Detection techniques for adulterants in honey: Challenges and recent trends

Brazilian honey: Metabolomic analysis and characterization by 1D- and 2D-nuclear magnetic resonance (NMR) spectroscopy and chemometrics

Honey is a complex matrix that contains a wide range of compounds. This rich composition is influenced by diverse environmental factors, including geographic and botanical origin. Honey has been among the most commonly tampered foods worldwide, with improvements in techniques to do it. Accordingly, there is a recurring need for new techniques and methods to assess the honey's metabolic profiles to distinguish adulterated from non-tampered samples. In this sense, this study aimed to determine the chemical profiles of honey samples from the eleven agroecological zones of the Santa Catarina State (southern Brazil), collected in the 2019-2020 and 2020-2021 harvest seasons through 1D- and 2D-NMR. As a result, a series of metabolites was identified and their concentrations measured in samples. Further, the metabolomic dataset was used for building descriptive models through chemometric techniques, in order to discriminate honey samples according to their geographic and botanical origins and harvest season effect. Twenty-one metabolites were identified, with predominance of glucose and fructose in all samples. Two other carbohydrates (sucrose and maltose) were identified in lower concentrations, in addition to amino acids, organic acids, ketone, alcohol, ester, and alkaloids. No discrepant 1H NMR resonances that could indicate fraud were detected in the spectra. By PCA, it was possible to find clusters with similar geographic origins, i.e., agroecological zones, and botanical origins. In this regard, patterns of composition were detected for honey samples of Eucalyptus spp. and Hovenia dulcis species, which presented acetoin and kynurenate, respectively, in higher concentrations. Taking together, the results allowed demonstrating that NMR spectroscopy coupled to chemometrics is an effective experimental approach to characterize Brazilian honey regarding their geographic origin and season of collection, despite the huge floral diversity available in that country for bee forage.

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.

Detecting Honey Adulteration: Advanced Approach Using UF-GC Coupled with Machine Learning

This article introduces a novel approach to detecting honey adulteration by combining ultra-fast gas chromatography (UF-GC) with advanced machine learning techniques. Machine learning models, particularly support vector regression (SVR) and least absolute shrinkage and selection operator (LASSO), were applied to predict adulteration in orange blossom (OB) and sunflower (SF) honeys. The SVR model achieved R2 values above 0.90 for combined honey types. Treating OB and SF honeys separately resulted in a significant accuracy improvement, with R2 values exceeding 0.99. LASSO proved especially effective when honey types were treated individually. The integration of UF-GC with machine learning not only provides a reliable method for detecting honey adulteration, but also sets a precedent for future research in the application of this technique to other food products, potentially enhancing food authenticity across the industry.

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.

High-resolution proteomics and machine-learning identify protein classifiers of honey made by Sicilian black honeybees (Apis mellifera ssp. sicula)

Apis mellifera ssp. sicula, also known as the Sicilian black honeybee, is a Slow Food Presidium that produces honey with outstanding nutraceutical properties, including high antioxidant capacity. In this study, we used high-resolution proteomics to profile the honey produced by sicula and identify protein classifiers that distinguish it from that made by the more common Italian honeybee (Apis mellifera ssp. ligustica). We profiled the honey proteome of genetically pure sicula and ligustica honeybees bred in the same geographical area, so that chemical differences in their honey only reflected the genetic background of the two subspecies, rather than botanical environment. Differentially abundant proteins were validated in sicula and ligustica honeys of different origin, by using the so-called "rectangular strategy", a proteomic approach commonly used for biomarker discovery in clinical proteomics. Then, machine learning was employed to identify which proteins were the most effective in distinguishing sicula and ligustica honeys. This strategy enabled the identification of two proteins, laccase-5 and venome serine protease 34 isoform X2, that were fully effective in predicting whether honey was made by sicula or ligustica honeybees. In conclusion, we profiled the proteome of sicula honey, identified two protein classifiers of sicula honey in respect to ligustica, and proved that the rectangular strategy can be applied to uncover biomarkers to ascertain food authenticity.

