Electrical impedance spectroscopy for potassium content analysis and botanical origin identification of honey.

Minerals are reported to dominate the electrical properties of honey and indicate its botanical and geographical origins. In this study, Electrochemical Impedance Spectroscopy (EIS) was used to assess the relation between mineral elements, electrical properties and botanical origin using three honey varieties - Citrus sp., Eucalyptus sp., and Erica sp. These varieties are identified through pollen analysis and market labelling. Flame atomic absorption and emission spectroscopies were used to quantify the concentrations of eight elements (potassium, sodium, calcium, magnesium, manganese, zinc, copper, and iron). Among all the mineral elements, potassium showed a consistent correlation with impedance. The potassium estimation in honey and standard solutions (calibration curve) had similar sensitivities of 153.43 nF/mM and 132.68 nF/mM, respectively. Additionally, the analysis revealed that potassium dominates the mineral composition, with the other species present in minimal quantities. The EIS technique showed high sensitivity to potassium and other ionisable species, making it possible to classify the botanical origin of these three honey types. The EIS technique proved to be both time and cost effective, yielding a classification rate higher than that achieved by analysing mineral composition.

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.

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.

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.

Fourier Transform Infrared Spectroscopy and Chemometrics for the Rapid Screening of Economically Motivated Adulteration of Honey Spiked With Corn or Rice Syrup.

Due to its high price, increased consumption, and limited production, honey has been a main target for economically motivated adulteration (EMA). An approach combining Fourier-Transform infrared spectroscopy (FTIR) and chemometrics was evaluated to develop a rapid screening tool to detect potential EMA of honey with either rice or corn syrup. A single-class soft independent modeling of class analogy (SIMCA) model was developed using a diverse set of commercial honey products and an authentic set of honey samples collected at four different U.S. Department of Agriculture (USDA) honey sample collection locations. The SIMCA model was externally validated with a set of calibration-independent authentic honey, typical commercial honey control samples, and those spiked with rice and corn syrups in the 1-16% concentration range. The authentic honey and typical commercial honey test samples were correctly predicted with an 88.3% classification rate. High accuracy was found in predicting the rice and corn syrup spiked samples above the 7% concentration range, yielding 97.6% and 94.8% correct classification rates, respectively. This study demonstrated the potential for a rapid and accurate infrared and chemometrics method that can be used to rapidly screen for either rice or corn adulterants in honey in less than 5 min.

A comprehensive overview of emerging techniques and chemometrics for authenticity and traceability of animal-derived food.

Authenticity and origin tracing of animal-derived food are particularly necessary due to various kinds of food fraud such as adulteration, counterfeiting, substitution and intentional mislabeling. This review focuses on the current research status of animal-derived food from the aspects of geographical origin, feeding ingredients and systems, adulteration of substitutes, and physical and chemical properties. The methods and statistical models involved in the research and their advantages and disadvantages are summarized. Stable isotope ratio analysis and element analysis are the most extensive used geographical traceability techniques. Spectroscopic techniques have the advantages of quick response, low cost and non-destructiveness. Instrument technology combined with chemometrics is the key method for origin traceability and authenticity of animal-derived food. In addition, there is a new trend of origin traceability by analyzing inedible parts of animal-derived food. This review intends to give a broad but comprehensive understanding in authenticity and geographical origin traceability of animal-derived food.

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.

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.

Comprehensive examination and comparison of machine learning techniques for the quantitative determination of adulterants in honey using Fourier infrared spectroscopy with attenuated total reflectance accessory.

