Honey Adulteration Research Articles

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

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

Development of FTIR-ATR spectra and PLS regression combination model for discrimination of pure and adulterated acacia honey

A Fourier Transform Infrared Spectroscopy and attenuated total reflectance (FTIR-ATR)-based chemometric model was evaluated for the rapid identification and estimation of adulterants in honey. Two types of honey, bee honey and stingless bee honey, were adulterated with acid adulterants (acetic acid, citric acid, and tamarind extract) at different concentrations of 1%, 3%, 5%, and 7% and sugar adulterants (liquid corn syrup, cane sugar, palm sugar, and inverted sugar) at different concentrations of 1%, 2%, and 3%. The physicochemical properties (Brix, moisture content, pH, and free acidity) and sugar contents (glucose, fructose, and sucrose) of each type of honey and their adulterated samples were determined before FTIR analysis. For all the samples, FTIR spectra were acquired in the mid-infrared range (400-3600 cm−1) and the spectra obtained were subjected to partial least squares (PLS) analysis to develop the model for the determination of adulterants in honey samples. The PLS model produced high coefficient of determination (R2) for bee honey adulterated with acetic acid (0.95), citric acid (0.98), tamarind extract (0.97), and stingless bee honey adulterated with liquid corn syrup (0.99), inverted sugar (0.91), cane sugar (0.86), and palm sugar (0.77). A recognition model was developed for mixture detection and verified through the physicochemical analysis. The combination of FTIR-ATR spectra and the PLS regression model can be a fast, reliable, nondestructive method to detect honey adulteration.

Comprehensive honey authentication in Bangladesh: Profiling physicochemical and bioactive compounds to distinguish floral sources and detect adulteration

This study aims to advance honey authentication in Bangladesh by developing a reliable, cost-effective, and user-friendly system capable of distinguishing floral sources and purity. We analyzed various physicochemical parameters and bioactive compounds in honey samples from diverse floral sources across Bangladesh during different floral seasons, including deliberately adulterated samples. Results showed clear distinctions among the tested parameters. Notably, moisture content exhibited considerable variability, with the highest levels found in kholisha (24.07%–24.99 %) and the lowest in black cumin honey (17.80%–27.46 %). Jujube honey exhibited the highest protein content at 10 % adulteration, whereas mustard honey showed the least at 20 % adulteration. Generally, adulterated honey samples showed a considerable deviation in protein content compared to known standards for pure honey, suggesting adulteration significantly impacts protein levels. Black cumin honey showed the highest electrical conductivity, followed by jujube. Antioxidant levels were highest in litchi honey, followed by ayurvedic and kholisha. Multivariate analysis revealed that two principal components explained 65 % of total variances and could separate the various clusters of honey samples. The pH values, total phenolic, total flavonoid, protein content, electrical conductivity, color intensity at 360 nm and 635 nm, and antioxidant properties of all tested honey samples showed a strong positive correlation. Overall, the manuscript strategically combines experimental findings with detailed analyses to explore the complex nature of honey composition, adulteration issues, and the significance of authenticating honey to maintain quality and ensure consumer trust in the market. This study is the first to employ a comprehensive set of physicochemical and bioactive indicators with multivariate analysis for honey authentication in Bangladesh, offering a novel and systematic approach to identifying purity and floral sources.

Fluorescence spectroscopy combined with multilayer perceptron deep learning to identify the authenticity of monofloral honey—Rape honey

Honey authenticity is critical to honey quality. The development of a quick, easy, and non-destructive technique for determining the authenticity of honey encourages an improvement in honey quality. Here, the authenticity of monofloral honey—rape honey was determined using fluorescence spectroscopy combined with multilayer perceptron (MLP) deep learning, without the need for any prior feature extraction or pre-processing. A total of 91 raw fluorescence intensity data of the real and adulterated honey samples at a fixed excitation wavelength of 280 nm were first matrixed, and all data were then categorized into a training set, a validation set, and a test set with numbers of 64, 16, and 11, respectively. The connection with dropout was selected to build and link the MLP internal network. The activation function, learning rate, optimizer, and number of epochs were among the hyperparameters of the MLP neural network that were tuned. A good MLP deep learning network model for determining the authenticity of monofloral honey, rape honey, was developed after constant validation and debugging. According to the accuracy curve of the MLP model, the accuracy of the training set increased with the number of epochs and eventually converged to 100 %, while the accuracy of the validation set could be well stabilized at about 100 % after 5000 epochs. Finally, the accuracy of the MLP model on the test set was close to 100 %. According to our findings, the MLP neural network and fluorescence intensity have great potential applications in identifying the authenticity of honey.

