The Toxic Impact of Honey Adulteration: A Review

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

Stingless bee honey is emerging as a superfood, given its enhanced health and therapeutic benefits. In this paper, we used handheld X-ray fluorescence spectroscopy (hXRF) with machine learning techniques to classify Philippine honey based on its entomological origin. Honey samples from three different bee species were analyzed, specifically European honeybee (Apis mellifera), Philippine giant honeybees (Apis breviligula and Apis dorsata), and Philippine stingless bee (Tetragonula biroi). Random forest and logistic regression models were used on the hXRF dataset for entomological origin classification. The optimized random forest model classified entomological origin with 85.2 % (225 out of 264) overall accuracy. The logistic regression model confirmed the entomological origin of Philippine stingless bees with 94.1 % accuracy and 100.0 % specificity. As such, honey that passes this model's test is undoubtedly made by Philippine stingless bees, making it an excellent screening tool for authenticating Philippine stingless bee honey.

Robust DEEP heterogeneous ensemble and META-learning for honey authentication

Food fraud raises significant concerns to consumer health and economic integrity, with the adulteration of honey by sugary syrups representing one of the most prevalent forms of economically motivated adulteration. This study presents a novel framework that combines data from multiple analytical techniques with specialized deep learning models (convolutional neural networks), integrated via meta-learning, in order to differentiate between pure honey and samples adulterated with sugar cane molasses, glucose syrup, or caramel-flavored ice cream topping. Unlike traditional chemometric methods, this approach expands the input feature space, leading to enhanced predictive performance. The resulting deep heterogeneous ensemble learner exhibited considerable generalization capability, achieving an average classification accuracy of 98.53 % and a Matthews correlation coefficient of 0.9710. Furthermore, the ensemble demonstrated exceptional robustness, maintaining an accuracy of 73 %, even when 90 % of the input data were corrupted, underscoring its unparalleled capacity to generalize under both subtle and extreme data variability. This adaptable and scalable solution underscores the transformative potential of ensemble-meta-learning strategy for addressing complex challenges in analytical chemistry. The model, its constituents and other additional resources were made available in an open repository.

Honey Differentiation Using Infrared and Raman Spectroscopy Analysis and the Employment of Machine-Learning-Based Authentication Models

Due to rising concerns regarding the adulteration and mislabeling of honey, new directives at the European level encourage researchers to develop reliable honey authentication models based on rapid and cost-effective analytical techniques, such as vibrational spectroscopies. The present study discusses the identification of the main vibrational bands of the FT-Raman and ATR-IR spectra of the most consumed honey varieties in Transylvania: acacia, honeydew, and rapeseed, exposing the ways the spectral fingerprint differs based on the honey's varietal-dependent composition. Additionally, a pilot study on honey authentication describes a new methodology of processing the combined vibrational data with the most efficient machine learning algorithms. By employing the proposed methodology, the developed model was capable of distinguishing honey produced in a narrow geographical region (Transylvania) with an accuracy of 85.2% and 93.8% on training and testing datasets when the Trilayered Neural Network algorithm was applied to the combined IR and Raman data. Moreover, acacia honey was differentiated against fifteen other sources with a 87% accuracy on training and testing datasets. The proposed methodology proved efficiency and can be further employed for label control and food safety enhancement.

Honey as a Natural Antimicrobial

Honey, a natural product with a rich history of medicinal use, has gained increasing recognition for its potent antimicrobial properties, particularly against antibiotic-resistant pathogens. This review focuses on the antimicrobial mechanisms of honey, including its efficacy against resistant bacteria, such as Methicillin-resistant Staphylococcus aureus and Pseudomonas aeruginosa. The antimicrobial action of honey is multifactorial, involving hydrogen peroxide production, phenolic compounds, high sugar concentrations, and the presence of bee defensin-1. The composition of honey varies based on its floral source, which can influence its antimicrobial strength. Certain types, such as Manuka honey, are particularly effective in clinical applications due to their higher levels of bioactive compounds. Honey has also been shown to disrupt bacterial biofilms, a major factor in antibiotic resistance, enhancing its therapeutic potential in treating chronic wounds and infections, especially in patients with compromised immune systems. Moreover, honey's ability to improve wound healing, reduce inflammation, and promote tissue regeneration highlights its broad therapeutic profile. As antibiotic resistance continues to challenge modern healthcare, honey offers a promising complementary treatment in antimicrobial therapy. Research into its specific bioactive components and potential synergistic effects with other natural agents, like ginger and propolis, could expand its applications. Standardizing honey products for medical use and establishing clinical guidelines are essential for optimizing its therapeutic benefits. As scientific understanding of honey's antimicrobial mechanisms deepens, its integration into healthcare systems as an adjunct therapy is expected to increase, offering a natural and effective alternative in the fight against infectious diseases.

Biofortification of Acacia and Polyflower Honey with Pine sylvestris L. Bud Extracts: Exploring Antioxidant Variation Across Developmental Stages for Enhanced Nutritional Value

Honey is a valuable natural product with antioxidant properties, and its quality is influenced by various factors, including botanical origin and biofortification. Pine bud extracts, known for their antioxidant capacity, were explored to enhance the properties of acacia and polyflower honey. This study aimed to investigate the effect of pine bud extracts at different maturation stages on the moisture content, dry matter, antioxidant activity, and total phenolic content (TPC) of acacia and polyflower honey. Acacia and polyflower honey were biofortified with pine bud extracts at three maturation stages (Stage I, Stage II, and Stage III). Various analyses were performed, including moisture and dry matter content determination, total phenolic content measurement using the Folin-Ciocalteu method, and antioxidant activity assessment through DPPH radical scavenging. FTIR analysis was used to study the chemical composition of the biofortified honeys. Results showed that Acacia and polyflower honey maintained moisture content below 20%, with biofortification significantly enhancing their antioxidant profiles. The highest total phenolic content (247 ± 0.04 mg GAE/100 g) and DPPH scavenging activity (55 ± 0.05%) were observed in Acacia honey biofortified at maturation stage II. Polyflower honey exhibited increased phenolic content (232.9 ± 0.9 mg GAE/100 g) and antioxidant activity (52.45%) when biofortified at maturation stage II. Significant color changes were observed in polyflower honey, with a* and b* values increasing, indicating darker coloration. The biofortification of honey with pine bud extracts enhances its antioxidant and nutritional profile. This approach holds potential for the production of functional foods with improved health benefits. Further studies should explore its commercial feasibility and other potential bioactive compounds.

