Honey Adulteration Research Articles

Improving the performance of the model developed from the classification of adulterated honey with different botanical origins based on near-Infrared hyperspectral imaging system and supervised classification algorithms

Honey is a high-value product that proscribes adulteration. Authentication of pure honey is an imperative act that can disregard the adverse impacts brought by fraudsters on honey producers and consumers. A reliable method that can detect food adulteration that is nondestructive, rapid, and efficient can help maintain product authenticity. This study investigated the potential of a near-infrared hyperspectral imaging (NIR-HSI) system combined with supervised classification techniques for the classification of honey samples from different botanical origins that were adulterated with sugar cane syrup at 20 % and 40 % w/w. Reflectance and transflectance modes of measurement were used to obtain honey spectral images from 864.53 nm to 1700 nm. Supervised classification algorithms, namely, stepwise discriminant analysis (SDA), support vector machine (SVM), and k-nearest neighbors (kNN) were used for the classification based on whole spectral data and feature selected spectral data. The best classification model from reflectance mode was observed from SDA based on a leave-one-out cross-validation (LOOCV) method with a test accuracy of 95 %. For transflectance mode, the classification model developed from SDA based on a LOOCV method provided better test set accuracy of 100 %. The classification performance levels of the models developed from SVM and kNN were lower with the use of the whole wavelength region. Moreover, the classification performance levels of SVM and kNN using the selected features by the SDA greatly increased, however, they were not as good as from using SDA alone. The classifier maps created using SDA and predicting the averaged spectra of all pixels in the image with 10-discriminant functions for each mode of measurement displayed a two-dimensional image that had a distinct colour pertaining to a particular class that could easily discriminate one class from another.

Application of an Electronic Nose as a New Technology for Rapid Detection of Adulteration in Honey

This work demonstrates the application of an electronic nose (e-nose) for discrimination between authentic and adulterated honey. The developed e-nose is based on electrodes covered with ionogel (ionic liquid + gelatin + Fe3O4 nanoparticle) films. Authentic and adulterated honey samples were submitted to e-nose analysis, and the capacity of the sensors for discrimination between authentic and adulterated honey was evaluated using principal component analysis (PCA) based on average relative response data. From the PCA biplot, it was possible to note two well-defined clusters and no intersection was observed. To evaluate the relative response data as input for autonomous classification, different machine learning algorithms were evaluated, namely instance based (IBK), Kstar, Trees-J48 (J48), random forest (RF), multilayer perceptron (MLP), naive Bayes (NB), and sequential minimal optimization (SMO). Considering the average data, the highest accuracy was obtained for Kstar: 100% (k-fold = 3). Additionally, this algorithm was also compared regarding its sensitivity and specificity, both being 100% for both features. Thus, due to the rapidity, simplicity, and accuracy of the developed methodology, the technology based on e-noses has the potential to be applied to honey quality control.

Honey Adulteration and Corporate Irresponsibility in India: Visualizing Corporate Sustainability

Responsibility is a crucial component of corporate sustainability. Deviation from responsibility is considered as irresponsible corporate practice. The present article highlights the issue of honey adulteration as an irresponsible corporate practice. The study is based on the Centre for Science and Environment investigation report on honey adulteration business in India. The article uses thematic analysis through descriptive research design with an inductive approach to show how honey adulteration is an irresponsible practice within the core area of corporate responsibilities—economic, legal and ethical domains. Honey adulteration is an irresponsible practice by the corporate in its core areas of responsibility and simultaneously affects all the stakeholders in its business relationship. Eventually, it would affect the business’s credibility and sustainability in the long run, if the issues are not appropriately addressed. The findings of the article suggest that to achieve business sustainability, corporations need to internalize and institutionalize the core responsibilities and government guidelines in their business operation. On the basis of the findings, the article offers relevant policy and practical implications for corporate to stay away from irresponsible business practices and suggests scopes for future research in the domain.