Evaluating the effectiveness of DNA-based methods to detect mislabelling and adulteration of chestnut honey

Honey is a naturally sweet and viscous substance that is consumed worldwide for its specific flavour, taste and biological properties, which in turn depend on the type of plant from which bees collect nectar. The quality of honey and, in particular, the information on the product label determines consumer choice and market price. In this study, the true botanical composition of chestnut monofloral honeys produced in Calabria (South Italy) was determined by DNA barcoding, PCR-RFLP and LAMP approaches, all based on the chloroplast trnL-trnF intergenic spacer region. All three DNA-based techniques were reliable in unambiguously identifying the floral composition of the different honeys tested at species level. However, the LAMP assay has the advantage over DNA barcoding and PCR-RFLP of greatly simplifying and speeding up the identification of Castanea sativa as the dominant plant species of the Calabrian chestnut honeys. This may encourage the adoption of this method by the food industry for the rapid detection of intentionally or accidentally mislabelled ingredients in this valuable natural product, thus ensuring consumer safety and protection.

Detection of sugar syrup adulteration in UK honey using DNA barcoding

Honey is a valuable and nutritious food product, but it is at risk to fraudulent practices such as the addition of cheaper syrups including corn, rice, and sugar beet syrup. Honey authentication is of the utmost importance, but current methods are faced with challenges due to the large variations in natural honey composition (influenced by climate, seasons and bee foraging), or the incapability to detect certain types of plant syrups to confirm the adulterant used. Molecular methods such as DNA barcoding have shown great promise in identifying plant DNA sources in honey and could be applied to detect plant-based sugars used as adulterants. In this work DNA barcoding was successfully used to detect corn and rice syrup adulteration in spiked UK honey with novel DNA markers. Different levels of adulteration were simulated (1 – 30%) with a range of different syrup and honey types, where adulterated honey was clearly separated from natural honey even at 1% adulteration level. Moreover, the test was successful for multiple syrup types and effective on honeys with different compositions. These results demonstrated that DNA barcoding could be used as a sensitive and robust method to detect common sugar adulterants and confirm syrup species origin in honey, which can be applied alongside current screening methods to improve existing honey authentication tests.

Application of Spatial Offset Raman Spectroscopy (SORS) and Machine Learning for Sugar Syrup Adulteration Detection in UK Honey.

Honey authentication is a complex process which traditionally requires costly and time-consuming analytical techniques not readily available to the producers. This study aimed to develop non-invasive sensor methods coupled with a multivariate data analysis to detect the type and percentage of exogenous sugar adulteration in UK honeys. Through-container spatial offset Raman spectroscopy (SORS) was employed on 17 different types of natural honeys produced in the UK over a season. These samples were then spiked with rice and sugar beet syrups at the levels of 10%, 20%, 30%, and 50% w/w. The data acquired were used to construct prediction models for 14 types of honey with similar Raman fingerprints using different algorithms, namely PLS-DA, XGBoost, and Random Forest, with the aim to detect the level of adulteration per type of sugar syrup. The best-performing algorithm for classification was Random Forest, with only 1% of the pure honeys misclassified as adulterated and <3.5% of adulterated honey samples misclassified as pure. Random Forest was further employed to create a classification model which successfully classified samples according to the type of adulterant (rice or sugar beet) and the adulteration level. In addition, SORS spectra were collected from 27 samples of heather honey (24 Calluna vulgaris and 3 Erica cinerea) produced in the UK and corresponding subsamples spiked with high fructose sugar cane syrup, and an exploratory data analysis with PCA and a classification with Random Forest were performed, both showing clear separation between the pure and adulterated samples at medium (40%) and high (60%) adulteration levels and a 90% success at low adulteration levels (20%). The results of this study demonstrate the potential of SORS in combination with machine learning to be applied for the authentication of honey samples and the detection of exogenous sugars in the form of sugar syrups. A major advantage of the SORS technique is that it is a rapid, non-invasive method deployable in the field with potential application at all stages of the supply chain.