Facile, robust, and accurate analyses of honey adulterants are required in the honey industry to assess its purity for commercialization purposes. A stacked regression ensemble approach using Fourier transform infrared spectroscopic method was developed for the quantitative determination of corn, cane, beet, and rice syrup adulterants in honey. A training set (n=81) was used to predict the percent adulterant composition of the aforementioned constituents in an independent test set (n=32). A comprehensive comparison of the performance of various machine learning techniques including support vector regression using linear function, least absolute shrinkage and selection operator, ride regression, elastic net, partial least squares, random forests, recursive partitioning and regression trees, gradient boosting, and gaussian process regression was assessed. The predictive performance of the aforementioned machine learning approaches was then compared with stacked regression, an ensemble learning technique which collates the performance of the various abovementioned techniques. Results show that stacked regression did not primarily outperform other techniques across all four syrup adulterant constituents in the testing set data. Further, elastic net generalized linear model generated the optimum results (Rootmeansquareerrorofprediction(RMSEP)average=0.0107,Raverage2=0.809) across all four honey adulterant constituents. Elastic net coupled with Fourier transform infrared spectroscopy may offer a novel, direct, and accurate method of simultaneously quantifying corn, cane, beet, and rice syrup adulterants in honey.

Addressing the unfulfilled codex standard for honey for stingless bee honey through lyophilization.

Some studies have found that the nutritional values of stingless bee honey (SBH) may be similar if not more than normal honey, prompting the Malaysian government to promote it as a superfood. However, SBH does not fulfil the Codex Standard for Honey (CODEX STAN 12-19811) in terms of moisture content and the lack of protein to be analysed with Internal Standard Carbon Isotope Ratio Analysis (ISCIRA). Hence, a lyophilization process was introduced prior to stable carbon isotope analysis of SBH to address both of these issues. It was found that once moisture content was decreased to a level below 20 % for 19 SBH samples, the percentage increment of protein extracted from the samples varied between 6 and 385 % relative to protein extracted from SBH before lyophilization with nine samples found to be adulterated. Caution is necessary when lyophilizing the SBH as significant isotope shifts were seen for SCIRA and ISCIRA values. Nevertheless, the carbon isotope shifts did not change the final outcome of the 'pass' or 'fail' of the adulteration result. Overall, the removal of water from SBH is required but caution is necessary as carbon isotope shifts were observed as SBHs underwent the lyophilization process.

Detecting adulteration of stingless bee honey using untargeted 1H NMR metabolomics with chemometrics.

As stingless bee honey (SBH) is gaining in popularity in the Malaysian market, it is now prone to adulteration. The higher price of SBH compared to floral honey has led to the use of unusual adulterants such as vinegar and even floral honey to mimic the unique taste and appearance of SBH. Since the current AOAC 998.12 method fails to detect these adulterants as their δ13C values are in the range for C3 plants, untargeted 1H NMR metabolomics was proposed. Principal component analysis of SBH 1H NMR fingerprints was able to distinguish authentic SBHs from adulterated ones down to 1% adulteration level for selected adulterants. Discriminant analysis showed promising results in distinguishing the preliminary datasets of authentic SBHs from the adulterated ones, including discriminating SBHs adulterated with different adulterants derived from C3 and C4 plants. Hence, to assure any emerging adulterant can be detected, all 1H NMR regions should be considered.

Tracing the origin of honey products based on metagenomics and machine learning.

The adulteration of honey is common. Recently, High Throughput Sequencing (HTS)-based metabarcoding method has been applied successfully to pollen/honey identification to determine floral composition that, in turn, can be used to identify the geographical origins of honeys. However, the lack of local references materials posed a serious challenge for HTS-based pollen identification methods. Here, we sampled 28 honey samples from various geographic origins without prior knowledge of local floral information and applied a machine learning method to determine geographical origins. The machine learning method uses a resilient backpropagation algorithm to train a neural network. The results showed that biological components in honey provided characteristic traits that enabled accurate geographic tracing for nearly all honey samples, confidently discriminating honeys to their geographic origin with >99% success rates, including those separated by as little as 39 km.

Voltammetric E-Tongue for Honey Adulteration Detection.