How to Effectively Reduce Honey Adulteration in China: An Analysis Based on Evolutionary Game Theory.

Apiculture has been greatly developed in recent years in China. Beekeeping cooperatives and honey manufacturing enterprises have increased rapidly. As a result, a variety of honey products have entered the market, adding vitality to the food economy; however, the adulteration of honey products is on the rise in China. Previous attempts to control the adulteration of honey products mostly relied on technical, product-specific measures, and there was a lack of modeling research to guide the supervision of the honey product industry. In order to help local governments to better control the adulteration of honey products from a management perspective, this paper establishes an evolutionary game model composed of beekeeping cooperatives, honey product enterprises, and local governments. Through stability analysis and model simulation, we found that local government subsidies to cooperatives have little impact on the game system. Local government penalties to cooperatives and price adjustments of unadulterated raw honey by cooperatives are effective management tools to reduce the adulteration behavior of cooperatives. Local government penalties for enterprises are an effective management tool to reduce the adulteration behavior of enterprises. This research provides useful information for government agencies to design appropriate policies/business modes so as to promote sustainability and the healthy development of the honey product industry in China.

Evaluation of honey authenticity in Thailand by analysis of carbon stable isotope ratio using elemental analyser coupled to isotope ratio mass spectrometry and cavity ring‐down spectrometry

SummaryIn Thailand, honey is used in a variety of commercial products that are distributed in domestic markets and abroad. As honey in Thailand comes from many locations, it is challenging to control the production quality of commercial products. The main problem commonly found in the honey industry is adulteration with undeclared added sugars. Identification of adulterated honey is crucial for authentication, which can increase consumer confidence on honey purity. In this study, 49 honey samples from beekeepers (natural) and 15 commercial honey producers were collected from different cities across Thailand. They were analysed for stable carbon isotope ratios (expressed as 13C/12C or δ13C and reported in relation to the international reference standard unit Vienna Pee Dee Belemnite, ‰ VPDB) using Elemental Analysis Isotope Ratio Mass Spectrometry (EA‐IRMS) for developing a δ13C database of natural honey and for checking the adulteration of honey. The δ13C values detected in 49 natural honey samples varied from −23.1‰ to −29.1‰ VPDB with an average of −26.8 ± 1.2‰ VPDB. According to the AOAC standard (No. 998.12, 2019), natural honey should have δ13C values lower than −23.5‰ VPDB, with an acceptable difference in δ13C values between honey and the protein extracted from honey up to 1‰. Compared to the standard guideline, 60% of commercial honey samples collected for this study (9 out of 15 samples) were found to contain undeclared added sugars.

Botanical and geographical origins of some commercial Apis mellifera var. adansonii honeys from Nigeria

In recent times, there has been an increase in adulterated honey in the commercial market owing to the lucrative nature of the honey business in Nigeria. Melissopalynology analyses can be used to obtain information about botanical and geographical origin of honey sources and to verify quality. Thus, this study was conducted to determine pollen composition and diversity in commercial honey samples from three states in Nigeria to ascertain their botanical and geographical origins. Seven commercial Apis mellifera var. adansonii honey samples were collected from three phytogeographical regions in Nigeria between 2017 and 2019. The samples were analysed for pollen content and other remaining structured elements occurring in sediment obtained using a non-acetolysed methodology. A total of 29 pollen types were identified, related to 18 families, 28 genera, and two unidentified pollen types. Fabaceae had the largest number of pollen types, followed by Asteraceae, Malvaceae, Arecaceae, Euphorbiaceae and Rubiaceae. Among the pollen identified, 22 pollen types were from nectariferous plants dominated by Dalbergia, Fagara/Zanthoxylum, Hymenocardia acida, Irvingia gabonensis, Macaranga, Nauclea latifolia and Pterocarpus. Monofloral honey of Nauclea latifolia and Pterocarpus sp. were also identified. The geographical origin demonstrates Apis mellifera var. adansonii using nectariferous sources available from the rainforest, forest-savanna ecotone, and Guinea savanna in the southeast, southwest, and central Nigeria, as well as in the brushwood, secondary forest, and open grassland areas, suggesting their importance as pollinators or pollen disperser agent of native flora and cultivated species.

