey is produced by bees (Apis mellifera) foraging and collecting secretions produced by certain types of aphids on various parts of plants. In addition to exhibiting organoleptic characteristics that distinguish them from nectar honey, these honeys are known for their functional properties, such as strong antioxidant and anti-inflammatory activities. Despite their importance, they remain poorly characterized in comparison with flower honeys, as most studies on this subject are not only carried out on too few samples but also still focused on traditional chemical-physical parameters, such as specific rotation, major sugars, or melissopalynological information. Since mass spectrometry has consistently been a primary tool for the characterization and authentication of honeys, this review will focus on the application of these methods to the characterization of the minor fraction of honeydew honey. More specifically, this review will attempt to highlight what progress has been made so far in identifying markers of the authenticity of the botanical and/or geographical origin of honeydew honeys by mass spectrometry-based approaches. Furthermore, strategies devoted to the determination of contaminants and toxins in honeydew honeys will be addressed. Such analyses represent a valuable tool for establishing the level of food safety associated with these products. A critical analysis of the presented studies will identify their limitations and critical issues, thereby describing the current state of research on the topic.

Composition of Proteins Associated with Red Clover (Trifolium pratense) and the Microbiota Identified in Honey.

The nutritional composition of honey is determined by environmental conditions, and botanical and geographical origin. In addition to carbohydrates, honey also contain pollen grains, proteins, free amino acids, and minerals. Although the content of proteins in honey is low, they are an important component that confirms the authenticity and quality of honey; therefore, they became a popular study object. The aim of the study was to evaluate protein content and composition of monofloral red clover and rapeseed honey collected from five different districts of Lithuania. Forty-eight proteins were identified in five different origin honey samples by liquid chromatography. The number of red clover proteins identified in individual honey samples in monofloral red clover honey C3 was 39 in polyfloral honey S22-36, while in monofloral rapeseed honey S5, S15, and S23 there was 33, 32, and 40 respectively. Aphids' proteins and lactic acid bacteria were identified in all honey samples tested. The linear relationship and the strongest correlation coefficient (r = 0.97) were determined between the content of Apilactobacillus kunkeei and Apilactobacillus apinorum, as well as between the number of faba bean (Vicia faba) pollen and lactic acid bacteria (r = 0.943). The data show a strong correlation coefficient between the amount of lactic acid and aphid protein number (r = 0.693). More studies are needed to evaluate the relationship between the pollination efficiency of red clover by bees and the multiplicity of red clover proteins in honey protein, as well as microbiota diversity and the influence of nature or plant diversity on the occurrence of microbiota in honey.

Detection and Identification of Honey Pollens by YOLOv7: A Novel Framework toward Honey Authenticity

Detection and Identification of Honey Pollens by YOLOv7: A Novel Framework toward Honey Authenticity

Impact of processing steps (filtration, creaming and pasteurization) on the botanical classification of honey using LC-QTOF-MS

The present study investigated the impact of filtration, creaming and pasteurization on the authentication of the botanical origin of honey using the dilute-and-shoot method in liquid chromatography coupled to mass spectrometry (LC-MS). The analytical method performances were satisfactory (analyte recoveries ranging from 95 % to 103 % and inter-day precision below 12 %). Three types of raw honeys including blueberry, canola and clover were processed under controlled conditions. Filtration, creaming and pasteurization had no impact on honey botanical classification based on the LC-MS fingerprint, and the key molecular fingerprints were retained after processing. However, results revealed that testing the impact of processing is essential when selecting honey authenticity markers because some candidates (e.g. adenosine) are not stable or can be removed during honey processing. The results of the present study also highlighted the suitability of the dilute-and-shoot approach to both develop authentication tools for honey and study the impact of processing methods on specific chemicals in honeys.

Rapid in situ identification of honey authenticity based on RP-Nano-ESI-MS using online desalting

High-content sugar in honey frequently results in severe matrix effects and requires complex pretreatment prior to analysis, posing significant challenges for the rapid analysis of honey. In this study, the reversal polarity nano-electrospray ionization mass spectrometry (RP-Nano-ESI-MS) analysis was developed for the direct evaluation of honey samples. The results indicated that RP-Nano-ESI-MS significantly mitigated the matrix effects induced by high-content sugar through the implementation of online desalting. Furthermore, RP-Nano-ESI-MS has been proven capable of not only differentiating acacia honey adulterated with 10% rape honey, but also effectively distinguishing six types of honey and exhibiting remarkable proficiency in detecting honey adulteration and botanical traceability. Additionally, RP-Nano-ESI-MS exhibited strong quantitative abilities, effectively characterizing variations in amino acid composition among six types of honey with high stability and reproducibility. Our studies underscore the significant potential of RP-Nano-ESI-MS for its rapid in situ analysis of sugar-rich foods like honey, especially in their authenticity verification.