The aim of this study is to establish the usefulness of an electronic tongue based on cyclic voltammetry e-tongue using five working electrodes (gold, silver, copper, platinum and glass) in honey adulteration detection. Authentic honey samples of different botanical origin (acacia, tilia, sunflower, polyfloral and raspberry) were adulterated with agave, maple, inverted sugar, corn and rice syrups in percentages of 5%, 10%, 20% and 50%. The silver and copper electrodes provided the clearest voltammograms, the differences between authentic and adulterated honey samples being highlighted by the maximum current intensity. The electronic tongue results have been correlated with physicochemical parameters (pH, free acidity, hydroxymethylfurfural content-5 HMF and electrical conductivity-EC). Using statistical methods such as Linear discriminant analysis (LDA) and Support vector machines (SVM), an accuracy of 94.87% and 100% respectively was obtained in the calibration step and 89.65% and 100% respectively in the validation step. The PLS-R (Partial Least Squares Regression) model (constructed from the minimum and maximum current intensity obtained for all electrodes) was used in physicochemical parameters prediction; EC reached the highest regression coefficients (0.840 in the calibration step and 0.842 in the validation step, respectively), being followed by pH (0.704 in the calibration step and 0.516 in the validation step, respectively).

Detection of acacia honey adulteration with high fructose corn syrup through determination of targeted α­Dicarbonyl compound using ion mobility-mass spectrometry coupled with UHPLC-MS/MS.

High-value acacia honey is often adulterated with inexpensive high fructose corn syrup (HFCS), due to their similar color and sugar composition. α­Dicarbonyl compounds formed by Maillard reaction or caramelization during heat treatment or storage, differ between HFCS and honey due to differences in starting materials and processing methods. In this study, we compared α-dicarbonyl compounds in acacia honey and HFCS by Ion Mobility-Mass Spectrometry and multivariate statistical analysis. Through α-dicarbonyl compound derivatization with o-phenylenediamine, we screened a marker with 189.1023 m/z and 139.3 Å2 Collision Cross-Section that can distinguish HFCS from acacia honey. Nuclear magnetic resonance spectra identified this marker compound as 3,4-dideoxypentosulose. We then used chromatography-coupled tandem mass spectrometry to quantitate 3,4-dideoxypentosulose in market samples of honey and HFCS and found that 3,4-dideoxypentosulose was negligible (<0.098 mg/kg) in honey, but prevalent in HFCS (≧1.174 mg/kg), indicating 3,4-dideoxypentosulose can serve as an alternative indicator of HFCS adulteration of acacia honey.

NMR carbohydrate profile in tracing acacia honey authenticity.

The sugar profile in honey can be used as a fingerprint to confirm the authenticity or reveal the adulteration of the product by sweetener addition. In this work, we have accurately determined the profile of 20 minor saccharides in a set of 46 European acacia honeys using a recently proposed NMR approach based on the CSSF-TOCSY experiment. Comparison of this reference profile with the sugar composition of several Chinese honey samples of the same declared botanical origin has revealed important differences. A detailed analysis of the saccharide profile of these Chinese honeys suggests product adulteration by overfeeding bee colonies with industrial sugars syrups during the main nectar flow period.

High-Throughput Monitoring of Multiclass Syrup Adulterants in Honey Based on the Oligosaccharide and Polysaccharide Profiles by MALDI Mass Spectrometry.

Honey is a natural product that could be easily adulterated with various cheaper sweeteners. In the present study, matrix-assisted laser desorption/ionization mass spectrometry (MALDI-MS) was applied for the detection of honey adulteration based on oligosaccharide and polysaccharide profiles. MS-based strategy could reveal the presence of polysaccharides with higher degree of polymerization (DP ≥ 13) and abnormal trends of saccharides in adulterated honey samples, which could be used as indicators for the identification of honey adulteration with high-fructose corn syrup and corn syrup. MS/MS-based strategy was proposed to characterize the difference in the composition of oligosaccharide isomers between honey samples and adulterated ones with corn syrup or invert syrup, in which the [M+Cl]- of disaccharides, trisaccharides, and tetrasaccharides were fragmented to give diagnostic product ion pairs. The method is effective and robust for the high-throughput monitoring of honey adulteration, and provides a new perspective for the identification of other high-carbohydrate foods.