Rapid Authentication of Intact Stingless Bee Honey (SBH) by Portable LED-Based Fluorescence Spectroscopy and Chemometrics

Indonesian stingless bee honey (SBH) of Geniotrigona thoracica is popular and traded at an expensive price. Brown rice syrup (RS) is frequently used as a cheap adulterant for an economically motivated adulteration (EMA) in SBH. In this study, authentic Indonesian Geniotrigona thoracica SBH of Acacia mangium (n = 100), adulterated SBH (n = 120), fake SBH (n = 100), and RS (n = 200) were prepared. In short, 2 mL of each sample was dropped directly into an innovative sample holder without any sample preparation including no dilution. Fluorescence intensity was acquired using a fluorescence spectrometer. This portable instrument is equipped with a 365 nm LED lamp as the fixed excitation source. Principal component analysis (PCA) was calculated for the smoothed spectral data. The results showed that the authentic SBH and non-SBH (adulterated SBH, fake SBH, and RS) samples could be well separated using the smoothed spectral data. The cumulative percentage variance of the first two PCs, 98.4749% and 98.4425%, was obtained for calibration and validation, respectively. The highest prediction accuracy was 99.5% and was obtained using principal component analysis-linear discriminant analysis (PCA-LDA). The best partial least square (PLS) calibration was obtained using the combined interval with R2cal = 0.898 and R2val = 0.874 for calibration and validation, respectively. In the prediction, the developed model could predict the adulteration level in the adulterated honey samples with an acceptable ratio of prediction to deviation (RPD) = 2.282, and range error ratio (RER) = 6.612.

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.

Honey Adulteration Detection via Ultraviolet-Visible Spectral Investigation Coupled with Chemometric Analysis

Any change in the composition or physicochemical parameters of honey outside the standardized intervals may be deemed fraud, irrespective of direct introduction of certain substances or feeding honeybees with syrups. Simple and rapid tools along with more sophisticated ones are required to monitor fraudulent practices in the honey trade. In this work, UV-Vis spectroscopy was used to identify and quantify six Romanian honey types (five monofloral and one polyfloral) mixed with commercially available corn syrup, corn syrup with plant extracts, inverted syrup, and fruit syrup at different concentrations (5%, 10%, 20%, 30%, 40%, and 50%). Relevant spectral features were used to develop a neural model, which was able to pinpoint adulteration, regardless of the honey and adulterant type. The proposed model was able to detect adulteration levels higher than 10%, thereby serving as a cost-effective and reliable tool to monitor honey quality.

Comparative study of physiochemical properties in Iranian multi-floral honeys: Local vs. commercial varieties

Iran, a leading honey-producing country, faces challenges in honey exports. This study aimed to evaluate the melissopalynological and physicochemical characteristics of local honeys belonging to Iranian flora, and compare them with Iranian commercial honeys. For this purpose, seven local honey samples were collected from Iran's renowned floristic regions, alongside seven commercial multi-floral honeys from a supermarket. Moisture content (MC), total solids (TS), pH, free acidity (FA), ash, electrical conductivity (EC), sugar profile, hydroxymethyl furfural (HMF), diastase number (DN), and proline were assessed. The sugar profile was analyzed by high-performance liquid chromatography with a refractive index detector (HPLC-RID). Pollen analysis classified local honeys as multi-floral. The results revealed that MC, TS, pH, FA, ash, and EC values in both local and commercial samples conformed to approved standards (Codex Alimentarius and European Union). Two local and one commercial sample exclusively satisfied the sucrose standard level. Of the local honeys, two samples complied with HMF standard, while five fulfilled DN criterion, and four had proline values within acceptable ranges. Conversely, HMF (56.32-228.11 mg/kg), DN (3.13-7.22 Schade units/g), and proline (109.84-173.86 mg/kg) levels in all commercial samples failed to meet the standard. A significant correlation was found between ash and EC (r = 0.915, p < 0.01) in local honeys, whereas no strong correlation (r = 0.299) existed in commercial samples. Hierarchical cluster analysis confirmed that Iranian honeys lacked clustering by botanical origin, possibly due to extensive sugar adulteration or thermal treatment. Overall, study findings confirmed the significantly inferior quality of Iranian commercial honeys compared to local varieties, albeit some local samples also exhibited quality concerns. Accordingly, it is recommended that regulatory bodies provide periodic training for beekeepers and establish monitoring programs to enhance honey quality, thereby boosting Iran's share in the global honey export market.

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.

Food authenticity and the interactions with human health and climate change

Food authenticity and fraud, as well as the interest in food traceability have become a topic of increasing interest not only for consumers but also for the research community and the food manufacturing industry. Food authenticity and fraud are becoming prevalent in both the food supply and value chains since ancient times where different issues (e.g., food spoilage during shipment and storage, mixing decay foods with fresh products) has resulted in foods that influence consumers health. The effect of climate change on the quality of food ingredients and products could also have the potential to influence food authenticity. However, this issue has not been considered. This article focused on the interactions between consumer health and the potential effects of climate change on food authenticity and fraud. The role of technology and development of risk management tools to mitigate these issues are also discussed. Where applicable papers that underline the links between the interactions of climate change, human health and food fraud were referenced.

Detection of honey adulteration using machine learning

Honey adulteration is a growing concern due to its health benefits and high nutritional content. Traditional methods like Melissopalynology are ineffective in detecting adulterated honey. This research presents a comparative study of machine learning algorithms for detecting adulteration in honey. The study uses hyperspectral imaging, a promising tool for food quality assurance, to classify and predict adulteration in honey. The proposed model relies on hyper-spectrum images and improves the accuracy of existing models using hyperparameter tuning. The dataset used includes segmented and pre-processed hyperspectral images of adulterated honey samples. The study found that machine learning and hyperspectral imaging can accurately identify if honey has been adulterated, with over 98% classification accuracy. The results showed that between 5% and 10% of adulterated honey samples are misclassified, with C1 Clover honey being the most frequently misclassified. This study aims to develop an efficient and accurate honey counterfeit detection technology using machine learning technologies such as Artificial Neural Networks (ANN), Support-vector machines (SVM), K Nearest Neighbors, Random Forests, and Decision trees. The proposed model relies on hyper-spectrum images and overcomes generalization to unknown honey types of problems. The dataset used includes segmented and pre-processed hyperspectral images of adulterated honey samples from seven different brands with 12 different botanical origin labels. Feature reduction techniques, such as feature ranking-based feature selection, and autoencoder techniques are employed to classify the botanical origins of honey. The model parameters are enhanced or tuned by the training process, and hyperparameters are adjusted by running the whole training data. The researchers used Python, and well-known algorithms like ANN, SVM, KNN, random forests, and decision trees. The results show that machine learning and hyperspectral imaging can accurately identify if honey has been adulterated, with over 98% classification accuracy.