Purity Assessment of Honey Based on Compound Specific Stable Carbon Isotope Ratios Obtained by LC-IRMS

BACKGROUND

The use of stable carbon isotope ratios (δ13C) of sugar fractions of honey is a powerful tool to detect adulteration with sugar syrups. This is accomplished by calculating differences of the δ13C values between individual honey saccharides and comparing them to published purity criteria. A liquid chromatography-isotope ratio mass spectrometry (LC-IRMS) method for the determination of δ13C values of sugars in honey was previously validated by an interlaboratory comparison, but no further guidance was given how to include the obtained precision figures of the compound-specific δ13C values in the purity assessment of honey.

OBJECTIVE

To use existing data to estimate the standard deviation of the repeatability (sr) and reproducibility (sR) of differences (Δ δ13C) between the δ13C values of individual honey saccharides.

METHODS

Previously published δ13C values were used to calculate differences (Δ δ13C values) between δ13C fructose-δ13C glucose, δ13C glucose-δ13C disaccharides, etc. in a honey sample; sr and sR of Δ δ13C values were calculated according to ISO 5725-2:2019.

RESULTS

The Δ δ13C sr and sR values were essentially of the same magnitude as the sr and sR values of δ13C values of the sugar fractions. The precision of the Δ δ13C values was used to estimate the critical difference for comparing a test result with a reference value according to ISO 5725-6:1994. This varied between 0.26 and 1.10‰.

CONCLUSION

The estimated critical differences can be used to determine whether a honey test result complies with published Δ δ13C purity criteria.

HIGHLIGHT

The proposed procedure will increase confidence in decisions based on compound-specific δ13C values regarding the conformity of honey with published purity criteria.

Exploring the role of NIR spectroscopy in quantifying and verifying honey authenticity: A review

Honey, recognized for its diverse flavors and nutritional benefits, confronts challenges in maintaining authenticity and quality due to factors like adulteration and mislabelling. This review undertakes a comprehensive exploration of the utility of Near-Infrared (NIR) spectroscopy as a non-destructive analytical method for concurrently evaluating both honey quantity and authenticity. The primary purpose of this investigation is to delve into the various applications of NIR spectroscopy in honey analysis, with a specific focus on its capability to identify and quantify significant quality parameters such as sugar content, moisture levels, 5-HMF, and proline content. Results from the study underscore the effectiveness of NIR spectroscopy, especially when integrated with advanced chemometrics models. This combination not only facilitates quantification of diverse quality parameters but also enhances the classification of honey based on geographical and botanical origin. The technology emerges as a potent tool for detecting adulteration, addressing critical challenges in preserving the authenticity and quality of honey products. The impact of this critical analysis extends to shedding light on the current state, challenges, and future prospects of applying NIR spectroscopy in the honey industry. This analysis outlines the current challenges and future prospects of NIR spectroscopy in the honey industry. Emphasizing its potential to improve consumer confidence and food safety, the research has broader implications for authenticity and quality assurance in honey. Integrating NIR spectroscopy into industry practices could establish stronger quality control measures, benefiting both producers and consumers globally.

The Contribution of Botanical Origin to the Physicochemical and Antioxidant Properties of Algerian Honeys

Honeys from different regions of Algeria were analyzed to determine their pollen characteristics and physicochemical properties (humidity, pH, electrical conductivity, diastase content, color, phenols, flavonoids and antioxidant activity). The antioxidant activity was investigated using the free radical scavenging and Ferric reducing/antioxidant power assays. The melissopalynological analysis revealed 129 pollen types from 53 botanical families. The pollen types found as dominant were Coriandrum, Bupleurum, Brassica napus type, Hedysarum coronarium, Ceratonia siliqua, Eucalyptus, Peganum harmala, Ziziphus lotus and Tamarix. Principal component analysis and cluster analysis were used to analyze significant relationships between the physicochemical variables and the botanical origin of the honeys and establish groupings based on the similarities of their physicochemical and antioxidant properties. The results showed that Ceratonia siliqua, Eucalyptus, Arbutus and honeydew honeys had a higher antioxidant contribution and higher phenolic and flavonoid contents than the rest of the honeys. In addition, the contributions of Mediterranean vegetation such as Myrtus and Phyllirea angustifolia were significant in this honey group. This paper demonstrates the diverse botanical variability for honey production in Algeria. However, there is a gap in its characterization based on its botanical origin. Therefore, these studies contribute positively to the needs of the beekeeping sector and the commercial valorization of the country's honey.

