Classical and novel approaches to the analysis of honey and detection of adulterants

One-step cascade method via glucose oxidase-copper ion complex for detecting glucose using a portable device

In this study, a one-step cascade fluorescence method was developed for the detection of glucose in honey, based on the glucose oxidase-copper ion complexes (GOx@Cu2+). These complexes exhibit dual enzymatic activities-glucose oxidase and peroxidase-like activities-which enable them to catalyze a cascade reaction. This reaction involves the oxidation of glucose and o-phenylenediamine (OPD), leading to the formation of 2,3-diaminophenazine (oxOPD), a compound with fluorescent properties. The proposed method overcomes the challenges of pH mismatch between enzymes and streamlines the testing process, eliminating the need for nanomaterial preparation and reducing the detection time to just 20 min. The feasibility of the method was validated by analyzing three honey samples, achieving recoveries between 96.4 % and 106 %, with relative standard deviations of less than 1.9 %. The selectivity and accuracy of the method were verified by capillary electrophoresis in three honey samples. Moreover, a self-designed portable device was introduced to enable on-site glucose detection.

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.

Contamination profile and hazards of neonicotinoid insecticides in honey from apiaries in Beijing, China

The residues of neonicotinoid insecticides in honey have raised global concern for their adverse effects on non-target organisms. However, information on the presence of neonicotinoids in raw honey in China is limited. Our study investigated the distribution profiles of neonicotinoids in raw honey samples collected from apiaries in plain and mountainous areas surrounding Beijing City. At least one of four neonicotinoids, imidacloprid, thiamethoxam, acetamiprid, or clothianidin, was found in 46.9% of samples. Neonicotinoids in multi-floral honey in plain areas exhibited higher concentrations and prevalence than in uni-floral honey collected from mountainous areas. These results indicated that neonicotinoid residues in honey were linked to the agricultural ecosystems influenced by geographies, particularly the intensity of agriculture and nectariferous plant types. The dietary risks to adult and children health from neonicotinoid exposure were deemed de minimis, while risks to honeybees at the maximum concentration level require much attention through refined, higher-tier assessments and possible mitigation measures for the use of these products.

Examining the Use of Polyphenols and Sugars for Authenticating Honey on the U.S. Market: A Comprehensive Review

The rise in honey production and imports into the United States necessitates the need for robust methods to authenticate honey origin and ensure consumer safety. This review addresses the scope of honey authentication, with a specific focus on the exploration of polyphenols and sugar markers to evaluate honeys in the U.S. In the absence of comprehensive federal standards for honey in the United States, challenges related to authenticity and adulteration persist. Examining the global landscape of honey authentication research, we observed a significant gap in the literature pertaining to U.S. honeys. While honeys from Europe, Australia, New Zealand, and Asia have been extensively studied, the decentralized nature of the U.S. honey market and the lack of comprehensive standards have limited the number of investigations conducted. This review consolidates the findings of global honey studies and emphasizes the need for further research studies on honey authenticity markers within the United States. We also explore previous studies on the U.S. that focused on identifying potential markers for honey authenticity. However, the inherent variability in polyphenol profiles and the lack of extensive studies of the sugar contents of honey on a global scale pose challenges to establishing universal markers. We conclude that by addressing these challenges, the field of research on polyphenols and sugars in honey can move toward more reliable and standardized methods. This advancement will enhance the use of polyphenols and other constituents like sugars as authenticity markers, ultimately benefiting both researchers and the honey industry in ensuring honey quality.

Sugar detection in adulterated honey using hyper-spectral imaging with stacking generalization method

This paper develops a new hybrid, automated, and non-invasive approach by combining hyper-spectral imaging, Savitzky-Golay (SG) Filter, Principal Components Analysis (PCA), Machine Learning (ML) classifiers/regressors, and stacking generalization methods to detect sugar in honey. First, the 32 different sugar concentration levels in honey were predicted using various ML regressors. Second, the six ranges of sugar were classified using various classifiers. Third, the 11 types of honey and 100% sugar were classified using classifiers. The stacking model (STM) obtained R2: 0.999, RMSE: 0.493 ml (v/v), RPD: 40.2, a 10-fold average R2: 0.996 and RMSE: 1.27 ml (v/v) for predicting 32 sugar concentrations. The STM achieved a Matthews Correlation Coefficient (MCC) of 99.7% and a Kappa score of 99.7%, a 10-fold average MCC of 98.9% and a Kappa score of 98.9% for classifying the six sugar ranges and 12 categories of honey types and a sugar.