Effects of Artificial Sugar Supplementation on the Composition and Nutritional Potency of Honey from Apis cerana

In the global apiculture industry, reward feeding and supplementary feeding are essential for maintaining bee colonies. Beekeepers provide artificial supplements to their colonies, typically in the form of either a honey-water solution or sugar syrup. Owing to cost considerations associated with beekeeping, most beekeepers opt for sugar syrup. However, the effects of different types of artificial sugar supplements on bee colonies and their subsequent impact on honey composition remain unclear. To address this gap, this study compared the chemical composition, antioxidant capacity, and nutritional potency of three types of honey: honey derived from colonies fed sugar syrup (sugar-based product, SP) or a honey-water solution (honey-sourced honey, HH) and naturally sourced honey (flower-sourced honey, FH), which served as the control. The results revealed that FH outperformed HH and SP in terms of total acidity, sugar content, total protein content, and antioxidant capacity, and HH outperformed SP. Regarding nutritional efficacy, including the lifespan and learning and memory capabilities of worker bees, FH exhibited the best outcomes, with no significant differences observed between HH and SP. This study underscores the importance of sugar source selection in influencing honey quality and emphasizes the potential consequences of substituting honey with sugar syrup in traditional apiculture practices.

Development of a zebrafish model for toxicity evaluation of adulterated Apis mellifera honey

Since ancient times, honey has been used for medical purposes and the treatment of various disorders. As a high-quality food product, the honey industry is prone to fraud and adulteration. Moreover, limited experimental studies have investigated the impact of adulterated honey consumption using zebrafish as the animal model. The aims of this study were: (1) to calculate the lethal concentration (LC50) of acid-adulterated Apis mellifera honey on embryos, (2) to investigate the effect of pure and acid-adulterated A. mellifera honey on hatching rate (%) and heart rate of zebrafish (embryos and larvae), (3) to elucidate toxicology of selected adulterated honey based on lethal dose (LD50) using adult zebrafish and (4) to screen the metabolites profile of adulterated honey from blood serum of adult zebrafish. The result indicated the LC50 of 31.10 ± 1.63 (mg/ml) for pure A. mellifera honey, while acetic acid demonstrates the lowest LC50 (4.98 ± 0.06 mg/ml) among acid adulterants with the highest mortality rate at 96 hpf. The treatment of zebrafish embryos with adulterated A. mellifera honey significantly (p ≤ 0.05) increased the hatching rate (%) and decreased the heartbeat rate. Acute, prolong-acute, and sub-acute toxicology tests on adult zebrafish were conducted at a concentration of 7% w/w of acid adulterants. Furthermore, the blood serum metabolite profile of adulterated-honey-treated zebrafish was screened by LC-MS/MS analysis and three endogenous metabolites have been revealed: (1) Xanthotoxol or 8-Hydroxypsoralen, (2) 16-Oxoandrostenediol, and (3) 3,5-Dicaffeoyl-4-succinoylquinic acid. These results prove that employed honey adulterants cause mortality that contributes to higher toxicity. Moreover, this study introduces the zebrafish toxicity test as a new promising standard technique for the potential toxicity assessment of acid-adulterated honey in this study and hazardous food adulterants for future studies.

The buzz about honey-based biosurveys

Approximately 1.8 million metric tonnes of honey are produced globally every year. The key source behind this output, the honey bee (Apis mellifera), works tirelessly to create the delicious condiment that is consumed worldwide. The honey that finds its way into jars on store shelves contains a myriad of information about its biogeographical origins, such as the bees that produced it, the botanical constituents, and traces of other organisms or pathogens that have come in contact with the product or its producer. With the ongoing threat of honey bee decline and overall global biodiversity loss, access to ecological information has become an key factor in preventing the loss of species. This review delves into the various molecular techniques developed to characterize the collective DNA harnessed within honey samples, and how it can be used to elucidate the ecological interactions between honey bees and the environment. We also explore how these DNA-based methods can be used for large-scale biogeographical studies through the environmental DNA collected by foraging honey bees. Further development of these techniques can assist in the conservation of biodiversity by detecting ecosystem perturbations, with the potential to be expanded towards other critical flying pollinators.

Physicochemical and antioxidant properties of Apis cerana honey from Lombok and Bali Islands

Limited honey production worldwide leads to higher market prices, thus making it prone to adulteration. Therefore, regular physicochemical analysis is imperative for ensuring authenticity and safety. This study describes the physicochemical and antioxidant properties of Apis cerana honey sourced from the islands of Lombok and Bali, showing their unique regional traits. A comparative analysis was conducted on honey samples from Lombok and Bali as well as honey variety from Malaysia. Moisture content was found slightly above 20% in raw honey samples from Lombok and Bali, adhering to the national standard (SNI 8664:2018) of not exceeding 22%. Both honey types displayed pH values within the acceptable range (3.40-6.10), ensuring favorable conditions for long-term storage. However, Lombok honey exhibited higher free acidity (78.5±2.14 meq/kg) than Bali honey (76.0±1.14 meq/kg), surpassing Codex Alimentarius recommendations (≤50 meq/kg). The ash content, reflective of inorganic mineral composition, was notably lower in Lombok (0.21±0.02 g/100) and Bali honey (0.14±0.01 g/100) compared to Tualang honey (1.3±0.02 g/100). Electric conductivity, indicative of mineral content, revealed Lombok and Bali honey with lower but comparable values than Tualang honey. Hydroxymethylfurfural (HMF) concentrations in Lombok (14.4±0.11 mg/kg) and Bali (17.6±0.25 mg/kg) were slightly elevated compared to Tualang honey (6.4±0.11 mg/kg), suggesting potential processing-related changes. Sugar analysis revealed Lombok honey with the highest sucrose content (2.39±0.01g/100g) and Bali honey with the highest total sugar content (75.21±0.11 g/100g). Both honeys exhibited lower glucose than fructose content, aligning with Codex Alimentarius guidelines. The phenolic content, flavonoids, and antioxidant activity were significantly higher in Lombok and Bali honey compared to Tualang honey, suggesting potential health benefits. Further analysis by LC-MS/MS-QTOF targeted analysis identified various flavonoids/flavanols and polyphenolic/phenolic acid compounds in Lombok and Bali honey. The study marks the importance of characterizing the unique composition of honey from different regions, ensuring quality and authenticity in the honey industry.