Untargeted headspace gas chromatography - Ion mobility spectrometry analysis for detection of adulterated honey.

The recognized properties of honey together with its price have, almost inevitably, led to economically motivated adulteration. In this work, headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS) is proposed for the differentiation of honey according to its purity and the level of adulteration by sugar cane or corn syrups. An easy and rapid sample treatment, consisting of incubating 1 g of honey at 100 °C for 15 min and then injecting 750 µL of the sample headspace into the GC-IMS system, is proposed. A 3-dimensional data map is obtained in 32 min. The proposed method was used for the analysis of 198 honey samples (56 pure honeys of different botanical origins, 71 honeys adulterated with sugar cane syrup and 71 adulterated with corn syrup). The influence of the adulterant on variations in the honey sample spectrum was studied. In order to obtain chemometric models for the detection of adulterated honey samples, the data obtained by HS-GC-IMS were processed selecting the significant markers of the spectrum fingerprint. OPLS-DA models were constructed using 80% of the samples, and the remaining 20% were used for method validation. The differentiation between pure and adulterated honeys had a validation success of 97.4%, and the assessment of adulterant content was obtained with a 93.8% validation success rate for both adulterant agents assayed. Nine commercial honey samples were analysed using the proposed methodology, and seven of them were classified as adulterated.

Pesticide and trace element residues in honey and beeswax combs from Israel in association with human risk assessment and honey adulteration.

Beehive products are considered sentinels for environmental pollutants. The presence of trace elements and pesticides in honey and beeswax may pose a health hazard to consumers. The study's aim was to determine the profile of pesticides and trace elements in Israeli honey and beeswax samples in relation to human risk assessment. At least two pesticides contaminated the honey and beeswax samples simultaneously, in which, amitraz metabolites and coumaphos were frequently detected. The neonicotinoid insecticides and 2,4-dichlorophenoxyacetic acid, were found only in honey samples, whereas the more lipophilic pesticides were predominantly found in beeswax. In honey, chromium displayed the highest mean concentration, followed by zinc, whereas lead and molybdene occurred only in beeswax. Our findings indicate that the daily consumption of honey and beeswax together may compromise children's health. Sucrose-syrup fed honey could not be distinguished from floral honey based on sugar profile, rather by its trace elements levels.

Novel inspection of sugar residue and origin in honey based on the 13C/12C isotopic ratio and protein content.

Regarding the honey industry, market prices are strongly affected by the origin and composition of products. In particular, the adulteration of honey can be divided into cases of honey being mixed with artificial syrup, the different origin of the adulteration and the presence of cane sugar residue. Unfortunately, recent studies mentioned that most honey is mixed with artificial syrups. Thus, determining such unnaturally present sugar is necessary to maintain the trust of the consuming populations. To investigate the possibility of syrup augmentation, this study first clarifies two points of great importance. First, long-term feeding of cane sugar to honey bee colonies in winter and the continuous harvest of honey were investigated to evaluate the C4 sugar ratio in spring through inspection of the 13C/12C isotopic ratio. As the results indicated, C4 sugar was detected as "sugar residue" in honey samples when the honey bee colonies were fed with cane sugar in winter and when the honey was collected in the first and second harvests in March. As indicated from the samples of 89 Taiwanese longan honeys, 54 Thai longan honeys, and 20 Taiwanese non-longan honeys for analysis, such "sugar residues" were in 40% (8/20) of the Taiwanese non-longan honeys, 15% (3/20) of 2017 Taiwanese longan honeys and 20% (4/20) of 2017 Thai longan honeys; these samples were classified as adulterated honey (C4% > 7). Second, as revealed in the honeys' protein contents, statistically significant differences were found between Taiwanese (>1.00 mg/g) and Thai longan honeys (<1.00 mg/g). Apparently, this significant difference could be used to classify the difference in origins of longan honeys. This novel inspection of "sugar residue" and "origin" in honey could represent the first attempt for a protocol to guarantee both the quality and quantity assurance of honey in the marketplace.