Accuracy and stability enhanced honey authenticity visual tracing method via false positive-eradicating PCR assisted nucleic acid-capturing lateral flow strip

Honey authenticity guarantee is crucial for consumer health and fair-trading commerce. New visual false-positive-free molecular lateral flow strip (LFS), termed 5'-3' exonuclease activity -directed false positive-eradicating PCR assisted lateral flow strip (FPE-PCR-LFS) was developed. This FPE-PCR-LFS explored the availability of using a signal-probe as the mediator to integrate the efficient amplification module with visual LFS module. With the genomic DNA extracted from target honey, the designed signal probe would be hydrolyzed and exhausted by the 5'-3' exonuclease activity of Taq DNA polymerase in the amplification process. The hydrolyzed signal probe would not be recognized and capture on the T line with only C line of LFS, reflecting the authenticity of the tested honey. And as low as 0.5% authenticity can be accurately identified in commercial honey samples. Significantly, the false-positive-interference was successfully eradicated for the final visual results judgement, which would greatly widen the application of molecular PCR-LFS in various fields.

Adulteration Detection of Pork in Mutton Using Smart Phone with the CBAM-Invert-ResNet and Multiple Parts Feature Fusion

To achieve accurate detection the content of multiple parts pork adulterated in mutton under the effect of mutton flavor essence and colorant by RGB images, the improved CBAM-Invert-ResNet50 network based on the attention mechanism and the inversion residual was used to detect the content of pork from the back, front leg, and hind leg in adulterated mutton. The deep features of different parts extracted by the CBAM-Invert-ResNet50 were fused by feature, stitched, and combined with transfer learning, and the content of pork from mixed parts in adulterated mutton was detected. The results showed that the R2 of the CBAM-Invert-ResNet50 for the back, front leg, and hind leg datasets were 0.9373, 0.8876, and 0.9055, respectively, and the RMSE values were 0.0268 g·g-1, 0.0378 g·g-1, and 0.0316 g·g-1, respectively. The R2 and RMSE of the mixed dataset were 0.9264 and 0.0290 g·g-1, respectively. When the features of different parts were fused, the R2 and RMSE of the CBAM-Invert-ResNet50 for the mixed dataset were 0.9589 and 0.0220 g·g-1, respectively. Compared with the model built before feature fusion, the R2 of the mixed dataset increased by 0.0325, and the RMSE decreased by 0.0070 g·g-1. The above results indicated that the CBAM-Invert-ResNet50 model could effectively detect the content of pork from different parts in adulterated mutton as additives. Feature fusion combined with transfer learning can effectively improve the detection accuracy for the content of mixed parts of pork in adulterated mutton. The results of this study can provide technical support and a basis for maintaining the mutton market order and protecting mutton food safety supervision.

Differentiation of High-Fructose Corn Syrup Adulterated Kelulut Honey Using Physicochemical, Rheological, and Antibacterial Parameters

Kelulut (stingless bee) honey (KH) possesses a wide range of benefits for human consumption and can exhibit medical effects. Due to its high value, this premium honey is often adulterated with different types of cheaper sugars, causing low nutrients and potential food safety threats in the final product. This study aims to determine the physicochemical, rheological, and antibacterial properties of sugar-based adulterated KH from the stingless bee species Heterotrigona itama. Adulterated samples were prepared using pure honey mixed with different concentrations of high fructose corn syrup (HFCS), i.e., 10%, 20%, 30%, 40%, and 50%. Water activity, colour, total soluble solids, pH, turbidity, viscosity, and antimicrobial activity of KH were determined. In addition, the primary sugar composition (fructose, glucose, and trehalulose) was determined by high-performance liquid chromatography with evaporative light-scattering detection (HPLC-ELSD). This study shows that the increasing percentage of HFCS addition in the KH samples significantly increases (p < 0.05) the total soluble solids, colour, pH, turbidity, viscosity, glucose, and fructose content; meanwhile, the water activity and trehalulose were reduced significantly (p < 0.05). Antimicrobial activity against S. aureus was reduced significantly (p = 0.006) by an increased percentage of HFCS compared to Control. Antimicrobial activity against P. aeruginosa was also found to be reduced significantly but showed non-significant effect from an increased percentage of HFCS in honey (p = 0.413). The bacterium S. aureus was more vulnerable to treatment with honey from both Control and adulterated groups compared to P. aeruginosa. In conclusion, HFCS-adulterated KH and authentic KH can be differentiated using all the parameters investigated. These data are vital for the governing bodies to ensure that KH sold in the markets is free from HFCS adulteration.