A comprehensive review on unethical honey: Validation by emerging techniques

Globally, honey is consumed as natural functional food, featuring high economic value, related to its authenticity and purity. Due to the high demand for honey, it has been wildly targeted for food adulteration with substandard honey or low-price syrups, and emerging absorbent resin technology has also affected the honey market. Researchers worldwide have been working persistently to invent and innovate advanced technologies for detecting honey adulteration and assuring its authenticity. The compositional intricacy of pure and unauthentic honey could be easily detangled by a combined approach of chemometrics and instrument. In this palimpsest, various detection methods like ISCIRA, NMR, AT-FTIR, Sensors, PCR based assay united with an appropriate Multivariate approach that provides accurate and acceptable results for the determination of honey authentication and adulteration were described. The botanical origin authentication of honey was determined with the application of δ13C-EA-IRMS and δ13C-LC-IRMS coupled SVM, which discriminate samples based on specific markers. LIBS, NMR, HPTLC, UHPLC, GC, and real-time PCR, generated data and processed with LDA, OPLS, PCA, ANN, CNN etc. The generated data discriminate adulterated honey from pure, as NMR clubbed with PCA can detect 1% of adulterants in honey. Hence, various methodologies in chemometrics have manifested their proficiency during ground application. Perhaps, there is a long way to go in this field to develop a universal technology for honey authentication and adulteration detection.

Tracking of Thermal, Physicochemical, and Biological Parameters of a Long-Term Stored Honey Artificially Adulterated with Sugar Syrups.

The growing phenomenon of honey adulteration prompts the search for simple methods to confirm the authenticity of honey. The aim of the study was to evaluate the changes in thermal characteristics, physicochemical parameters, antioxidant and enzymatic activity of honey subjected to artificial adulteration. Two series of products were prepared with the use of two different sugar syrups with an increasing dosage of adulterant (0 to 30%). After 24 months of storage, the quality of adulterated samples (partially crystallized) was assessed in comparison to the control honey (solid). Used adulteration changed physicochemical parameters and reduced antioxidant and enzymatic activity of honey (p < 0.05). The admixture of syrup and invert (p < 0.05) reduced the viscosity of liquid phase of delaminated honey in a dose-dependent manner. In the study, artificially adulterated honeys were controlled using the standard differential scanning calorimetry, DSC. In all adulterated honeys, a specific glass transition, TG, was observed in the range of 34-38.05 °C, which was not observed for control honey and pure adulterants. Moreover, the additional Tgs were observed in a wide range from -19.5 °C to 4.10 °C for honeys adulterated by syrup only. In turn, the Tg in range of 50.4-57.6 °C was observed only for the honeys adulterated by invert. These specific Tg seem to be useful to detect honey adulteration and to identify the kind of adulterant used.

Analysis and Discrimination of Canadian Honey Using Quantitative NMR and Multivariate Statistical Methods

To address the growing concern of honey adulteration in Canada and globally, a quantitative NMR method was developed to analyze 424 honey samples collected across Canada as part of two surveys in 2018 and 2019 led by the Canadian Food Inspection Agency. Based on a robust and reproducible methodology, NMR data were recorded in triplicate on a 700 MHz NMR spectrometer equipped with a cryoprobe, and the data analysis led to the identification and quantification of 33 compounds characteristic of the chemical composition of honey. The high proportion of Canadian honey in the library provided a unique opportunity to apply multivariate statistical methods including PCA, PLS-DA, and SIMCA in order to differentiate Canadian samples from the rest of the world. Through satisfactory model validation, both PLS-DA as a discriminant modeling technique and SIMCA as a class modeling method proved to be reliable at differentiating Canadian honey from a diverse set of honeys with various countries of origins and floral types. The replacement method of optimization was successfully applied for variable selection, and trigonelline, proline, and ethanol at a lower extent were identified as potential chemical markers for the discrimination of Canadian and non-Canadian honeys.

