Honey Adulteration Research Articles

Rapid Assessment of Molasses Adulterated Honey Using Laser Raman Spectroscopy and Principal Component Analysis

Rapid Assessment of Molasses Adulterated Honey Using Laser Raman Spectroscopy and Principal Component Analysis

Review of scientific instruments: Evaluation of adulteration in honey using a microwave planar resonator sensor.

A microwave microstrip line resonator sensor is developed as an alternative tool for detecting adulteration in honey. A honey-filled tube is placed at the position with the maximum electric field intensity. When the honey is adulterated, its permittivity is changed, leading to a distinct resonance frequency shift and enabling detection. Compared with the existing microwave sensors, this sensor offers the advantages of low cost, compact size, and easy fabrication. Moreover, quantitative analysis of the adulteration could be achieved. Electromagnetic simulation is performed using a co-simulation with CST and MATLAB. The simulation results reveal that the resonance frequency of the resonator decreases as the added water content increases, following a quadratic polynomial relationship. In the experiments, the results demonstrate a successive decrease in the resonance frequency from the empty tube, honey-filled tube to water-filled tube cases. Furthermore, honey samples with varying water contents (up to 70%) are tested, and the resonance frequency decreases with increasing added water content, which agrees well with the simulation results. In addition, there is a quadratic relationship between the two parameters. Principal component analysis is conducted on the transmission coefficients, and the first principal component decreases with increasing water content. With the addition of the second principal component, the cases of different water contents in honey can be well classified.

Ultraviolet–visible spectroscopy combined with machine learning as a rapid detection method to the predict adulteration of honey

Honey is often adulterated with inexpensive and artificial sweeteners. To overcome the time-consuming honey adulteration tests, which require precision, chemicals, and sample preparation, it is needful to develop trustworthy analytical methods to assure its authenticity. In the present study, the potential of ultraviolet–visible spectroscopy (UV–Vis) in predicting the sucrose content was evaluated by using Support Vector Regression (SVR) and Partial Least Square Regression (PLSR). To predict the sucrose content based on diagnostic wavelengths, a Point Spectro Transfer Function (PSTF) was evaluated using Multiple Linear Regression (MLR). For this purpose, the spectra of authentic (n = 12), commercial (n = 12), and adulterated (n = 16) honey samples were recorded. Four distinguished wavelengths from correlation analysis between sucrose content and spectra absorption were 216, 280, 316, and 603 nm. The SVR performed better calibration model than the PLSR estimations (RMSE = 0.97, and R2 = 0.98). The predictive models result revealed that both models had high accuracy for the sucrose content estimation. This study proved that UV–Vis spectroscopy provides an economical alternative for the rapid quantification of adulterated honey samples with sucrose.

A comprehensive review of the current trends and recent advancements on the authenticity of honey.

The authenticity of honey currently poses challenges to food quality control, thus requiring continuous modernization and improvement of related analytical methodologies. This review provides a comprehensively overview of honey authenticity challenges and related analytical methods. Firstly, direct and indirect methods of honey adulteration were described in detail, commenting the existing challenges in current detection methods and market supervision approaches. As an important part, the integrated metabolomic workflow involving sample processing procedures, instrumental analysis techniques, and chemometric tools in honey authenticity studies were discussed, with a focus on their advantages, disadvantages, and scopes. Among them, various improved microscale extraction methods, combined with hyphenated instrumental analysis techniques and chemometric data processing tools, have broad application potential in honey authenticity research. The future of honey authenticity determination will involve the use of simplified and portable methods, which will enable on-site rapid detection and transfer detection technologies from the laboratory to the industry.

A new approach to detecting sugar syrup addition to honey: Stable isotope analysis of hexamethylenetetramine synthesised from honey monosaccharides (fructose and glucose)

One of the most common types of adulteration of honey involves the addition of invert sugar syrups. A new method was developed to measure the stable isotope ratios of carbon and carbon-bound non-exchangeable (CBNE) hydrogen from specific molecular positions in fructose and glucose in honey. This was achieved through periodate oxidation of the sugars to produce formaldehyde, followed by reaction with ammonia to form hexamethylenetetramine (HMT). The preparation was simplified, optimized, and validated by isotopic analysis of replicate syntheses of HMT from fructose, glucose, sugar syrup and a representative authentic honey sample. The optimized method had a repeatability standard deviation from 1.5‰ to 3.0‰ and from 0.1‰ to 0.4‰ for δ2H and δ13C, respectively. This methodology has advantages over alternative isotopic methods, for measuring CBNE hydrogen isotope ratios in sugars, in terms of time, sensitivity and operability and offers a complementary method to differentiate authentic honey from invert sugar syrups.