Mechanisms and Health Aspects of Food Adulteration: A Comprehensive Review

Food adulteration refers to the alteration of food quality that takes place deliberately. It includes the addition of ingredients to modify different properties of food products for economic advantage. Color, appearance, taste, weight, volume, and shelf life are such food properties. Substitution of food or its nutritional content is also accomplished to spark the apparent quality. Substitution with species, protein content, fat content, or plant ingredients are major forms of food substitution. Origin misrepresentation of food is often practiced to increase the market demand of food. Organic and synthetic compounds are added to ensure a rapid effect on the human body. Adulterated food products are responsible for mild to severe health impacts as well as financial damage. Diarrhea, nausea, allergic reaction, diabetes, cardiovascular disease, etc., are frequently observed illnesses upon consumption of adulterated food. Some adulterants have shown carcinogenic, clastogenic, and genotoxic properties. This review article discusses different forms of food adulteration. The health impacts also have been documented in brief.

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.

Comparison of Total Soluble Protein Content and SDS-PAGE Pattern Between Four Different Types of Honey

Generally, there are two types of beekeeping: the Apini tribe and the Meliponini tribe. Both tribes produce honey and have a good demand due to their health benefit properties. Considering the influence of diverse factors on honey composition and the lack of studies, establishing quality standards for stingless bee honey (Meliponini tribe) is still challenging and need to do to protect the consumer. In this sense, this study aimed to determine the total soluble protein content and compare the SDS-PAGE profile between two species of Apini tribe and two species of Meliponini tribe. Protein concentrations in honey samples were varied and resulted in a micro component in honey. SDS-PAGE profile for Meliponini tribe showed more number of protein bands compared to protein from Apini tribe. The unique protein bands that appeared in the Meliponini tribe may have potential as a biomarker to justify the authenticity and quality of that honey, which is known as Unique Kelulut Factor (UKF).

Accurate and Rapid Genetic Tracing the Authenticity of Floral Originated Honey with the Molecular Lateral Flow Strip

Honey is a natural product and is heavily consumed for its well-known nutritional functions. Honeys with different floral origins possess distinctive flavors, tastes, functions and economic values. It is vital to establish an effective strategy for identifying the authenticity of honey. The intrinsic genetic materials of pollen were adopted as target analytes for the effective identification of honey with floral origins. With an optimized protocol for the rapid gene extraction from honey, target genetic templates were amplified on-site with a portable device. Conveniently, all on-site amplified functional products were easily judged by the designed lateral flow strip (LFS), which was defined as the molecular LFS in this research. Additionally, the entire on-site genetic authentication of honey was completed in less than 2 h by visual observation. Commercial honey products have been successfully identified with excellent accuracy. This low-cost, high-efficiency and easy-operational strategy will greatly benefit the quality guarantee of foods with specific functions and geographical markers.

Qualitative and Quantitative Detection of Acacia Honey Adulteration with Glucose Syrup Using Near-Infrared Spectroscopy

Honey adulteration with cheap sweeteners such as corn syrup or invert syrup results in honey of lesser quality that can harm the objectives of both manufacturers and consumers. Therefore, there is a growing interest for the development of a fast and simple method for adulteration detection. In this work, near-infrared spectroscopy (NIR) was used for the detection of honey adulteration and changes in the physical and chemical properties of the prepared adulterations. Fifteen (15) acacia honey samples were adulterated with glucose syrup in a range from 10% to 90%. Raw and pre-processed NIR spectra of pure honey samples and prepared adulterations were subjected to Principal Component Analysis (PCA), Partial Least Squares (PLS) regression, and Artificial Neural Network (ANN) modeling. The results showed that PCA ensures distinct grouping of samples in pure honey samples, honey adulterations, and pure adulteration using NIR spectra after the Multiplicative Scatter Correction (MSC) method. Furthermore, PLS models developed for the prediction of the added adulterant amount, moisture content, and conductivity can be considered sufficient for screening based on RPD and RER values (1.7401 < RPD < 2.7601; 7.7128 < RER < 8.7157) (RPD of 2.7601; RER of 8.7157) and can be moderately used in practice. The R2validation of the developed ANN models was greater than 0.86 for all outputs examined. Based on the obtained results, it can be concluded that NIR coupled with ANN modeling can be considered an efficient tool for honey adulteration quantification.