Honey–Water Content Analysis by Mixing Models Using a Self-Calibrating Microwave Method

Microwave techniques, as an indirect approach, can be applied for analyzing water content in honey by way of permittivity measurements. However, these techniques require proper calibration to accurately perform such indirect evaluation. Improper calibration standards used in this calibration process could naturally result in a reduction in the accuracy and thus the performance of dielectric characterization using microwaves. Self-calibrating microwave techniques can reduce the effects of imprecise standards and thus improve the performance of microwave measurements by bypassing the requirement of calibration standards. In this study, we develop a self-calibrating microwave measurement technique to determine the relative permittivity of honey samples and implement binary mixing models to predict adulteration levels of water-adulterated honey. From this implementation, it is observed that the parallel-capacitance mixing model could efficiently be applied to determine the concentration of water adulteration by examining the differences between absolute values of the real parts of the measured and predicted complex permittivities of adulterated honey.

Authentication of Apis Cerana and Apis Mellifera Derived Honey Using Major Royal Jelly Protein 2 Gene and Spectroscopy

Honey, as a natural health product, encounter great compositional influence by its entomological, botanical and geographical origin. In Bangladesh, apiculture relies on the honey production by mostly the European honeybee, Apis mellifera. However, the Asian honeybee Apis cerana honey typically costs three times as much as honey from A. mellifera in honey market due to the production limit, variation in nutrient composition, being prone to adulteration by mislabeling or incorporation of two honey. A. mellifera honey may be mislabeled as A. cerana honey or mixed with A. cerana honey by dishonest businesses and beekeepers who are motivated by substantial earnings. The current study used the MRJP2 (Major Royal Jelly Protein 2) gene and spectroscopic analysis to differentiate honey from A. mellifera and A. cerana. Primers for two different species were designed. A. cerana and A. mellifera exhibited amplification products of 212 and 560 bp, respectively. Duplex PCR was able to identify the presence of as little as 1% A. mellifera honey in the mixture. A. mellifera and A. cerana originated honey was differentiated using MRJP2 gene and UV-VIS spectroscopy. The resulted banding pattern can be used to identify adulterated honey. UV-VIS spectroscopy has been used to determine the concentration of DNA at different wavelength for standardizing the corresponding honey types. Amplifications MRJP2 gene fragments in gel electrophoresis and responsiveness in screening for two honey types with their probable admixtures provided an effective tool for discriminating the honey types. DNA Spectrophotometric Spectrum analysis in different wavelengths and ratios also provided a clear-cut discrimination protocol of these two honey types. The findings of these two methods can be used efficiently apply towards authentication of the honey originated by A. mellifera and A. cerana in the honey market of Bangladesh.

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.

Authentication of chaste honey adulterated with high fructose corn syrup by HS-SPME-GC-MS coupled with chemometrics

The adulteration of the honey industry is serious, especially the syrup adulteration method is difficult to detect. To develop the detection technology of chaste honey adulteration with high fructose corn syrup (HFCS), the condition of headspace solid-phase micro-extraction (HS-SPME) was optimized for extracting the volatile compounds of chaste honey. HS-SPME conditions were selected for optimization using Plackett Burman, steepest ascent and Box-Behnken. Volatile compounds from chaste honey adulterated with HFCS were analyzed by gas chromatography-mass spectrometry (GC-MS). M/Z RT pairs data from differences in chaste honey contents in HFCS-adulterated samples were analyzed by linear discriminant analysis (LDA), principal component analysis (PCA), and artificial neural network (ANN). The results indicated distinguished adulterated chaste honey at different proportions was not ideal using LDA and PCA. A back propagation (BP) artificial neural network （ANN）model was constructed based on m/z RT pair data. Correlation coefficients of training, verification, testing and comprehensive data of BP-ANN were 0.994, 0.945, 0.968 and 0.979, respectively, indicating good accuracy of the BP-ANN prediction model. The present study discusses a new strategy that determined the chaste honey contents in HFCS-adulterated samples as well as did not rely on the identification of volatile compounds.