Characterization and profiling of mustard honey derived from three subspecies of Apis cerana F. in Bangladesh

The Asian honey bee, Apis cerana, has received considerable attention as a quality honey producing bee species among the beekeepers and consumers all over the world As a natural health product, the nutritional profiling of honey from different botanical sources have well understood, however, the physico-chemical properties of honey can also be mediated from its entomological sources. Recent phylogeny of A. cerana in Bangladesh revealed the occurrences of three subspecies, however, the physico-chemical profiling of the honey corresponding to A. cerana subspecies were lacking. This research aims to analyze the physicochemical and biochemical properties of honey derived from the identified A. cerana subspecies for profiling for using as an authentical tool against honey adulteration. Honeybee samples were collected from seven localities in Bangladesh based on nectarine sources. Genetic diversity was estimated using the mtDNA sequences of Cytochrome Oxidase Subunit-1 (COI 1) gene of A. cerana with accession number (MZ558037-MZ558043). The physicochemical properties, namely moisture, pH, color, free acidity, formaldehyde, hydroxymethylfurfural (HMF), electrical conductivity (EC), and optical density (OD), were analyzed for profiling and determining correlations among the identified honeybee subspecies. Phylogenetic analysis of A. cerana revealed three subspecies namely A. cerana indica, A. cerana japonica, and A. cerana cerana. Among physicochemical properties of honey color absorbance showed strong positive correlation with OD, EC, pH that denoted increase in OD, EC, pH also increases the color. free acidity among the honey produced by three different subspecies. However, formaldehyde and free acidity are positively correlate with each other. The findings can be used to discriminate honey in accordance to their entomological origin.

Non-Targeted Detection and Quantification of Food Adulteration of High-Quality Stingless Bee Honey (SBH) via a Portable LED-Based Fluorescence Spectroscopy.

Stingless bee honey (SBH) is rich in phenolic compounds and available in limited quantities. Authentication of SBH is important to protect SBH from adulteration and retain the reputation and sustainability of SBH production. In this research, we use portable LED-based fluorescence spectroscopy to generate and measure the fluorescence intensity of pure SBH and adulterated samples. The spectrometer is equipped with four UV-LED lamps (peaking at 365 nm) as an excitation source. Heterotrigona itama, a popular SBH, was used as a sample. 100 samples of pure SBH and 240 samples of adulterated SBH (levels of adulteration ranging from 10 to 60%) were prepared. Fluorescence spectral acquisition was measured for both the pure and adulterated SBH samples. Principal component analysis (PCA) demonstrated that a clear separation between the pure and adulterated SBH samples could be established from the first two principal components (PCs). A supervised classification based on soft independent modeling of class analogy (SIMCA) achieved an excellent classification result with 100% accuracy, sensitivity, specificity, and precision. Principal component regression (PCR) was superior to partial least squares regression (PLSR) and multiple linear regression (MLR) methods, with a coefficient of determination in prediction (R2p) = 0.9627, root mean squared error of prediction (RMSEP) = 4.1579%, ratio prediction to deviation (RPD) = 5.36, and range error ratio (RER) = 14.81. The LOD and LOQ obtained were higher compared to several previous studies. However, most predicted samples were very close to the regression line, which indicates that the developed PLSR, PCR, and MLR models could be used to detect HFCS adulteration of pure SBH samples. These results showed the proposed portable LED-based fluorescence spectroscopy has a high potential to detect and quantify food adulteration in SBH, with the additional advantages of being an accurate, affordable, and fast measurement with minimum sample preparation.

Assessment of adulteration in honey by artificial sweeteners using dynamic laser speckle technique

Our research work uses the dynamic laser speckle technique for the first time to analyze the adulteration of “Dabur Honey” with two golden syrups Dhampur Green Golden Syrup (DGGS) and Solar Golden Syrup (SGS) as adulterate. Identifying 25% adulteration in honey by sweeteners is challenging due to several reasons, such as the complexity of honey composition and the similarity of some sweeteners to honey. A spatially filtered He-Ne laser beam is made to pass through adulterated samples. Forwarding scattered light is captured using a charge-coupled device (CCD) camera and Digital Image Processing is used to analyze the images. Seven textures and six features of images are evaluated. For 20% adulteration in honey by both the adulterates, a significant percentage change of 17%, 16%, 16%, 13%, and 11% is observed in Angular Second Moment, Covariance, Autocorrelation, Inertia, and Kurtosis respectively. Additionally, for a 20% adulteration in honey by DGGS and SGS separately, a notable percentage change of 3.8% and 6.6% respectively in the value of Inertia Moment (IM) is obtained. Thus, it is concluded that the above image processing algorithms can be used to detect 20% adulteration in pure honey.