Impact of Different Adulterants on Honey Quality Properties and Evaluating Different Analytical Approaches for Adulteration Detection

The study was carried out keeping in view the recently emerging concern of adulteration of natural honey on the honey markets. This study intended to investigate honey adulteration detection using physical and chemical composition to achieve a foreign component (a marker) that is present in the honey that confirms either the adulteration or authenticity of the honey. The technique was evaluated on honey samples that were 5-50% adulterated with various common adulterants in Ethiopia. Preliminary quick tests and characterization of physicochemical and antioxidant properties were tested as alternative analytical approaches for honey adulteration detection. Preliminary quick test methods were used to detect adulterated honey, but these methods were found specific to adulterant materials. The proline and pH levels decreased as molasses, sugar, and banana adulterants increased, while increased as melted candy and shebeb adulterants increased. Moisture content decreased as sugar, melted candy, and shebeb adulterants were increased, while decreased as molasses and banana adulterants increased. HMF content increased as molasses, melted candy, and shebeb adulterants were increased. The sugar compositions are key differential criteria to detect the adulteration of honey with sugar. Based on their physical characteristics, PCA demonstrated a considerable difference between samples of pure and contaminated honey. In conclusion, it was observed that honey adulteration was detected based on significant deviations of physicochemical and biochemical components from expected values in the concentration of naturally occurring components. This study successfully demonstrated a method to rapidly and accurately classify and authenticate honey. Accordingly, it is recommended that frequent training for stakeholders on adulteration detection methods should be carried out to avoid adulteration of honey from the markets.

Detection of barley malt syrup as an adulterant in honey by 1H NMR profile

Currently, Barley Malt Syrup (BMS) is one of the forms of growing adulteration in honey. However, there have been no reports regarding its identification by NMR. In this aspect, we proposed a 1H NMR profiling method to discriminate between authentic and honey adulterated with BMS. The authenticated honey samples were artificially adulterated with varying percentages of BMS. It was found that a marker peak primarily falling around the 5.40 ppm region exhibited discrimination between pure and adulterated samples. Furthermore, NMR data of the samples were analyzed using statistical models. The findings demonstrate that NMR sugar profiles region, when combined with PCA analysis, can effectively detect varying degrees of adulteration. Despite qualitative nature of the outcomes, spiking studies have revealed that approach can reliably identify sugar addition at levels as low as 5-10%. Overall, NMR-based approach proves to be effective in detecting BMS as an adulterant in honey.

Investigation of Foreign Amylase Adulteration in Honey Distributed in Japan by Rapid and Improved Native PAGE Activity Staining Method

Foreign amylase addition to honey in an effort to disguise diastase activity has become a widespread form of food fraud. However, since there is no report on the investigation in Japan, we investigated foreign amylases in 67 commercial honeys in Japan. First, the α-glucosidase and diastase activities of honeys were measured, which revealed that only α-glucosidase activity was significantly low in several samples. As both enzymes are secreted from honeybee glands, it is unlikely that only one enzyme was inactivated during processing. Therefore, we suspected the presence of foreign amylase. α-Amylase in honey were assigned using protein analysis software based on LC-QTOF-MS. As a result, α-amylases from Aspergillus and Geobacillus were detected in 13 and 6 out of 67 honeys, respectively. To detect foreign amylases easily, we developed a cost-effective method using native PAGE. Conventional native PAGE failed to separate the α-amylase derived from honeybee and Geobacillus. However, when native PAGE was performed using a gel containing 1 % maltodextrin, the α-amylase from honeybee did not migrated in the gel and the α-amylase could be separated from the other two α-amylases. The results from this method were consistent with those of LC-QTOF-MS method, suggesting that the novel native PAGE method can be used to detect foreign amylases.

Enabling honey quality and authenticity with NMR and LC-IRMS based platform

Honey has been known for economically motivated adulteration around the world, because of its high demand and short supply. As consequence increasing honey production using the deliberate addition of sugar syrups while claiming a fictitious origin and diversifying it to increase its value. Generally, honey testing is supervised by a set of guidelines and quality parameters to ensure its quality and authenticity. As per the many regulatory bodies, current honey scams have been challenging to identify with conventional methods, so quality control labs require sophisticated technology. With these paradigm shifts, the aim of the present review is focused on the authenticity of honey through two important cutting-edge methods viz LC-IRMS and NMR. The LC-IRMS aids in the detection of added C₃ and C₄ sugars. Whereas NMR has provided a potent solution by allowing the classification of botanical varieties and geographical origin along with the quantification of a set of quality parameters in a single experiment.

Physicochemical Composition of Local and Imported Honeys Associated with Quality Standards

The compliance with honey standards is crucial for its validity and quality. The present study evaluated the botanical origin (pollen analysis) and physicochemical properties: moisture, color, electrical conductivity (EC), free acidity (FA), pH, diastase activity, hydroxymethylfurfural (HMF), and individual sugar content of forty local and imported honey samples. The local honey exhibited low moisture and HMF (14.9% and 3.8 mg/kg, respectively) than imported honey (17.2% and 23 mg/kg, respectively). Furthermore, the local honey showed higher EC and diastase activity (1.19 mS/cm and 11.9 DN, respectively) compared to imported honey (0.35 mS/cm and 7.6 DN, respectively). The mean FA of local honey (61 meq/kg) was significantly naturally higher than that of imported honey (18 meq/kg). All local nectar honey that originated from Acacia spp. exhibited naturally higher FA values that exceeded the standard limit (≤50 meq/kg). The Pfund color scale ranged from 20 to 150 mm in local honey and from 10 to 116 mm in imported honey. The local honey was darker, with a mean value of 102.3 mm, and was significantly different from imported honey (72.7 mm). The mean pH values of local and imported honey were 5.0 and 4.5, respectively. Furthermore, the local honey was more diverse in pollen grain taxa compared to imported honey. Local and imported honey elicited a significant difference regarding their sugar content within individual honey type. The mean content of fructose, glucose, sucrose, and reducing sugar of local honey (39.7%, 31.5%, 2.8%, and 71.2%, respectively) and imported honey (39.2%, 31.8%, 0.7%, and 72.0%, respectively) were within the permitted quality standards. This study indicates the necessity of increasing the awareness regarding quality investigations for healthy honey with good nutritional value.