Determination of Possible Adulteration and Quality Assessment in Commercial Honey.

This study aims to predict several quality traits in commercial honey samples simultaneously and to reveal possible honey adulteration using a field-deployable portable infrared spectrometer without any sample preparation. A total of one hundred and forty-seven commercial honey samples were purchased from local and online markets in Turkey and the United States of America (USA), and their soluble solids (°Brix), pH, free acidity, moisture, water activity (aw), glucose, fructose, sucrose, and hydroxymethyl furfural (HMF) contents were determined using reference methods. The HMF (n = 11 samples) and sucrose (n = 21) concentrations were higher than the regulatory limits in some tested samples. The exceeding HMF content may imply temperature abuse during storage and prolonged storing. On the other hand, high sucrose content may indicate possible adulteration with commercial sweeteners. Therefore, soft independent modeling of class analogies (SIMCA) analysis was conducted to reveal this potential sweetener adulteration in the samples, and the SIMCA model was able to identify all the flagged samples. The suggested FT-IR technique may be helpful in regulatory bodies in determining honey authenticity issues as well as assessing the quality characteristics of honey samples in a shorter period and at a lower cost.

Detection of Low-Level Adulteration of Hungarian Honey Using near Infrared Spectroscopy

Honey adulteration is a worldwide problem; however, its detection is a challenge for researchers and authorities. There are numerous ways of honey counterfeiting; amongst them, direct adulteration is one of the most common methods. Correlative techniques, such as near-infrared spectroscopy (NIRS), are useful tools in the detection of honey adulteration; however, this method has not been applied to Hungarian honeys. The aim of this research was to investigate the performance of NIRS for the detection of sugar syrup addition to Hungarian honeys at lower concentration levels (&lt;10% w/w). Acacia, rape, forest, sunflower, and linden honeys were mixed with high-fructose-content sugar syrup, rice syrup, or self-made glucose fructose syrup in 3%, 5%, and 10% w/w. NIRS analysis was performed in the spectral range of 950–1650 nm. Principal component analysis was coupled with linear discriminant analysis and partial least square regression models were built for the classification and prediction of adulteration levels, respectively. Our results showed that the performance of NIRS highly depends on both type of syrup and honey. PCA-LDA models provided the 100% correct classification of control in the case of all the models, while PLSR results could predict the added sugar syrup content in the case of rice and F40 syrup models, obtaining &gt;2.2 RPDCV value.

Computer vision analysis of sample colors versus quadruple-disk iridium-platinum voltammetric e-tongue for recognition of natural honey adulteration

This work presents a deep learning approach for computer vision-based recognition of natural honey adulteration. The results are compared with the machine learning models using chemical data obtained with an innovative bimetallic voltammetric electrode that acts as an e-tongue. Computer vision is based on the recognition of the color of the sample, which changes with the addition of undesirable synthetic substances. The pictures were taken with a smartphone under controlled conditions using seven different background illumination colors for samples that contained 0 – 50 % foreign additives. In the best case root mean square error (RMSE) in the regression approach was 0.46 % for the external test set at R2 = 0.9993, calculated as a compatibility evaluation between the true values and the prediction result. The minimum RMSE for the voltammetric measurements on the quadruple-disk iridium-platinum electrode and the regression algorithms PCR, PLSR, and SVR was 0.25 % with R2 = 0.9998. It has been shown that when food adulteration affects its color, it is possible to successfully replace the classical e-tongue with computer vision. The convenient, rapid, and nondestructive approach does not require the use of equipment, reagents, or solvents and laboratory work techniques typical for analytical chemistry.