Adulteration quantification of cheap honey in high-quality Manuka honey by two-dimensional correlation spectroscopy combined with deep learning

The adulteration of cheap honey in high value honey has a negative impact on consumer health, and its detection is also a challenging task. A fast, non-invasive and cost-effective spectroscopic strategy was proposed to quantify the honey fraud based on the two-dimensional correlation spectroscopy (2D-COS) of Raman spectra combined with multiple deep learning techniques. 700 Raman spectra of Manuka, acacia and multi-floral honeys were collected, and the corresponding synchronous, asynchronous and integrative correlation spectra were obtained. The t-distributed stochastic neighbor embedding (t-SNE) and partial least square regression (PLSR) were used to analyze one-dimensional spectra and 2D-COS image datasets of the same sample, demonstrating that 2D-COS can highlight the complex fingerprint features of samples and thus contribute to the spectral characterization. The combination of synchronous 2D-COS and deep residual shrinkage networks (DRSN) achieved the best performance compared to the other models, with the root mean square errors of prediction (RMSEP) of 3.1166 for Manuka honey and 2.3188 for acacia honey, respectively. The impressive performance of the proposed spectral method is sufficient to quantify the adulteration of cheap honey. This study provides a novel alternative for relevant detection tasks and facilitates to ascertain the authenticity of high-value honey products.

Plasmonic sensor for rapid detection of water adulteration in honey and quantitative measurement of lactose concentration in solution

In this article, a novel surface plasmon polariton-based plasmonic refractive index sensor is proposed and numerically investigated. It consists of a structure of an octagonal cavity resonator inside a square disk along with silver nanodots and two slits which are coupled to a straight metal–insulator–metal (MIM) waveguide arrangement. The finite element method (FEM) has been employed to perform a numerical analysis of the proposed structure in the near-IR (Infrared) and mid-IR spectrum. The sensor detects changes in refractive index in terms of changes in resonant wavelength which can be measured using an optical spectrum analyzer. The recorded maximum sensitivity is 2527.6 nm per refractive index unit (RIU) with a sensing resolution of 3.956×10−7 RIU. The proposed sensor has been further numerically analyzed, and its ability to measure the quality of honey samples and detect lactose concentration in a solution has been verified.

Straightforward monitoring of honey with foreign diastase by leveraging the differentiation in LC-UV proteome profiles of authentic and fraudulent samples

Honey adulteration using sugar syrups is ubiquitous and adulterated honey may also be prepared by direct addition of α-amylases from different sources to match the legislation for honey trade. Colorimetric diastase assays may report false negatives in terms of foreign diastase (FD) detection owing to using substrates being prone to be cleaved by any α-amylases. The main objective of this study was to develop a reliable analytical method to determine FD contained honey. For this purpose, intrinsic honey proteins were first isolated, enriched, and cleaned up via experimentally designed serial ultrafiltration methodology. Next, diastase and invertase as abundant honey enzymes were purified simultaneously from obtained crude protein isolate by means of FPLC and novel purification protocol. Total protein and enzyme activity assays along with SDS-PAGE analysis were conducted for purity check. Subsequently, two purity-confirmed enzymes were used as analytical references in method development. With the hyphenation of UV detection, optimized chromatographic resolution, and practical dilute & shoot sample pretreatment, Foreign Amylase Monitoring (FAM) method has been introduced for routine analysis. Purified authentic enzymes were labeled in LC-UV chromatogram of honey proteome profile to distinguish any unnatural enzyme/protein signal. According to the validation of the FAM method, precise (RSDR; 1.27%), and linear (mean R2 >0.995) results were able to obtain. Commercial honey samples (n = 202) collected over 3 consecutive years were probed using the FAM method and industrial α-amylases were also analyzed for verification. The diastase activities were also measured prior to FAM application to elucidate if any positive correlation. The developed method rapidly and reliably detected the presence of FD qualitatively. A total of 74 samples were found to contain FD. It was observed that the presence of FD was correlated in samples with abnormally high diastase activities and an apparent FD peak was identified readily in all enzyme adulterated samples. The developed FAM method and performed trend analysis allowed us to decipher a dramatic increase in FD existence and this revealed the recently emerging problem of honey authenticity.