Aqueous two-phase systems coupled with chemometrics-enhanced HPLC-DAD for simultaneous extraction and determination of flavonoids in honey

In this study, an accurate, rapid, green, and environment friendly method for the extraction and quantitative analysis of flavonoids in honey was established by using the aqueous two-phase extraction combined with the chemometrics-assisted HPLC-DAD. The first purpose of this study was to extract seven flavonoids in five different types of honey using alcohol/salt aqueous two-phase system (ATPS). The system with 2.82 mL sodium citrate (30%), 1.58 mL water, and 3.10 mL isopropanol, showed the highest flavonoids extraction yields in the top phase (87.66-101.50%). Additionally, the three-way array of honey samples based on HPLC-DAD was decomposed mathematically by the alternating trilinear decomposition (ATLD) algorithm to obtain reasonable chromatograms, spectra, and concentration profiles for each analyte. Compared with the traditional solid-phase extraction method, the ATPS-ATLD-based method showed satisfactory spiked recoveries, lower limit of detection, and higher sensitivity, further verifying its accuracy and stability.

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.

FT-MIR-ATR Associated with Chemometrics Methods: A Preliminary Analysis of Deterioration State of Brazil Nut Oil

Brazil nut oil is highly valued in the food, cosmetic, chemical, and pharmaceutical industries, as well as other sectors of the economy. This work aims to use the Fourier transform infrared (FTIR) technique associated with partial least squares regression (PLSR) and principal component analysis (PCA) to demonstrate that these methods can be used in a prior and rapid analysis in quality control. Natural oils were extracted and stored for chemical analysis. PCA presented two groups regarding the state of degradation, subdivided into super-degraded and partially degraded groups in 99.88% of the explained variance. The applied PLS reported an acidity index (AI) prediction model with root mean square error of calibration (RMSEC) = 1.8564, root mean square error of cross-validation (REMSECV) = 4.2641, root mean square error of prediction (RMSEP) = 2.1491, R2cal (calibration correlation coefficient) equal to 0.9679, R2val (validation correlation coefficient) equal to 0.8474, and R2pred (prediction correlation coefficient) equal to 0, 8468. The peroxide index (PI) prediction model showed RMSEC = 0.0005, REMSECV = 0.0016, RMSEP = 0.00079, calibration R2 equal to 0.9670, cross-validation R2 equal to 0.7149, and R2 of prediction equal to 0.9099. The physical-chemical analyses identified that five samples fit in the food sector and the others fit in other sectors of the economy. In this way, the preliminary monitoring of the state of degradation was reported, and the prediction models of the peroxide and acidity indexes in Brazil nut oil for quality control were determined.

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.

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.

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).

Honey authentication and adulteration detection using emission - excitation spectra combined with chemometrics

The aim of this study was to evaluate the usefulness of emission-excitation matrices for honey authentication and adulteration detection. For this purpose, 4 types of authentic honeys (tilia, sunflower, acacia and rape) and samples adulterated with different adulteration agents (agave, maple, inverted sugar, corn and rice in different percentages - 5%, 10% and 20%) were analysed. Each honey type and each adulteration agent exhibit unique emission-excitation spectra that can be used for the classification according to the botanical origin and for the detection of adulteration. The principal component analysis clearly separated the rape, sunflower and acacia honeys. The partial least squares - discriminant analysis (PLS-DA) and support vector machines (SVM) were used in a binary mode to separate the authentic honeys from the adulterated ones, and the SVM proved to separate much better than PLS-DA.

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.