Rapid in situ identification of honey authenticity based on RP-Nano-ESI-MS using online desalting

High-content sugar in honey frequently results in severe matrix effects and requires complex pretreatment prior to analysis, posing significant challenges for the rapid analysis of honey. In this study, the reversal polarity nano-electrospray ionization mass spectrometry (RP-Nano-ESI-MS) analysis was developed for the direct evaluation of honey samples. The results indicated that RP-Nano-ESI-MS significantly mitigated the matrix effects induced by high-content sugar through the implementation of online desalting. Furthermore, RP-Nano-ESI-MS has been proven capable of not only differentiating acacia honey adulterated with 10% rape honey, but also effectively distinguishing six types of honey and exhibiting remarkable proficiency in detecting honey adulteration and botanical traceability. Additionally, RP-Nano-ESI-MS exhibited strong quantitative abilities, effectively characterizing variations in amino acid composition among six types of honey with high stability and reproducibility. Our studies underscore the significant potential of RP-Nano-ESI-MS for its rapid in situ analysis of sugar-rich foods like honey, especially in their authenticity verification.

Highly sensitive adulteration detection in kelulut honey utilizing fiber Bragg grating technology with Q-switched pulse erbium-doped fiber laser and innovative spider silk substrate

The growing field of photonics has seen significant developments in optical pulse fiber-based sensing technologies. In this research, a novel fiber Bragg grating using a Q-switched erbium-doped fiber laser source and innovative spider silk as a base material is presented, specifically designed for the detection of adulteration in pure kelulut honey. Kelulut honey has attracted much attention from Malaysian researchers as it is a highly prized, versatile local health remedy. During the experimental setup, concentrations of the adulterant isoglucose ranging from 0 % to 8 % and 10–50 % were intentionally added to the pure kelulut honey. The sensor showed remarkable sensitivity and detected subtle shifts in both the wavelength and radio frequency spectrum. The experimental results obtained emphasize the effectiveness of the proposed sensor and demonstrate its exceptional sensitivity in detecting adulteration in pure kelulut honey. This inherent property makes the derived pulse fiber laser sensor a promising tool for various applications in clinical detection and diagnosis as well as in the food industry. The results presented here not only contribute to the field of photonics but also have the potential to advance the quality control and authenticity assessment of kelulut honey in various fields.

The development of honey recognition models with broad applicability based on the association of isotope and elemental content with ANNs

Honey adulteration represents a worldwide problem, driven by the illicit economic gain that producers, traders, or merchants pursue. Among the falsification methods that can unfairly influence the price is the incorrect declaration of the botanical origin and harvesting year. Therefore, the present study aimed to test the potential given by the application of Artificial Neural Networks (ANNs) for developing prediction models able to assess honey botanical origin and harvesting year based on isotope and elemental fingerprints. For each classification criterion, significant focus was dedicated to the data preprocessing phase to enhance the models' prediction capability. The obtained classification performances (accuracy scores >86% during the test phase) have highlighted the efficiency of ANNs for honey authentication as well as the feasibility of applying the developed classifiers for a large-scale application, supported by their ability to recognize the correct origin despite considerable variability in botanical source, geographical origin, and harvesting period.

Occurrence of 3-methoxytyramine in Cuban honeys

Honey adulteration is a subject of great concern worldwide. The compound 3-methoxytyramine (3-MT) is one of the biomarkers used to determine adulteration or contamination with sugar, syrups, and other products of beetroot (Beta vulgaris) from Amaranthaceae family. However, there is scientific evidence of the presence of 3-methoxytyramine in other species of this family, some of which, reported in Cuba. As honey plants, bees use them to carry nectar and pollen to hives to make honey, therefore, there is a possibility to detect 3-methoxytyramine in these samples, not only in Cuban's honeys samples, but also worldwide.