A Novel, Rapid Screening Technique for Sugar Syrup Adulteration in Honey Using Fluorescence Spectroscopy

The adulteration of honey with different sugar syrups is common and difficult to detect. To ensure fair trade and protect the interests of apiarists, a rapid, simple and cost-effective detection method for adulterants in honey is needed. In this work, fluorescence emission spectra were obtained for honey and sugar syrups between 385 and 800 nm with excitation at 370 nm. We found substantial differences in the emission spectra between five types of honey and five sugar syrups and also found differences in their frequency doubled peak (FDP) intensity at 740 nm. The intensity of the FDP significantly declined (p < 0.01) when spiking honey with ≥10% sugar syrup. To validate this method, we tested 20 adulterant-positive honey samples and successfully identified 15 that were above the limit of detection. We propose that fluorescence spectroscopy could be broadly adopted as a cost-effective, rapid screening tool for sugar syrup adulteration of honey through characterization of emission spectra and the intensity of the FDP.

Latex-Based Paper Devices with Super Solvent Resistance for On-the-Spot Detection of Metanil Yellow in Food Samples

The following paper presents a construct for a paper-based device which utilizes latex as the hydrophobic material for the fabrication of its hydrophobic barrier, which was deposited onto the cellulose surface either by free-hand or stenciled drawing. This method demands the least amount of expertise and time from its use, enabling a simple and rapid fabrication experience. Several properties of the hydrophobic material were characterized, such as the hydro head and penetration rate, with the aim of assessing its robustness and stability. The presented hydrophobic barriers fabricated using this approach have a barrier width of 4 mm, a coating thickness of 208 µm, and a hydrophilic resolution of 446.5 µm. This fabrication modality boasts an excellent solvent resistance with regard to the hydrophobic barrier. These devices were employed for on-the-spot detection of Metanil Yellow, a banned food adulterant often used in curcumin and pigeon peas, within successful limits of detection (LOD) of 0.5% (w/w) and 0.25% (w/w), respectively. These results indicate the great potential this fabricated hydrophobic device has in numerous paper-based applications and other closely related domains, such as diagnostics and sensing, signalling its capacity to become commonplace in both industrial and domestic settings.

Deep learning assisted distinguishing of honey seasonal changes using quadruple voltammetric electrodes

The work presents innovative quadruple disk iridium, platinum, and iridium-platinum voltammetric electrodes with a special design, dedicated to the testing of samples with a complex organic composition. Noble metal wires are tightened in one silver rod, and therefore each of them acts as a single sensor. It was demonstrated that the signals of the iridium-platinum sensor combine the electrode responses constructed from one metal, which increases the possibilities and range of applications of this sensor, and it can be used as an electronic tongue. These single and combined noble metal electrodes were successfully verified to profile the seasonal variability of honey collected from an apiary in Małopolska (voivodeship in Poland). Data obtained by the differential pulse voltammetry, according to the principles of green chemistry, without using any reagents, were interpreted by principal component analysis, preceded by the optimized variable selection procedure. The best results in distinguishing 12 honeys were obtained using a multimetallic electrode. The classification model calculated using deep convolutional neural networks indicated the proper belonging of honeys to the groups with 100% accuracy for the training and validation set. The proposed solution proved that noble metals quadruple disk electrodes are a promising tool supporting voltammetric profiling of samples and this strategy, considering deep learning, can be developed to a large extent.

Rheological behavior of honey adulterated with agave, maple, corn, rice and inverted sugar syrups

The aim of this study was to assess the influence of different adulteration agents (agave, maple, corn, rice and inverted sugar) on honey rheology. There was studied the influence of different percentages of adulteration agent on steady state and dynamic state rheology but also on rheology in the negative temperature domain. The authentic honey and adulterated ones behaved as a Newtonian fluid with a liquid-like behavior (G">>G'). Regarding the physicochemical parameters analyzed (moisture and sugar content), significant changes depending on the adulteration agent/degree used were observed. The viscoelastical parameters (η*-complex viscosity, G' -elastic modulus and G"-viscous modulus) and glass transition temperature (Tg) were predicted in function of the chemical composition (moisture content, glucose, fructose, sucrose, maltose, raffinose, trehalose, turanose, melesitose, and F/G ratio) using the PLS-R (partial least square regression). All parameters analyzed had a high regression coefficient for calibration (> 0.810) and validation (> 0.790), except for the elastic modulus.