Rapid, reliable and easy-to-perform chemometric-less method for rice syrup adulterated honey detection using FTIR-ATR

The adulteration of honey (Apis mellifera) is a global problem due to its economic, commercial and health implications. The world's leading beekeeping organisation, APIMONDIA, considers that the detection of adulteration in honey is a problem that has not yet been resolved. This evidence of the importance of the intensive development of analytical techniques that allow the unequivocal detection of adulterants in honey, especially those whose use as honey adulterants has recently emerged. This work aims to develop a fast, easy-to-perform, low-cost analytical method to qualitatively and quantitatively determine rice syrup using the Fourier transform infrared spectroscopy (FTIR) technique with attenuated total reflectance (ATR) mode without complex mathematical procedures and sophisticated sample preparation. This study involved the analysis of 256 intentionally rice-syrup-adulterated honey samples and 92 pure honey samples of bee multifloral honey from Spain. The method, based strictly on the determination of the absorbance directly from the samples, at 1013 cm^-1 The methodology used no need for previous treatments or preparations and demonstrated the scope for the unequivocal detection of rice syrup in adulterated honey containing equal to or higher than 3% (m/m) or more of this adulterant. Using the Exponential Plus Linear model (r = 0.998) shows high accuracy and precision, in terms of relative error (0.32%, m/m) and coefficient of variation (1.4%). The results of this study have led to the establishment of a maximum absorbance threshold of 0.670 for honey without rice syrup.

Hyperspectral Microscopy Technology to Detect Syrups Adulteration of Endemic Guindo Santo and Quillay Honey Using Machine-Learning Tools

Honey adulteration is a common practice that affects food quality and sale prices, and certifying the origin of the honey using non-destructive methods is critical. Guindo Santo and Quillay are fundamental for the honey production of Biobío and the Ñuble region in Chile. Furthermore, Guindo Santo only exists in this area of the world. Therefore, certifying honey of this species is crucial for beekeeper communities-mostly natives-to give them advantages and competitiveness in the global market. To solve this necessity, we present a system for detecting adulterated endemic honey that combines different artificial intelligence networks with a confocal optical microscope and a tunable optical filter for hyperspectral data acquisition. Honey samples artificially adulterated with syrups at concentrations undetectable to the naked eye were used for validating different artificial intelligence models. Comparing Linear discriminant analysis (LDA), Support vector machine (SVM), and Neural Network (NN), we reach the best average accuracy value with SVM of 93% for all classes in both kinds of honey. We hope these results will be the starting point of a method for honey certification in Chile in an automated way and with high precision.

Physicochemical Profile, Antioxidant and Antimicrobial Activities of Honeys Produced in Minas Gerais (Brazil)

Honeys can be classified as polyfloral or monofloral and have been extensively studied due to an increased interest in their consumption. There is concern with the correct identification of their flowering, the use of analyses that guarantee their physicochemical quality and the quantification of some compounds such as phenolics, to determine their antioxidant and antimicrobial action. This study aims at botanical identification, physicochemical analyses, and the determination of total polyphenols, chromatographic profile and antiradical and antimicrobial activity of honey from different regions of Minas Gerais. Seven different samples were analyzed for the presence of pollen, and color determination. The physicochemical analyses performed were total acidity, moisture, HMF, reducing sugar, and apparent sucrose. The compound profile was determined by UHPLC/MS, the determination of total phenolics and antiradical activity (DPPH method) were performed by spectrophotometry, and minimum inhibitory and bacterial concentrations were determined for cariogenic bacteria. All honey samples met the quality standards required by international legislation, twenty compounds were detected as the main ones, the polyfloral honey was the only honey that inhibited all of the bacteria tested. Sample M6 (Coffee) was the one with the highest amount of total polyphenols, while the lowest was M4 (Cipó-uva). Regarding the antioxidant activity, M5 (Velame) had the best result and M4 (Cipó-uva) was the one that least inhibited oxidation. Of the polyfloral honeys, there was not as high a concentration of phenolic compounds as in the others. Coffee, Aroeira, Velame and Polyfloral have the best anti-radical actions. Betônica, Aroeira, Cipó-uva and Pequi inhibited only some bacteria. The best bacterial inhibition results are from Polyfloral.