Botanical honey recognition and quantitative mixture detection based on Raman spectroscopy and machine learning

The development of new approaches for honey recognition, based on spectroscopic techniques, presents a huge market potential especially because of the fast development of portable equipment. As an emerging approach, the association between Raman spectroscopy and Artificial Intelligence (i.e. Machine Learning algorithms) for food and beverages recognition starts to prove its efficiency, becoming an important candidate for the development of a practical application. Through this study, new recognition models for the rapid and efficient botanical differentiation of investigated honey varieties were developed, allowing the correct prediction of each type in a percentage better than 81%. The performances of the constructed models were expressed in terms of precision, sensitivity, and specificity. Moreover, through this approach, the detection of honey mixtures was possible to be made and an estimative percentage of the mixture components was obtained. Thus, the applicative potential of this new approach for honey recognition as well as a qualitative and quantitative estimation of the honey mixture was demonstrated.

Detection of honey adulteration using benchtop 1H NMR spectroscopy

High magnetic field NMR spectroscopy featuring the use of superconducting magnets is a powerful analytical technique for the detection of honey adulteration. Such high field NMR systems are, however, typically housed in specialised laboratories, require cryogenic coolants, and necessitate specialist training to operate. Benchtop NMR spectrometers featuring permanent magnets are, by comparison, significantly cheaper, more mobile and can be operated with minimal expertise. The lower magnetic fields used in such systems, however, result in limited spectral resolution, which diminishes their ability to perform quantitative composition analysis. These limitations may be overcome by implementing a recently developed field-invariant model-based fitting method which is defined by the underlying quantum mechanical properties of the nuclear spin system; this method is applied here to quantify the sugar composition of honey using benchtop ¹H NMR (43 MHz) spectroscopy. The detection of adulteration of 26 honey samples with brown rice syrup is quantitatively demonstrated to a minimum adulterant concentration of 5 wt%. Honey adulteration with corn syrup, glucose syrup and wheat syrup was also quantitatively detected using this approach. Our NMR detection of adulteration was shown to be invariant with time over 60 days of storage.

Carbon Black Integrated Polylactic Acid Electrodes Obtained by Fused Deposition Modeling: A Powerful Tool for Sensing of Sulfanilamide Residues in Honey Samples

Sulfanilamide (SFL) is used to prevent infections in honeybees. However, many regulatory agencies prohibit or establish maximum levels of SFL residues in honey samples. Hence, we developed a low-cost and portable electrochemical method for SFL detection using a disposable device produced through 3D printing technology. In the proposed approach, the working electrode was printed using a conductive filament based on carbon black and polylactic acid and it was associated with square wave voltammetry (SWV). Under optimized SWV parameters, linear concentration ranges (1-10 μmol L^-1 and 12.5-35.0 μmol L^-1), a detection limit of 0.26 μmol L^-1 (0.05 mg L^-1), and suitable RSD values (2.4% for inter-electrode; n = 3) were achieved. The developed method was selective in relation to other antibiotics applied in honey samples, requiring only dilution in the electrolyte. The recovery values (85-120%) obtained by SWV were statistically similar (95% confidence level) to those obtained by HPLC, attesting to the accuracy of the analysis and the absence of matrix interference.

Tracking of Thermal, Physicochemical, and Biological Parameters of a Long-Term Stored Honey Artificially Adulterated with Sugar Syrups

The growing phenomenon of honey adulteration prompts the search for simple methods to confirm the authenticity of honey. The aim of the study was to evaluate the changes in thermal characteristics, physicochemical parameters, antioxidant and enzymatic activity of honey subjected to artificial adulteration. Two series of products were prepared with the use of two different sugar syrups with an increasing dosage of adulterant (0 to 30%). After 24 months of storage, the quality of adulterated samples (partially crystallized) was assessed in comparison to the control honey (solid). Used adulteration changed physicochemical parameters and reduced antioxidant and enzymatic activity of honey (p < 0.05). The admixture of syrup and invert (p < 0.05) reduced the viscosity of liquid phase of delaminated honey in a dose-dependent manner. In the study, artificially adulterated honeys were controlled using the standard differential scanning calorimetry, DSC. In all adulterated honeys, a specific glass transition, TG, was observed in the range of 34-38.05 °C, which was not observed for control honey and pure adulterants. Moreover, the additional T_gs were observed in a wide range from -19.5 °C to 4.10 °C for honeys adulterated by syrup only. In turn, the T_g in range of 50.4-57.6 °C was observed only for the honeys adulterated by invert. These specific T_g seem to be useful to detect honey adulteration and to identify the kind of adulterant used.

Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods

To address the growing concern of honey adulteration in Canada and globally, a quantitative NMR method was developed to analyze 424 honey samples collected across Canada as part of two surveys in 2018 and 2019 led by the Canadian Food Inspection Agency. Based on a robust and reproducible methodology, NMR data were recorded in triplicate on a 700 MHz NMR spectrometer equipped with a cryoprobe, and the data analysis led to the identification and quantification of 33 compounds characteristic of the chemical composition of honey. The high proportion of Canadian honey in the library provided a unique opportunity to apply multivariate statistical methods including PCA, PLS-DA, and SIMCA in order to differentiate Canadian samples from the rest of the world. Through satisfactory model validation, both PLS-DA as a discriminant modeling technique and SIMCA as a class modeling method proved to be reliable at differentiating Canadian honey from a diverse set of honeys with various countries of origins and floral types. The replacement method of optimization was successfully applied for variable selection, and trigonelline, proline, and ethanol at a lower extent were identified as potential chemical markers for the discrimination of Canadian and non-Canadian honeys.

Using Front-Face Fluorescence Spectroscopy and Biochemical Analysis of Honey to Assess a Marker for the Level of Varroa destructor Infestation of Honey Bee (Apis mellifera) Colonies

Varroa destructor is a parasitic mite responsible for the loss of honey bee (Apis mellifera) colonies. This study aimed to find a promising marker in honey for the bee colony infestation level using fluorescence spectroscopy and biochemical analyses. We examined whether the parameters of the honey samples' fluorescence spectra and biochemical parameters, both related to proteins and phenolics, may be connected with the level of honey bee colonies' infestation. The infestation level was highly positively correlated with the catalase activity in honey (r = 0.936). Additionally, the infestation level was positively correlated with the phenolic spectral component (r = 0.656), which was tentatively related to the phenolics in honey. No correlation was found between the diastase activity in honey and the colonies' infestation level. The results indicate that the catalase activity in honey and the PFC1 spectral component may be reliable markers for the V. destructor infestation level of the colonies. The obtained data may be related to the honey yield obtained from the apiaries.