Identification of adulterated honey with different varieties and concentrations based on hyperspectral imaging technology combined with deep learning

Honey is an important agricultural and sideline product in China, which contains the high nutritional value and commercial value. An advanced honey adulteration identification model can help beekeepers and consumers better identify honey and avoid losses. Given the complex operation and high technical requirements of traditional honey identification experimental instruments, a new model for quickly and efficiently identifying honey adulteration with different kinds and concentrations was developed in this study. Based on the data of adulterated honey with different varieties and concentrations obtained by hyperspectral imaging technology, a large number of regions of interest were randomly selected as samples. Then, the classification model is established by adopting preprocessing methods such as standardization, centralization, multivariate scattering correction, standard variable transformation, first-order difference, and second-order difference, combining the advantages of convolutional neural network and support vector machine. Finally, the accuracy of the results is compared, and D1-CNN-SVM is determined as the best classification prediction model, with an accuracy rate of 100%. At the same time, through the analysis of the confusion matrix of experimental results, this study summarizes the difficulties in identifying adulterated honey of different varieties and concentrations.

Rapid and Automated Method for Detecting and Quantifying Adulterations in High-Quality Honey Using Vis-NIRs in Combination with Machine Learning.

Honey is one of the most adulterated foods, usually through the addition of sweeteners or low-cost honeys. This study presents a method based on visible near infrared spectroscopy (Vis-NIRs), in combination with machine learning (ML) algorithms, for the correct identification and quantification of adulterants in honey. Honey samples from two botanical origins (orange blossom and sunflower) were evaluated and adulterated with low-cost honey in different percentages (5%, 10%, 15%, 20%, 25%, 30%, 35%, 40%, 45%, and 50%). The results of the exploratory analysis showed a tendency to group the samples according to botanical origin, as well as the presence of adulteration. A supervised analysis was performed to detect the presence of adulterations. The best performance with 100% accuracy was achieved by support vector machines (SVM) and random forests (RF). A regression study was also carried out to quantify the percentage of adulteration. The best result was obtained by support vector regression (SVR) with a coefficient of determination (R2) of 0.991 and a root mean squared error (RMSE) of 1.894. These results demonstrate the potential of combining ML with spectroscopic data as a method for the automated quality control of honey.

FTIR-ATR detection method for emerging C3-plants-derivated adulterants in honey: Beet, dates, and carob syrups

The European Union Publications Office has recently presented a report on the European Union's coordinated action with the Joint Research Centre to determine certain fraudulent practices in the honey sector, in which it has been indicated that 74% of the samples analyzed, imported from China, and 93% of the samples analyzed, imported from Turkey, the two largest honey producers worldwide, presented at least one indicator of exogenous sugar or suspicion of being adulterated. This situation has revealed the critical state of the problem of honey adulteration worldwide and the need to develop analytical techniques for its detection. Even though the adulteration of honey is carried out in a general way with sweetened syrups derived from C4 plants, recent studies have indicated the emerging use of syrups derived from C3 plants for the adulteration of honey. This kind of adulteration makes it impossible to analyze its detection using official analysis techniques. In this work, we have developed a fast, simple, and economical method based on the Fourier transform infrared spectroscopy technique, with attenuated total reflectance, for the qualitative, quantitative, and simultaneous determination of beetroot, date, and carob syrups, derived from of C3 plants; whose available bibliography is very scarce and analytically not very conclusive for its use by the authorities. The proposed method has been based on the establishment of the spectral differences between honey and the mentioned syrups at eight different points in the spectral region between 1200 and 900 cm−1 of the mid-infrared, characteristic of the vibrational modes of carbohydrates in honey, which allows the pre-discrimination of the presence or absence of the syrups studied, and their subsequent quantification, with precision levels lower than 2.0% of the relative standard deviation and relative errors lower than 2.0% (m/m).

Rapid Identification of Corn Sugar Syrup Adulteration in Wolfberry Honey Based on Fluorescence Spectroscopy Coupled with Chemometrics.