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.

NMR Metabolite Profiling in the Quality and Authentication Assessment of Greek Honey—Exploitation of STOCSY for Markers Identification

Honey is a natural, healthy commodity and is probably among the most complex foods produced by nature. It is the oldest recorded and certainly the only natural sweetener that can be used by humans without any further processing. Nowadays, the increase in honey’s value, along with its growing list of healthy attributes, has made the present raw material a prime target for adulteration. In the current study, NMR-based metabolite profiling in combination with chemometrics was applied in the quality control of Greek honeys from northeastern Aegean islands. Moreover, statistical total correlation spectroscopy (STOCSY) was employed for the first time as a dereplication and structural elucidation tool in the honey biomarker identification process. A total of 10 compounds were successfully identified in honey total extracts via 1H NMR spectroscopy. Compounds such as 5-(hydroxymethyl)furfural, methyl syringate, a mono-substituted glycerol derivative and 3-hydroxy-4-phenyl-2-butanone, among others, were identified as potential biomarkers related to the botanical and geographical origin of the samples. High-Resolution Mass Spectrometry (HRMS) was used as an additional verification tool on the identified compounds.

Rapid and accurate discrimination between pure and adulterated commercial Indian Honey brands using FTIR spectroscopy and principal component analysis

Background:

Four leading commercial Indian honey brands were investigated using FTIR spectroscopy and principal component analysis for rapid and accurate differentiation of pure, mildly adulterated, and highly adulterated honey brand samples.

Methods:

This study is the first to investigate commercial Indian honey brands using FTIR and PCA.

Results:

Hence such methods can investigate adulterations in bulk commercial honey brand samples where sophisticated instrumentations and facilities are not available.

Conclusion:

Thus, the potential of FTIR and PCA can be further used for detecting the presence of adulterations in bulk honey samples without much cost and efforts.

Use of magic sandwich echo and fast field cycling NMR relaxometry on honey adulteration with corn syrup

BACKGROUND

Adulteration is defined as the intentional addition of a material that is not a part of the nature. In this study, a non-conventional time domain nuclear magnetic resonance (TD-NMR) pulse sequence: magic sandwich echo (MSE) was used to detect the adulteration of honey by glucose syrup (GS) and high fructose corn syrup (HFCS) accompanied with T₁ and T₂ relaxation times. Also, fast field cycling NMR (FFC-NMR) relaxometry and multivariate analysis were performed to investigate the adulteration.

RESULTS

Higher maltose in GS and changing glucose to water ratio of HFCS gave high correlation with the crystal content values. In HFCS adulteration, two separate populations of protons having different T₂ values were detected and T₁ times were also used to determine GS adulteration. Addition of GS increased T₁ while addition of HFCS increased T₂ , significantly.

CONCLUSION

The results showed that it is possible to differentiate the unadulterated and adulterated honey samples by using TD-NMR relaxation times and crystal content values obtained by the MSE sequence. By FFC-NMR relaxometry, not only GS addition but also the amount of GS was examined. The multivariate analysis technique of principal component analysis was able to distinguish the types of adulterants. © 2021 Society of Chemical Industry.

Classification of Sidr honey and detection of sugar adulteration using right angle fluorescence spectroscopy and chemometrics

Sidr honey is vulnerable to adulteration with low-grade honey and sugar syrups, which compromises its nutritional and medicinal value, demanding fast and reliable analytical tools for quality assessment. In this study, fluorescence spectroscopy was employed to assess the quality of a honey samples, specifically, Sidr, unifloral (Acacia) and multifloral (Acacia, Carisa and Justicia) honey. Fluorescence spectroscopy revealed characteristic spectral signatures of Sidr honey, compared to Acacia and multifloral honey. In addition, cane sugar syrup was artificially added to Sidr honey at different concentrations. These spectral signatures were exploited for the machine-assisted classification of Sidr, sugar syrup and different concentrations of Sidr–sugar mixture. The bagging classification algorithm generated values of sensitivity and specificity close to unity, indicating its ability for efficient discrimination of the samples. Fluorescence spectroscopy in tandem with chemometrics could potentially be used as a rapid analytical tool to identify Sidr honey and its sugar adulteration.