Handheld Devices for Food Authentication and Their Applications: A Review

This review summarises miniaturised technologies, commercially available devices, and device applications for food authentication or measurement of features that could potentially be used for authentication. We first focus on the handheld technologies and their generic characteristics: (1) technology types available, (2) their design and mode of operation, and (3) data handling and output systems. Subsequently, applications are reviewed according to commodity type for products of animal and plant origin. The 150 applications of commercial, handheld devices involve a large variety of technologies, such as various types of spectroscopy, imaging, and sensor arrays. The majority of applications, ~60%, aim at food products of plant origin. The technologies are not specifically aimed at certain commodities or product features, and no single technology can be applied for authentication of all commodities. Nevertheless, many useful applications have been developed for many food commodities. However, the use of these applications in practice is still in its infancy. This is largely because for each single application, new spectral databases need to be built and maintained. Therefore, apart from developing applications, a focus on sharing and re-use of data and calibration transfers is pivotal to remove this bottleneck and to increase the implementation of these technologies in practice.

Identification of pollen taxa by different microscopy techniques

Melissopalynology is an important analytical method to identify botanical origin of honey. Pollen grain recognition is commonly performed by visual inspection by a trained person. An alternative method for visual inspection is automated pollen analysis based on the image analysis technique. Image analysis transfers visual information to mathematical descriptions. In this work, the suitability of three microscopic techniques for automatic analysis of pollen grains was studied. 2D and 3D morphological characteristics, textural and colour features, and extended depth of focus characteristics were used for the pollen discrimination. In this study, 7 botanical taxa and a total of 2482 pollen grains were evaluated. The highest correct classification rate of 93.05% was achieved using the phase contrast microscopy, followed by the dark field microscopy reaching 91.02%, and finally by the light field microscopy reaching 88.88%. The most significant discriminant characteristics were morphological (2D and 3D) and colour characteristics. Our results confirm the potential of using automatic pollen analysis to discriminate pollen taxa in honey. This work provides the basis for further research where the taxa dataset will be increased, and new descriptors will be studied.

Imaging of bee honey sugar crystals by second-harmonic generation microscopy

Bee honey is an exceptionally nutritious food with unique chemical and mineral contents. This report introduces the use of the second-harmonic generation (SHG) microscopy for imaging honey sugar crystals' morphology as an alternative for its authentication process. The crystals and their boundaries are clearly observed with SHG compared with bright-field microscopy, where the liquid honey avoids the visualization of a sharp image. Four different honey samples of Mexico's various floral origins and geographical regions are analyzed in our study. These samples are representative of the diversity and valuable quality of bee honey production. The SHG image information is complemented with Raman spectroscopy (RS) analysis, since this optical technique is widely used to validate the bee's honey composition stated by its floral origin. We relate the SHG imaging of honey crystals with the well-defined fructose and glucose peaks measured by RS. Size measurement is introduced using the crystal´s length ratio to differentiate its floral origin. From our observations, we can state that SHG is a promising and suitable technique to provide a sort of optical fingerprint based on the floral origin of bee honey.

Volatile Profile of Portuguese Monofloral Honeys: Significance in Botanical Origin Determination

The volatile profiles of 51 samples from 12 monofloral-labelled Portuguese honey types were assessed. Honeys of bell heather, carob tree, chestnut, eucalyptus, incense, lavender, orange, rape, raspberry, rosemary, sunflower and strawberry tree were collected from several regions from mainland Portugal and from the Azores Islands. When available, the corresponding flower volatiles were comparatively evaluated. Honey volatiles were isolated using two different extraction methods, solid-phase microextraction (SPME) and hydrodistillation (HD), with HD proving to be more effective in the number of volatiles extracted. Agglomerative cluster analysis of honey HD volatiles evidenced two main clusters, one of which had nine sub-clusters. Components grouped by biosynthetic pathway defined alkanes and fatty acids as dominant, namely n-nonadecane, n-heneicosane, n-tricosane and n-pentacosane and palmitic, linoleic and oleic acids. Oxygen-containing monoterpenes, such as cis- and trans-linalool oxide (furanoid), hotrienol and the apocarotenoid α-isophorone, were also present in lower amounts. Aromatic amino acid derivatives were also identified, namely benzene acetaldehyde and 3,4,5-trimethylphenol. Fully grown classification tree analysis allowed the identification of the most relevant volatiles for discriminating the different honey types. Twelve volatile compounds were enough to fully discriminate eleven honey types (92%) according to the botanical origin.