Using pollen DNA metabarcoding to trace the geographical and botanical origin of honey from Karangasem, Indonesia

The unique floral fingerprint embedded within honey holds valuable clues to its geographical and botanical origin, playing a crucial role in ensuring authenticity and detecting adulteration. Honey from native Apis cerana and Heterotrigona itama bees in Karangasem, Indonesia, was examined utilizing pollen DNA metabarcoding for honey source identification. In this study, we used ITS2 amplicon sequencing to identify floral DNA in honey samples. The finding reveals distinct pollen signatures for each bee species. Results analysis showed A. cerana honey generated 179,267 sequence reads, assembled into Amplicon Sequence Variants (ASVs) with a total size of 485,932 bp and an average GC content of 59 %. H. itama honey generated 177,864 sequence reads, assembled into ASVs with a total size of 350,604 bp and an average GC content of 57 %. A. cerana honey exhibited a rich tapestry of pollen from eleven diverse genera, with Schleichera genus dominating at an impressive relative read abundance of 72.8 %. In contrast, H. itama honey displayed a remarkable mono-dominance of the Syzygium genus, accounting for a staggering 99.95 % of its pollen composition or relative read abundance, highlighting their distinct foraging preferences and floral resource utilization. Notably, all identified pollen taxa were indigenous to Karangasem, solidifying the geographical link between honey and its origin. This study demonstrates pollen DNA metabarcoding may identify honey floral sources. By using pollen profiles from different bee species and their foraging patterns, we may protect consumers against honey adulteration and promote sustainable beekeeping in Karangasem district. Future research could explore expanding the database of reference pollen sequences and investigating the influence of environmental factors on pollen composition in honey. Investigating this technology's economic and social effects on beekeepers and consumers may help promote fair trade and sustainable beekeeping worldwide.

Fast and Efficient Prediction of Honey Adulteration using Hyperspectral Imaging and Machine Learning Models

Fast and Efficient Prediction of Honey Adulteration using Hyperspectral Imaging and Machine Learning Models

Detection of the thaumatin-like protein gene from Brassica rapa var. oleifera in honey with the Proofman-LMTIA method

ABSTRACT Honey adulteration has been on the rise with the increasing demand for high-quality honey. The ladders-shape melting temperature isothermal amplification (LMTIA) was a newly developed nucleic acid amplification technique, and the aim of this study was to establish a method combining the LMTIA with the proofreading enzyme-mediated probe cleavage (Proof-man probe) for detecting the thaumatin-like protein (TLP) genes of Brassica rapa var. oleifera in honey. The LMTIA primers was selected as a target to be designed, the temperature of the Proof-man LMTIA reaction was optimized, and the specificity, sensitivity, and applicability to the honey samples were determined. The results showed that the Proof-man LMTIA method was highly specific with a detection sensitivity of 30-40 copies genomic DNA/reaction at the optimal temperature of 60°C, and the amplification could be completed within 20 min. This method can detect the adulteration of low-price B. rapa honey into high-price honey.

Authentication of the Apis cerana honey with the proofreading enzyme-mediated probe cleavage combined with ladder-shape melting temperature isothermal amplification (Proofman-LMTIA) method

Honey adulteration has been on the rise with the increasing demand for high-quality honey, and the authentication of A. cerana honey is a major problem that the current bee industry needs to solve. The aim of this research was to establish the proofreading enzyme-mediated probe cleavage combined with ladder-shape melting temperature isothermal amplification (Proofman-LMTIA) method for the authentication of Apis cerana honey. The LMTIA primers were designed to target the specific 18S rRNA sequence of A. cerana, and the conditions for Proofman-LMTIA amplification were optimized. The specificity and sensitivity of the method were determined and compared with that of the droplet digital polymerase chain reaction (PCR). The optimal temperature of the established Proofman-LMTIA method was 62 °C. The method can specially to detect the A. cerana gene without cross-reactivity with the genomes from other animal and plant species, and the amplification reaction can be finished within 20 min with the sensitivity of 10 pg A. cerana genomic DNA, which was more sensitive than the droplet digital PCR method. The obtained results demonstrate that the established Proofman-LMTIA procedure was specific, sensitive, and rapid, and can be used for authentication of A. cerana honey.