Honey authenticity: the opacity of analytical reports - part 1 defining the problem

The composition of honey, a complex natural product, challenges analytical methods attempting to determine its authenticity particularly in the face of sophisticated adulteration. Of the advanced analytical techniques available, only isotope ratio mass spectrometry (IRMS) is generally accepted for its reproducibility and ability to detect certain added sugars, with nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HRMS) being subject to stakeholder differences of opinion. Herein, recent reviews of honey adulteration and the techniques to detect it are summarised in the light of which analytical reports are examined that underpinned a media article in late 2020 alleging foreign sugars in UK retailers' own brand honeys. The requirement for multiple analytical techniques leads to complex reports from which it is difficult to draw an overarching and unequivocal authenticity opinion. Thus arose two questions. (1) Is it acceptable to report an adverse interpretation without exhibiting all the supporting data? (2) How may a valid overarching authenticity opinion be derived from a large partially conflicting dataset?

Determination of honey adulterated with corn syrup by quantitative amplification of maize residual DNA using ultra-rapid real-time PCR

BACKGROUND

Honey is a naturally sweet syrup made by honeybees from floral nectar. However, high-fructose corn syrup has been prevalently used for the adulteration of honey. A novel molecular method was developed for the characterization of corn syrup-adulterated honey by specific amplification and quantification of maize residual DNA in honey. An ultra-rapid real-time polymerase chain reaction (UR-qPCR) system for rapid amplification and protocol for direct purification of residual DNA from honey were described.

RESULTS

Rapidity of maize DNA amplification was acquired within 20 min for a limit of detection of around three copies of targeted DNAs. The amplification of maize residual DNA in honeys adulterated with corn syrup from 5% to 80% (v/v) showed that a minimum rate of 10% adulteration can be identified, and Maize genomic DNA in 5 mL of adulterated honeys was from 13 ± 9 copies to 2478 ± 827 copies, respectively. However, the residual DNA of maize was also detected in natural honey produced in the region where pollen and nectar of maize were collected, and the quantity of maize genomic DNA in these natural honeys was in the range of 10% adulteration with corn syrup. Therefore, detection of both pollen and residual DNA of maize in honey is important in identifying the source of maize residual DNA present in honey.

CONCLUSION

A rapid PCR assay was first developed for the accurate detection and quantification of maize residual DNA in honey. It is a useful tool for specific identification of the corn syrup used for honey adulteration. Further studies on residual DNA in various types of corn syrup and specificity of primer are recommended. © 2021 Society of Chemical Industry.

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.

Detection of syrup adulterants in manuka and jarrah honey using HPTLC-multivariate data analysis

High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles’ datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey’s dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys’ sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model.

Statistical Analysis of Chemical Element Compositions in Food Science: Problems and Possibilities

In recent years, many analyses have been carried out to investigate the chemical components of food data. However, studies rarely consider the compositional pitfalls of such analyses. This is problematic as it may lead to arbitrary results when non-compositional statistical analysis is applied to compositional datasets. In this study, compositional data analysis (CoDa), which is widely used in other research fields, is compared with classical statistical analysis to demonstrate how the results vary depending on the approach and to show the best possible statistical analysis. For example, honey and saffron are highly susceptible to adulteration and imitation, so the determination of their chemical elements requires the best possible statistical analysis. Our study demonstrated how principle component analysis (PCA) and classification results are influenced by the pre-processing steps conducted on the raw data, and the replacement strategies for missing values and non-detects. Furthermore, it demonstrated the differences in results when compositional and non-compositional methods were applied. Our results suggested that the outcome of the log-ratio analysis provided better separation between the pure and adulterated data and allowed for easier interpretability of the results and a higher accuracy of classification. Similarly, it showed that classification with artificial neural networks (ANNs) works poorly if the CoDa pre-processing steps are left out. From these results, we advise the application of CoDa methods for analyses of the chemical elements of food and for the characterization and authentication of food products.