Honey adulteration has become a prominent issue in the honey market. Herein, we used the fluorescence spectroscopy combined with chemometrics to explore a simple, fast, and non-destructive method to detect wolfberry honey adulteration. The main parameters such as the maximum fluorescence intensity, peak positions, and fluorescence lifetime were analyzed and depicted with a principal component analysis (PCA). We demonstrated that the peak position of the wolfberry honey was relatively fixed at 342 nm compared with those of the multifloral honey. The fluorescence intensity decreased and the peak position redshifted with an increase in the syrup concentration (10-100%). The three-dimensional (3D) spectra and fluorescence lifetime fitting plots could obviously distinguish the honey from syrups. It was difficult to distinguish the wolfberry honey from another monofloral honey, acacia honey, using fluorescence spectra, but it could easily be distinguished when the fluorescence data were combined with a PCA. In all, fluorescence spectroscopy coupled with a PCA could easily distinguish wolfberry honey adulteration with syrups or other monofloral honeys. The method was simple, fast, and non-destructive, with a significant potential for the detection of honey adulteration.

Chemical and functional characteristics to detect sugar syrup adulteration in honey from different botanical origins

ABSTRACT The objective of this study was to detect the changes in physicochemical and functional properties of raw and processed divergent honey varieties after adulterating with different sugar syrups. Specifically, when 25% to 55% cane syrup was added to raw Acacia honey, the HMF content increased from 46.25 to 101.6 mg/kg. In processed honey that contained 55% cane syrup, HMF content was even higher, reaching up to 402.47 mg/kg. The reported values for HMF (>80 mg/kg) content and DN (>8 DN) were above the described quality evaluation standard of honey. The results indicated that adulterating Acacia honey with up to 55% corn and corn syrup caused a reduction in diastase content to below 3.5 DN, and an increase in HMF content above 93.28 mg/kg. Similarly, adding 10% corn syrup to raw Ziziphus honey resulted in a decrease in both DPPH and ABTS+ values from 83.16% and 88.58%, respectively, to 72.83% and 76.97%. The findings in this study demonstrated that the addition of malt syrup (55%) to raw Trifolium honey resulted in an increase in the phytochemical content, with the TPC value reaching 805.15 mg GAE/100 g. However, processing Trifolium honey caused a significant decrease in TPC content to 505.15 mg GAE/100 g. This work highlights the need for more research into unexplored honey varieties and adulteration methods to improve quality evaluation standards and mitigate authenticity issues.

Safety and quality of natural bee honey produced under different trademarks as sold in supermarkets

Against the background of the globalization of economic relations between countries, the activation of international trade is becoming urgent. In view of this, one of the main tasks for Ukraine as a member of the WTO and in connection with the prospect of its accession to the EU is the harmonization of national regulatory and legal requirements with international ones in the field of food safety and quality. The purpose of the research is to assess the safety and quality of honey of various types of individual domestic producers and to develop express, efficient methods for determining the quality of honey using the POCKET-PAL-3 refractometer and the safety of honey for establishing its adulteration with sodium hydrogen carbonate, alkaline detergents using indicators, as well as beet sugar by microscopy. The object of the study was the assessment of organoleptic, physico-chemical, microbiological parameters of natural bee honey of different botanical origins and varieties and detection of its adulteration, which was sold in supermarkets from various domestic manufacturers under the following trademarks. Analytical, organoleptic, physicochemical, microbiological, radiological, statistical methods of research were used to implement the research goal. Honey produced by different manufacturers according to organoleptic indicators: appearance, color, consistency, smell and taste, met the requirements of DSTU 4497:2005, except for sample №. 3, in which a discrepancy in taste was found. At the same time, according to physical and chemical parameters, honey samples №. 1, 2, 4 and 5 met the requirements of DSTU 4497:2005, except for sample №. 3 - by mass fraction of sucrose (to anhydrous substance) - 6.48±0.04 %, which did not meet the requirements of the current DSTU, the increase was 8.00 %. According to microbiological parameters and specific activity of Cs-137, it was established that all honey samples met the requirements of the national standard of Ukraine (DSTU 4497:2005). Tests were conducted to detect the adulteration of honey with sodium bicarbonate using the bromocresol green indicator, alkaline detergents using the chrome dark blue indicator, and beet sugar adulteration. For the express determination of the qualitative indicator - the mass fraction of honey, we used the POCKET-PAL-3 refractometer (Poland) for the first time. So, for the first time, we used the POCKET-PAL-3 refractometer (Poland) for the express determination of the mass fraction of water, which provides an opportunity to determine the mass fraction of dry matter with the subsequent calculation of the mass fraction of water, which ensures the reliability of the obtained indicators in 99.9 % compared to the indicators, established using a URL refractometer (model 1) in accordance with the requirements of DSTU 4497, and for the first time, patented methods of establishing the adulteration of honey with sodium bicarbonate, alkaline detergents were applied, the probability of the obtained indicators was 99.9 %, as well as adulteration with beet sugar by the microscopy method. Key words: natural honey, honey production capacity, organoleptic, physicochemical, microbiological parameters, safety, quality, consumer.