A review on the phytochemical composition and health applications of honey

Background

Though honey has long been used as medicine, there is a scarcity of knowledge on how it interacts with the body.

Scope and approach

While different types of honey have different chemical and medicinal properties according to their origin, this narrative review seeks to analyse the current knowledge on the chemical composition and therapeutic use of honey. With numerous chemical components, honey has a range of health benefits in multiple disciplines of medicine, and provides an interesting prospect in chemical analysis with regards to identification of its origin.

Key findings and conclusions

There is a great potential for the use of honey in medicine, primarily due to its antioxidant and antimicrobial properties. Recent studies on the phenolic and enzymatic components of honey have made honey's therapeutic method of action in relation to the above properties clearer, still more research needs to be conducted and more innovations need to be tested, for the full potential of honey to be understood.

Determination of Possible Adulteration and Quality Assessment in Commercial Honey

This study aims to predict several quality traits in commercial honey samples simultaneously and to reveal possible honey adulteration using a field-deployable portable infrared spectrometer without any sample preparation. A total of one hundred and forty-seven commercial honey samples were purchased from local and online markets in Turkey and the United States of America (USA), and their soluble solids (°Brix), pH, free acidity, moisture, water activity (aw), glucose, fructose, sucrose, and hydroxymethyl furfural (HMF) contents were determined using reference methods. The HMF (n = 11 samples) and sucrose (n = 21) concentrations were higher than the regulatory limits in some tested samples. The exceeding HMF content may imply temperature abuse during storage and prolonged storing. On the other hand, high sucrose content may indicate possible adulteration with commercial sweeteners. Therefore, soft independent modeling of class analogies (SIMCA) analysis was conducted to reveal this potential sweetener adulteration in the samples, and the SIMCA model was able to identify all the flagged samples. The suggested FT-IR technique may be helpful in regulatory bodies in determining honey authenticity issues as well as assessing the quality characteristics of honey samples in a shorter period and at a lower cost.

Uptrend in global managed honey bee colonies and production based on a six-decade viewpoint, 1961-2017

We conducted a retrospective study to examine the long-term trends for the global honey bee population and its two main products: honey and beeswax. Our analysis was based on the data collected by the Food and Agriculture Organization of the United Nations from 1961 to 2017. During this period, there were increases in the number of managed honey bee colonies (85.0%), honey production (181.0%) and beeswax production (116.0%). The amount of honey produced per colony increased by 45.0%, signifying improvements in the efficiency for producing honey. Concurrently, the human population grew by 144.0%. Whilst the absolute number of managed colonies increased globally, the number per capita declined by 19.9% from 13.6 colonies per 1000 population in 1961 to 10.9 colonies per 1000 population in 2017. Beeswax had a similar trend as the global production per capita reduced by 8.5% from 8.2 to 7.5 kg per 1000 population. In contrast, the global honey production per capita increased by 42.9% at the global level. The global human population growth outpaced that of managed honey bee colonies. Continuation of this trend raises the possibility of having a shortfall of pollinators to meet the increasing consumer demand for pollinated crops. To mitigate these challenges locally driven solutions will be key as influencing factors differed geographically.

Adulteration detection technologies used for halal/kosher food products: an overview

In the Islamic and Jewish religions, there are various restrictions that should be followed in order for food products to be acceptable. Some food items like pork or dog meat are banned to be consumed by the followers of the mentioned religions. However, illegally, some food producers in various countries use either the meat or the fat of the banned animals during food production without being mentioned in the label on the final products, and this considers as food adulteration. Nowadays, halal or kosher labeled food products have a high economic value, therefore deceiving the consumers by producing adulterated food is an illegal business that could make large gains. On the other hand, there is an insistent need from the consumers for getting reliable products that comply with their conditions. One of the main challenges is that the detection of food adulteration and the presence of any of the banned ingredients is usually unnoticeable and cannot be determined by the naked eye. As a result, scientists strove to develop very sensitive and precise analytical techniques. The most widely utilized techniques for the detection and determination of halal/kosher food adulterations can be listed as High-Pressure Liquid Chromatography (HPLC), Capillary Electrophoresis (CE), Gas Chromatography (GC), Electronic Nose (EN), Polymerase Chain Reaction (PCR), Enzyme-linked Immuno Sorbent Assay (ELISA), Differential Scanning Calorimetry (DSC), Nuclear Magnetic Resonance (NMR), Near-infrared (NIR) Spectroscopy, Laser-induced Breakdown Spectroscopy (LIBS), Fluorescent Light Spectroscopy, Fourier Transform Infrared (FTIR) Spectroscopy and Raman Spectroscopy (RS). All of the above-mentioned techniques were evaluated in terms of their detection capabilities, equipment and analysis costs, accuracy, mobility, and needed sample volume. As a result, the main purposes of the present review are to identify the most often used detection approaches and to get a better knowledge of the existing halal/kosher detection methods from a literature perspective.

Characterization and Classification of Spanish Honey by Non-Targeted LC-HRMS (Orbitrap) Fingerprinting and Multivariate Chemometric Methods

A non-targeted LC-HRMS fingerprinting methodology based on a C18 reversed-phase mode under universal gradient elution using an Orbitrap mass analyzer was developed to characterize and classify Spanish honey samples. A simple sample treatment consisting of honey dissolution with water and a 1:1 dilution with methanol was proposed. A total of 136 honey samples belonging to different blossom and honeydew honeys from different botanical varieties produced in different Spanish geographical regions were analyzed. The obtained LC-HRMS fingerprints were employed as sample chemical descriptors for honey pattern recognition by principal component analysis (PCA) and partial least squares-discriminant analysis (PLS-DA). The results demonstrated a superior honey classification and discrimination capability with respect to previous non-targeted HPLC-UV fingerprinting approaches, with them being able to discriminate and authenticate the honey samples according to their botanical origins. Overall, noteworthy cross-validation multiclass predictions were accomplished with sensitivity and specificity values higher than 96.2%, except for orange/lemon blossom (BL) and rosemary (RO) blossom-honeys. The proposed methodology was also able to classify and authenticate the climatic geographical production region of the analyzed honey samples, with cross-validation sensitivity and specificity values higher than 87.1% and classification errors below 10.5%.