Differences in the adulteration degree and antimicrobial activity of chilean ulmo honey versus multifloral honey revealed by stable isotope analysis

Honey, valued for its nutritional and antimicrobial benefits, has experienced an increased production in recent decades. However, this rise has been accompanied by concerns of adulteration, often involving the fraudulent addition of sugars. Our study sought to compare the physicochemical and isotopic properties of various honeys available to Chilean consumers, assessing the extent of adulteration. Samples included honey produced from bees that fed on multiple flowers and those fed by ulmo flowers – an endemic species of South America that produces a high-quality, high-cost honey – and analyzed for antimicrobial activity against Escherichia coli, Staphylococcus aureus and Pseudomonas aeruginosa. The analysis of ash content (%), moisture (%), pH and total phenolic content (mg GAE/100 g honey) found little obvious differences among honeys regardless of the feeding flowers (multifloral vs ulmo), type of purchase market (formal vs informal) or origin of the honeys (Central vs Southern Chile). However, the use of stable isotope analysis (δ13C and δ15N) of honey provided a powerful means to identify the degree of adulteration prior to the point of sale. Multifloral honeys purchased at informal markets were all adulterated, and ulmo honeys included both the least and most adulterated honeys. Regarding their antimicrobial activity, most multifloral honeys were less effective than ulmo honeys. Notably, while multifloral honey activity was independent of adulteration, the antimicrobial activity of ulmo honey was negatively affected by adulteration.

Establishment of rapid extraction and sensitive detection system of trace corn syrup DNA in honey

Honey adulteration with exogenous syrup has become a common phenomenon, and current detection techniques that require large instruments are cumbersome and time-consuming. In this study, a simple and efficient method was developed by integrating the rapid extraction of nucleic acids (REMD) and recombinase polymerase amplification (RPA), known as REMD-RPA, for the rapid screening of syrup adulteration in honey. First, a rapid extraction method was developed to rapidly extract corn syrup DNA in five minutes to meet the requirements of PCR and RPA assays. Then, the RPA method for detecting endogenous maize genes (ZssIIb) was established, which could detect 12 copies/μL of the endogenous maize gene within 30 min without cross-reacting with other plant-derived genes. This indicated that the RPA technique exhibited high sensitivity and specificity. Finally, the REMD-RPA detection platform was used to detect different concentrations of corn syrup adulteration, and 1 % adulteration could be detected within 30 min. The 22 commercially available samples were tested to validate the efficacy of this method, and the established RPA was able to detect seven adulterated samples in less than 30 min. Overall, the developed method is rapid, sensitive, and specific, providing technical support for the rapid field detection of honey adulteration and can serve as a reference for developing other field test methods.

Detection of honey adulteration by characterization of the physico-chemical properties of honey adulterated with the addition of glucose–fructose and maltose corn syrups

This study aimed to experimentally investigate the effect of sugar syrup additions on quality measurements of honey and to detect adulteration. For that purpose, two different pure blossom honey samples were adulterated by directly mixing 0%, 5%, 10%, 20%, 30%, 40%, and 50% of commercially available glucose–fructose corn syrup and maltose corn syrup. In this regard, key physico-chemical properties like moisture, pH, free acidity, proline, diastase number, color (L, a, b and Delta-E), electrical conductivity, HMF, sugar profile (glucose, fructose, sucrose and maltose), and C4 sugar analysis were tested. The results of the individual analysis of moisture, pH, free acidity, proline, diastase number, color, electrical conductivity, and HMF failed to detect sugar syrup adulteration. However, when principal component analysis (PCA) was utilized to analyze the data gathered from these tests, adulterations at all-syrup ratios (5–50%) were successfully detected.Graphical

Simple and inexpensive microwave setup for industrial based applications: Quantification of flower honey adulteration as a case study.

A simple and inexpensive microwave measurement setup based on measurements of magnitudes of transmission properties ( ) is proposed for industrial-based microwave aquametry (moisture or water content) applications. An easy-to-apply calibration procedure based on normalization is implemented to eliminate systematic errors in the measurement system. As a case study, we applied this setup for the quantification of water-adulteration in flower honey. After validating this system by distilled water and pure flower honey measurements, measurements of the pure flower honey with various adulteration percentages ( ) up to 9% are conducted to examine the performance of the measurement setup for quantification of water adulteration. A multi-dimensional fitting procedure is implemented to predict using the proposed inexpensive microwave measurement setup. It is shown that it is possible to quantify an adulteration level with an accuracy better than % by the proposed measurement setup and the applied multi-dimensional fitting procedure.

Ultra-Sensitive Plasmonic Sensor for Detecting Honey Adulteration with III–V Nitride and Tungsten Disulfide

Ultra-Sensitive Plasmonic Sensor for Detecting Honey Adulteration with III–V Nitride and Tungsten Disulfide