Pivot© and check‐all‐that‐apply techniques for the analysis of honey adulteration: impact on consumer liking, emotions and memories

SummaryThe study aimed to combine Pivot© Profile and Check‐all‐that‐apply (CATA) sensory techniques to determine the sensory attributes of adulterated honey and the impact of honey adulteration on consumer liking, emotions, and memories. Consumers (130) performed the Pivot technique to generate the sensory vocabulary. The CATA technique was then used to filter the relevant attributes from samples. This process allowed to reduce the preliminary list from Pivot© Profile (50 attributes) to 12 relevant attributes. Consumers determined liking, emotions and memories on each sample. Regarding preference, the pivot samples and honey with 20% and 40% adulteration were located in the 40% and 80% preference regions in the PREFMAP. The adulterated honey with 60% and 80% had the lowest preference, causing their total rejection. This may be associated with emotions (Aggressive, Disgusted and Bored) and negative memories (Pain, Interpersonal conflict, and Poverty). The combined use of Pivot© and CATA sensory techniques allows for the identification of sensory attributes associated with adulteration. An increase in honey adulteration was associated with negative emotions and memories evoked in consumers that may lead to the rejection of honey.

Physicochemical Properties and Botanical Sources of Honey from Different Areas of Ethiopia: An Implication for Quality Control

Honey is one of the important food commodities due to its nutritional and medicinal values. However, the issue of its quality and authenticity remain as important factors in consumption and marketing. This study was aimed at determining the botanical sources and quality parameters of honey samples collected from different areas of Ethiopia. The botanical sources of honey were analyzed using the melissopalynological method. Sugar profiles were analyzed using HPLC, and physicochemical properties were determined following the harmonized methods of honey analysis. Diverse plant species, including Schefflera abyssinica, Eucalyptus spp., Guizotia abyssinica., Echinops spp., Coffee arabica, Bersama abyssinica, and Rumex spp., were identified as dominant sources of honey. However, honey from the Gimbo district contains no pollen fingerprints, and its source has remained unknown. The average values of honey sugar compositions ranged from 30.3–46.7%, 29.9–40.3%, 15.0–22.2%, and 0.28–4.4% for fructose, glucose, sucrose, and maltose, respectively. Although the quality parameter values of most honeys fit the acceptable range of national and international honey quality standards, honeys collected from Guassa district revealed some abnormal characteristics. This abnormality may be related with poor handling, processing, or suspected for honey adulteration. Thus, regular testing and monitoring of honey quality are crucial in order to maintain its natural properties as well as control the current widespread practice of honey adulteration in Ethiopia.

Identification of novel unique mānuka honey markers using high-resolution mass spectrometry-based metabolomics

Mānuka honey is a valuable commodity produced by bees foraging the flowers of Leptospermum scoparium, a bush native to New Zealand and Australia. Due to its high value and proven health benefits, authenticity fraud in the sale of this food is a significant risk, as recounted in the literature. Four compulsory natural products must be present at minimum concentrations to authenticate mānuka honey (3-phenyllactic acid, 2′-methoxyacetophenone, 2-methoxybenzoic acid, and 4-hydroxyphenyllactic acid). However, spiking other kinds of honey with these compounds and/or the dilution of mānuka honey with other varieties may result in fraud going undetected. In this work, liquid chromatography coupled with high-resolution mass spectrometry and a metabolomics-based strategy has allowed us to tentatively identify 19 natural products –putative mānuka honey markers–, nine of which are reported for the first time. Chemometric models applied to these markers allowed the detection of both spiking and dilution fraud attempts of mānuka honey, even at 75% mānuka honey purity. Thus, the herein-reported methodology can be employed in the prevention and detection of mānuka honey adulteration even at low levels, and the tentatively identified markers presented in this work proved valuable for mānuka honey authentication procedures.