Isolation and Barcoding of Trace Pollen-free DNA for Authentication of Honey

Adulteration and mislabeling of honey to mask its true origin have become a global concern. Pollen microscopy, the current gold standard for identifying honey's geographical and plant origins, is laborious, requires extensive training, and fails to identify filtered honey and honey spiked with pollen from a more favorable plant to disguise its origins. We successfully isolated pollen-free DNA from filtered honey using three types of adsorbents: (i) anti-dsDNA antibodies coupled to magnetic microspheres; (ii) anion-exchange adsorbent; and (iii) ceramic hydroxyapatite. The internal transcribed spacer 2 region of the captured pollen-free DNA was polymerase chain reaction-amplified and subjected to next-generation sequencing. Using an in-house bioinformatics pipeline, initial experiments showed that anion exchange had the greatest capacity to capture trace pollen-free DNA, and it was successfully applied to isolate DNA from five honey samples. Enrichment of trace pollen-free DNA from filtered honey samples opens a new approach for identifying the true origins of honey.

LC-HRMS-Based Non-Targeted Metabolomics for the Assessment of Honey Adulteration with Sugar Syrups: A Preliminary Study

Honey is a natural product that is in great demand and has a relatively high price, thus making it one of the most common targets of economically motivated adulteration. Its adulteration can be obtained by adding cheaper honey or sugar syrups or by overfeeding honeybees with sugar syrups. Adulteration techniques are constantly evolving and advanced techniques and instruments are required for its detection. We used non-targeted metabolomics to underscore potential markers of honey adulteration with sugar syrups. The metabolomic profiles of unadulterated honeys and sugar beet, corn and wheat syrups were obtained using hydrophilic interaction liquid chromatography high-resolution mass spectrometry (LC-HRMS). The potential markers have been selected after data processing. Fortified honey (5%, 10% and 20%), honey obtained from overfeeding, and 58 commercial honeys were analyzed. One potential marker appeared with a specific signal for syrups and not for honey. This targeted analysis showed a linear trend in fortified honeys with a calculated limit of quantification around 5% of fortification.

Characterization of Various Honey Samples from Different Regions of Morocco Using Physicochemical Parameters, Minerals Content, Antioxidant Properties, and Honey-Specific Protein Pattern

Honey is a bee product relatively expensive; therefore, it has been a target of adulteration by many sweeteners. In this work, we evaluated the good quality, authenticity, and content in bioactive molecules of twenty-two Moroccan honey from different botanical origins and geographical areas. For that, the following analyses were determined: the content in total protein and especially the major royal jelly protein (apalbumin 1), the analysis of total acidity, free acidity, lactonic acidity, pH, ash, Pfund, electrical conductivity, and moisture. In addition, the content of sodium, potassium, calcium, and magnesium, the dosage of polyphenols, flavones, and flavonols, and the antioxidant activities were assessed. All analyzed samples had good antioxidant activities and present a source of antioxidant compounds, the predominant mineral in all honey samples was potassium, and the physicochemical parameters are in line with the standards’ recommended limits. The content of honey samples in total protein and apalbumin 1 ranged between 212 μg/g and 4121.2 μg/g and between 27.4 μg/g and 790.82 μg/g, respectively. Overall, the detection of apalbumin 1 in all honey samples and the results of physicochemical parameters, minerals, bioactive compounds, and antioxidant activities confirm the authenticity and no adulteration of Moroccan honey.

Comparative Study of the Potentially Toxic Elements and Essential Microelements in Honey Depending on the Geographic Origin

The profiling and quantification of potentially toxic elements (PTEs) in honey from Poland was the main aim of this work. Due to the differences in botanical and geographical origin, 33 honey samples from various parts of Poland have been tested and compared to 12 samples taken from other countries, such as Australia, Bulgaria, Italy, Germany, Portugal, Romania and Turkey. The studied elements in honey samples were: As, Be, Cd, Co, Cr, Cu, Fe, Mg, Mn, Mo, Ni, Pb, Sb, V and Zn. In most cases, the analyzed samples of honey were characterized by the moderate values of analyzed PTEs. Only a few samples contained higher concentrations of copper and manganese were noted. The presence of cadmium and lead in the level below the background equivalent concentrations was measured in the tested samples.

Dielectric properties of honey adulterated by sugar syrup

Dielectric properties of pure honey adulterated by varying amount of sugar syrup have been measured over 20 Hz to 2 MHz frequency range, at room temperature. The samples were prepared by mixing different proportions of sugar syrup in pure honey. It has been observed that, dielectric constant (ε′) and dielectric loss (ε″) of the mixture increases as percentage of sugar-syrup in honey increases. ε′ of the mixture decreases rapidly with the increase in frequency and achieves static value at higher frequency. ε″ decreases linearly with increase in frequency. The relaxation time corresponding to tanδ peak varies nonlinearly with increase in concentration of sugar syrup in the honey. The dielectric loss at 2 MHz, and the relaxation frequency of the mixture increases linearly with increase in concentration of sugar syrup in honey, which can be used to estimate the amount of adulteration of honey by sugar syrup.

Physicochemical Properties of a New Green Honey from Banggi Island, Sabah

Green honey is exclusively available on the island of Banggi in Sabah, and its uniqueness sees the commodity being sold at a high market price. Therefore, green honey is prone to adulteration by unscrupulous individuals, possibly compromising the health of those consuming this food commodity for its curative properties. Moreover, an established standard for reducing sugar in green honey is unavailable. Ipso facto, the study aimed to profile green honey’s physical and chemical properties, such as its pH, moisture content, free acidity, ash content, electroconductivity, hydroxymethylfurfural (HMF), total phenolic content, total flavonoid content, DPPH, colour, total sugar content, total protein content, and heavy metals as well as volatile organic compounds, the data of which are profoundly valuable in safeguarding consumers’ safety while providing information for its quality certification for local consumption and export. The results revealed that the honey’s physicochemical profile is comparable to other reported kinds of honey. The honey’s naturally green colour is because of the chlorophyll from the nectar from various flowers on the island. The raw honey showed free acidity between 28 and 33 Meq/100 g, lower than the standard’s 50 Meq/100 g. The hydroxymethylfurfural content is the lowest compared to other reported honey samples, with the total phenolic content between 16 and 19 mg GAE/100 g. The honey’s reducing sugar content is lower (~37.9%) than processed ones (56.3%) because of water removal. The protein content ranged from 1 to 2 gm/kg, 4- to 6-fold and 2-fold higher than local and manuka honey, respectively. The exceptionally high content of trans-4-hydroxyproline in raw honey is its source of collagen and other healing agents. Interestingly, low levels of arsenic, lead, nickel, cadmium, copper, and cobalt were detected in the honey samples, presumably due to their subterranean hives. Nevertheless, the honey is fit for general consumption as the concentrations were below the maxima in the Codex Alimentarius Commission of 2001.