Results of an interlaboratory comparison of a liquid chromatography-isotope ratio mass spectrometry method for the determination of 13C/12C ratios of saccharides in honey

Background

Stable carbon isotope analysis of sugars in honey by LC–isotope ratio mass spectrometry (IRMS) is a useful tool for detecting adulteration of honey with extraneous sugar. Purity criteria based on ¹³C/¹²C ratios of saccharides in honey, determined by LC–IRMS of a large number of authentic honey samples, have been elaborated. However, no interlaboratory comparison (ILC) has yet been performed to estimate the precision of the method under reproducibility conditions.

Objective

To address this knowledge gap an ILC involving 14 laboratories and using six honey samples was conducted.

Methods

The participants were allowed to use their LC–IRMS-based method of choice for sample preparation and compound separation.

Results

The precision figures were estimated according to ISO 5725:1994. The repeatability relative standard deviation (RSD_r) for the determination of δ¹³C values of fructose and glucose varied between 0.3 and 0.5%, with 0.3 and 1.0% for disaccharides, and 0.7 and 2.8% for trisaccharides. The RSD_R varied between 0.8 and 1.8% for the monosaccharides, 1.0 and 1.5% for disaccharides, and 1.4 and 2.8% for trisaccharides.

Conclusion

Based on the obtained precision data the LC–IRMS method for the determination of ¹³C/¹²C ratios of saccharides in honey was considered fit for the conformity assessment of honey with established purity criteria.

Highlights

Precision estimates for a LC–IRMS method to determine ¹³C/¹²C ratios of saccharides in honey were obtained through an ILC. The data created can form the basis for the standardization of the method by interested standards-developing organizations for use in official control.

Comparison of ultraviolet and refractive index detections in the HPLC analysis of sugars

Refractive index (RI) detection is the standard approach for quantitatively detecting sugars via high-performance liquid chromatography (HPLC), while ultraviolet (UV) absorbance detection is the most commonly used detection method for general HPLC analysis. We compared the two detection approaches of UV and RI in the HPLC analysis of small sugars to investigate whether UV detection could be an alternative method to RI detection. UV detection was performed using a photodiode array scanning from 190 to 400 nm. We obtained comparable limit of detection (LOD) results for RI and UV detection in the HPLC analysis of monosaccharides, while HPLC-RI provided better LOD results than HPLC-UV in disaccharide analysis. Both HPLC-RI and HPLC-UV methods were applied to analyze a real honey sample, and similar results were obtained in terms of precision and recovery. The study conclusively shows that the UV-based HPLC analysis of sugars offers a sufficient alternative to RI-based HPLC analysis.

Honey authenticity: analytical techniques, state of the art and challenges

Honey is a high-value, globally consumed, food product featuring a high market price strictly related to its origin. Moreover, honey origin has to be clearly stated on the label, and quality schemes are prescribed based on its geographical and botanical origin. Therefore, to enhance food quality, it is of utmost importance to develop analytical methods able to accurately and precisely discriminate honey origin. In this study, an all-time scientometric evaluation of the field is provided for the first time using a structured keyword on the Scopus database. The bibliometric analysis pinpoints that the botanical origin discrimination was the most studied authenticity issue, and chromatographic methods were the most frequently used for its assessment. Based on these results, we comprehensively reviewed analytical techniques that have been used in honey authenticity studies. Analytical breakthroughs and bottlenecks on methodologies to assess honey quality parameters using separation, bioanalytical, spectroscopic, elemental and isotopic techniques are presented. Emphasis is given to authenticity markers, and the necessity to apply chemometric tools to reveal them. Altogether, honey authenticity is an ever-growing field, and more advances are expected that will further secure honey quality.