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).

Analytical Rheology of Honey: A State-of-the-Art Review

Honey has been used as a nutraceutical product since ancient times due to its nutritional and medicinal properties. Honey rheology influences its organoleptic properties and is relevant for processing and quality control. This review summarizes the rheological behaviour of honeys of different botanical source(s) and geographical locations that has been described in the literature, focusing on the relation between rheological parameters, honey composition (moisture, water activity, sugar content, presence of colloidal matter) and experimental conditions (temperature, time, stress, shear rate). Both liquid and crystallized honeys have been addressed. Firstly, the main mathematical models used to describe honey rheological behaviour are presented highlighting moisture and temperature effects. Then, rheological data from the literature regarding distinct honey types from different countries is analysed and results are compared. Although most honeys are Newtonian fluids, interesting shear-thinning and thixotropic as well as anti-thixotropic behaviour have been described for some types of honey. Rheological parameters have also been successfully applied to identify honey adulteration and to discriminate between different honey types. Several chemometric techniques have also been employed to obtain the complex relationships between honey physicochemical and rheological properties, including partial least squares (PLS), principal component analysis (PCA) and artificial neural networks (ANN).

A single honey proteome dataset for identifying adulteration by foreign amylases and mining various protein markers natural to honey

Honey adulteration is a common practice that deceives consumers and devalues the unique curative and food properties of honey. For marketing, each honey must satisfy an internationally valid Codex standard. One of the quality parameters is diastase/amylase activity, which, if lowered, may be compensated for by the addition of foreign amylases. However, the estimation of enzyme activity does not enable identification of artificially added amylases. 45 honey samples were analyzed using label-free nanoLC-MS/MS proteomics. Four honeys were found to contain the foreign amylases from Aspergillus niger, Bacillus amyloliquefaciens and/or Bacillus licheniformis. This result was confirmed via proof of specificity at multiple levels. Furthermore, we identified a series of plant-related protein groups. Despite plant-related proteins constituting a significant portion of honey proteins, they were minor components compared to the major honey bee-derived proteins. Bioinformatic analysis also provided evidence for aphid and catalase proteins in honey, but the limited specificity of the MS/MS identified peptides must be considered. Overall, we demonstrate a proteomics approach employing LC-MS/MS that is useful for proving adulteration and assessing honey quality. As an resource useful for reference, we provide curated sequence databases. In addition, we provide many markers that are naturally found in honey for future studies. SIGNIFICANCE: Honey is unique natural product used since ancient times as a food and natural medicine. Humans strive to understand honey components because they can characterize different types of honey and be used for authentication and origin assessment. One of the important honey components are proteins. The proteins present in honey can naturally occur in honey, but some of them can be used to mask deficiencies in some honey quality properties. Diastases/amylases are such proteins, and their activity, a measure of honey freshness, can decrease in time or due to processing. To our knowledge, we for the first time specifically identify foreign amylases in honey. However, this study provided new information on other non-honey bee proteins in honey. Thus, this study is also of importance due to its identification of plant and aphid proteins and catalase-related proteins. This study provides a clue explaining the controversial presence of catalase in honey, since catalases can be identified and their origin determined via proteomics.

DNA barcoding: a modern age tool for detection of adulteration in food

Globalization of the food trade requires precise and exact information about the origin, methods of production, transformation technologies, authentication, and the traceability of foodstuffs. New challenges in food supply chains such as deliberate fraudulent substitution, tampering or mislabeling of food and its ingredients or food packaging incapacitates the market and eventually the national economy. Currently, no proper standards have been established for the authentication of most of the food materials. However, in order to control food fraud, various robust and cost-effective technologies have been employed, like a spectrophotometer, GC-MS, HPLC, and DNA barcoding. Among these techniques, DNA barcoding is a biotechnology advantage with the principle of using 400-800 bp long standardized unique DNA sequences of mitochondrial (e.g. COI) or plastidial (e.g. rbcL) of nuclear origin (e.g. ITS) to analyze and classify the food commodities. This review covers several traded food commodities like legumes, seafood, oils, herbal products, spices, fruits, cereals, meat, and their unique barcodes which are critically analyzed to detect adulteration or fraud. DNA barcoding is a global initiative and it is being accepted as a global standard/marker for species identification or authentication. The research laboratories and industries should collaborate to realize its potential in setting standards for quality assurance, quality control, and food safety for different food products.

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.