Oral Mucositis Induced by Chemoradiotherapy in Head and Neck Cancer—A Short Review about the Therapeutic Management and the Benefits of Bee Honey

Background and Objectives: Oral mucositis, a severe non-hematological complication, can be induced by chemoradiotherapy. It is associated with severe local dysfunction, severely affecting the patient’s quality of life; it increases the risk of oral infections and interrupts oncological treatment, thus prolonging the duration and cost of hospitalization. Besides all of the agents used in the prevention and treatment of oral mucositis induced by oncological treatment, can there be found an easier one to administer, with an effective preparation, high addressability, both for adults and paediatric patients, without side effects, and at the same time cheap and easy to purchase? The aim of the present paper is to demonstrate the existence of this product, which is available to everyone, having multiple benefits. Materials and Methods: For the purpose of writing this article, materials were searched in electronic databases in between 2019 and 2021, taking into consideration papers where authors have demonstrated the effectiveness of this product through its topical or systemic use. Results: Numerous studies have highlighted the benefits of honey on oral mucositis. Through its analgesic, anti-inflammatory, anti-cancerous and antibacterial action, honey has proved to have a major impact on the patient’s quality of life and nutritional status by promoting tissue epithelialization and healing of the chemoradiotherapy-induced lesions. Conclusions: Superior to many natural agents, bee honey can be successfully used in both preventing and treating oral mucositis. There are currently numerous studies supporting and recommending the use of bee honey in the management of this oncological toxicity.

Potential of honey against the onset of autoimmune diabetes and its associated nephropathy, pancreatitis, and retinopathy in type 1 diabetic animal model

Honey has been used as a traditional remedy for various health benefits. This study investigated the potential of honey against the onset of autoimmune diabetes and its associated secondary complications in type 1 diabetic (T1D) experimental animals. Autoimmune diabetes was induced in Sprague Dawley rats, and at the same time, the rats were treated with honey or metformin. Sandwich ELISAs were used to estimate blood glucose, hemoglobin A1C (HbA1c), total cholesterol, and triglycerides. Histopathological examinations determined the T1D-induced lesions on kidneys, pancreas, cornea, and retina. Treatment of rats with honey during the course of T1D induction showed a significant reduction in fasting-blood-glucose and HbA1c (p < 0.01), and total lipid profile was also improved (p < 0.05). Not only these, but honey also reduced the T1D-induced lesions in the kidney, pancreas, and cornea/retina (p < 0.05). Metformin showed similar effects and was used as a positive control. In conclusion, honey showed therapeutic potential against the onset of autoimmune diabetes, as it reduces blood glucose/HbA1c and improves the lipid profile by reducing the plasma levels of total cholesterol, low-density lipoproteins (LDL), very low-density lipoprotein (VLDL), and triglycerides. Moreover, it also showed protective potential against the development of diabetic nephropathy, pancreatitis, and retinopathy.

Comparison of Various Signal Processing Techniques and Spectral Regions for the Direct Determination of Syrup Adulterants in Honey Using Fourier Transform Infrared Spectroscopy and Chemometrics

Honey consumption has become increasingly popular worldwide. However, the increase in demand for honey has also caused an increase in its adulteration, a deliberate fraud which involves adding of other substances to pure honey for economic purposes. This process not only lowers the quality of honey, but also has potential health risks, including high blood sugar, increased risk of diabetes, and weight gain. Herein, we develop an easy-to-use and direct method of quantifying corn, cane, beet, and rice syrup adulterants in honey using Fourier transform infrared spectroscopy and chemometrics. Various signal processing techniques, including derivatives, moving average, binning, Savitzky–Golay, and standard normal variate using the entire spectral region (3996–650 cm−1) and specific spectral region (1501–799 cm−1), were compared. Optimum results were obtained using first derivative signal processing for both the entire and specific spectral regions. The first derivative signal processing technique garnered the most optimum results using the specific spectral range (1501–799 cm−1) (RMSECVaverage = 0.021, RMSEPaverage = 0.014, R2average = 0.859) across all syrup adulterants. An exploratory analysis to assess the utility of this specific spectral region in pattern recognition of samples based on their adulterant content show that this region is effective in discriminating samples according to the presence or absence of honey syrup adulterants.

Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification

Nuclear magnetic resonance (NMR) is a powerful analytical tool which can be used for authenticating honey, at chemical constituent levels by enabling identification and quantification of the spectral patterns. However, it is still challenging, as it may be a person-centric analysis or a time-consuming process to analyze many honey samples in a limited time. Hence, automating the NMR spectral analysis of honey with the supervised machine learning models accelerates the analysis process and especially food chemistry researcher or food industry with non-NMR experts would benefit immensely from such advancements. Here, we have successfully demonstrated this technology by considering three major sugar adulterants, i.e., brown rice syrup, corn syrup, and jaggery syrup, in honey at varying concentrations. The necessary supervised machine learning classification analysis is performed by using logistic regression, deep learning-based neural network, and light gradient boosting machines schemes.

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NMR helps to identify the fingerprints of honey chemical constituents.

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Combined NMR and ML tools can determine the type of adulteration in honey.

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Supervised classification schemes, Logistic regression, DNN, and LGBM are utilized.

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Corn, brown rice, and jaggery adulterations are discriminated in honey.

Rapid determination of multi-antibiotic residues in honey based on modified QuEChERS method coupled with UPLC-MS/MS

Antibiotic residues in honey cause public health problems. To analyze multi-antibiotic residues in honey, a modified QuEChERS (quick, easy, cheap, effective, rugged and safe) extraction method coupled with ultra-performance liquid chromatography tandem mass spectrometry (UPLC-MS/MS) was developed for simultaneous quantification of 70 antibiotic residues in honey. Matrix-matched calibrations indicated the correlation coefficients were higher than 0.998. The recovery was in a range of 70.5%-119.8% with intra-day relative standard deviation (RSD) of ≤ 10.0% and inter-day RSD of ≤ 13.9%. The limits of detection ranged between 0.050 μg/kg and 1.02 μg/kg. Limits of quantification was 0.17 μg/kg to 3.40 μg/kg. The matrix effects were negligible in 71.4% of compounds and moderately in 24.3% of compounds. Methacycline, oxytetracycline, tetracycline and its metabolite 4-tetracycline residues were detected in the tested samples. Validation parameters were acceptable and were in line with the Codex guidelines. This method was effective for detecting multi-antibiotic residues in honey.

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.