The Use of UV Spectroscopy and SIMCA for the Authentication of Indonesian Honeys According to Botanical, Entomological and Geographical Origins

As a functional food, honey is a food product that is exposed to the risk of food fraud. To mitigate this, the establishment of an authentication system for honey is very important in order to protect both producers and consumers from possible economic losses. This research presents a simple analytical method for the authentication and classification of Indonesian honeys according to their botanical, entomological, and geographical origins using ultraviolet (UV) spectroscopy and SIMCA (soft independent modeling of class analogy). The spectral data of a total of 1040 samples, representing six types of Indonesian honey of different botanical, entomological, and geographical origins, were acquired using a benchtop UV-visible spectrometer (190-400 nm). Three different pre-processing algorithms were simultaneously evaluated; namely an 11-point moving average smoothing, mean normalization, and Savitzky-Golay first derivative with 11 points and second-order polynomial fitting (ordo 2), in order to improve the original spectral data. Chemometrics methods, including exploratory analysis of PCA and SIMCA classification method, was used to classify the honey samples. A clear separation of the six different Indonesian honeys, based on botanical, entomological, and geographical origins, was obtained using PCA calculated from pre-processed spectra from 250-400 nm. The SIMCA classification method provided satisfactory results in classifying honey samples according to their botanical, entomological, and geographical origins and achieved 100% accuracy, sensitivity, and specificity. Several wavelengths were identified (266, 270, 280, 290, 300, 335, and 360 nm) as the most sensitive for discriminating between the different Indonesian honey samples.

Antibacterial activity and characterisation of some Egyptian honey of different floral origin

The aim of the current study was to evaluate the antibacterial activity and to analyse the physicochemical properties of some Egyptian honey of different botanical origin in comparison with Manuka honey from New Zealand. Antibacterial activity of Egyptian honey of different floral origin was evaluated against five reference bacterial strains including both Gram-positive and Gram-negative bacteria using well diffusion method. Pollen analysis was used to confirm the floral origin of honey. Meanwhile, the physicochemical parameters including total phenolic and total flavonoid contents were measured to assess the quality of honey. Some honey types including Flowers, Aashab, Bardakosh, and Black seed honey showed significant antibacterial activity against Staphylococcus aureus (ATCC 25923) when compared with clindamycin. The same types of honey, except Black seed honey exhibited significant antibacterial activity against Citrobacter diversus (ATCC 13315). The total phenolic and total flavonoid contents ranged from 130.5±9.0 to 175.3±11.3 mg GAE/100 g honey and 22.3±1.7–30.9±2.6 mg RE/100 g honey, respectively. The results indicated that Egyptian honey is a promising natural product that can be potentially used as an alternative to synthetic antibiotics. Authentication of honey through the investigation of its physicochemical characteristics is a very important determinant of its biological activity. Separation and investigation of the antimicrobial activity of each of the active compounds of honey will provide more information on the efficacy and the mechanism of its biological activity. Further studies are still needed to identify and standardise protocols for the use of honey either in the protection against or the treatment of microbial infections.

Characterization and classification of Romanian acacia honey based on its physicochemical parameters and chemometrics

Three groups of Romanian acacia honey, i.e., pure, directly adulterated (by mixing the pure honey with three sugar syrups), and indirectly adulterated (by feeding the bees with the same syrups), were characterized and discriminated based on their physicochemical parameters. Moisture, ash, 5-hydroxymethylfurfural (HMF), reducing sugars (fructose and glucose), and sucrose contents, free acidity, diastase activity, ratio between stable carbon isotopes of honey and its proteins (δ¹³C_H and δ¹³C_P) were evaluated. Adulteration led to a significant increase in sucrose content, HMF level, and Δδ¹³C = δ¹³C_H‒δ¹³C_P as well a decrease in reducing sugar content and diastase activity. Principal component analysis (PCA) and linear discriminant analysis (LDA) were applied to experimental data in order to distinguish between pure and adulterated honey. The most relevant discriminative parameters were diastase activity, HMF, sucrose, and reducing sugar contents. Posterior classification probabilities and classification functions obtained by LDA revealed that 100% of honey samples were correctly assigned to their original group.