Untargeted metabolomic analysis of honey mixtures: Discrimination opportunities based on ATR-FTIR data and machine learning algorithms

Honey adulteration issues represent an important concern at distinct societal levels (i.e. producers, consumers, and state authorities) because honey represents one of the most falsified food commodities in the world. Adulterations can be more or less subtle and, as a consequence, these practices can be easy or very difficult to detect. One of the subtlest types of adulteration is represented by the detection of honey mixture, when honey is wrongly labelled as monovarietal. During the last few years, it was demonstrated that a refinement of the analytical results can be achieved by the employment of artificial intelligence in the development of food and beverages recognition models. In this light, our study proposes a new approach for the detection of colza honey addition to acacia one and the identification of the presence of sunflower honey in linden samples. For this purpose, the association between ATR-FTIR spectroscopy and machine learning algorithms was applied for recognition models development. Based on these models, it was possible to detect the mixture of colza-acacia mixture with an accuracy of 94.4%, while the blend of linden and sunflower honey was possible to be identified with a 90.7% accuracy.

Low-cost rapid workflow for honey adulteration detection by UV–Vis spectroscopy in combination with factorial design, response surface methodology and supervised machine learning classifiers

Honey is often adulterated and heat treated for higher profits. This study aims to develop a low-cost, easy to perform, and less time-consuming method for screening the honey status. Known (12: adulterated 4; unadulterated 8) and unknown (7) honey samples of different geographical and biological sources were used to simulate adulteration and heat treatment effects by factorial design. Crude methanol-chloroform extracts of simulated samples were used for generating UV–Vis spectra (200-700 nm) and the effects were assessed by response surface methodology. Four machine learning classifiers were applied to predict the sample status, and among them Neural network and Random-forest were found to be satisfactory. The selected classifiers successfully detected 2 adulterated, and 4 unadulterated honeys among 7 unknown samples. Present workflow is proved to be an effective technique in detecting honey adulteration, and can be applied in honey and other food industries for optimization and quality control of their products.

Intelligent electronic tongue system for the classification of genuine and false honeys

ABSTRACT Honey quality is a global concern since this product is highly susceptible to adulteration, given its competitive price. As a reliable strategy for honey authenticity determination, this work introduces an intelligent classification system that considers the pattern recognition point of view to develop an economical and quick analytical method to identify and differentiate genuine from adulterated honey. This work used an electronic tongue composed of three working electrodes of carbon, platinum, and gold. The system used Cyclic voltammetry to obtain data from 50 genuine and 50 adulterated honey samples. Subsequently, the system used multivariate data analysis using a pattern recognition methodology composed of three big stages, including data organization and normalization, dimensionality reduction, and k-Nearest Neighbors (k-NN) as a classification method. The process was validated with the Leave One Out Cross Validation technique (LOOCV), reaching a classification accuracy performance of 100%. The results show that it was possible the development of a combined methodology between analytical tools and chemometrics for an in-situ, quick and efficient authenticity honey evaluation.

Application of Fourier transform infrared (FT-IR) spectroscopy and multivariate analysis for detection of adulteration in honey markets in Ethiopia

Honey is a highly susceptible food item to adulteration in national and international trade. Spectrum screening by FTIR coupled with multivariate analysis was investigated as an alternate analytical technique for honey adulterations and authentication. This technique was evaluated using pure honey samples that were blended at a ratio of 0–50% with commonly known adulterant materials and honey samples that were readily available for purchase in the Addis Ababa markets channel. Holeta Bee Research's bee farm pure honey, which is authentic honey, is employed as the control in this experiment. In the region, 4000–400 cm−1, spectral data of honey samples and five adulterant materials were recorded. The combination of spectra measurement with multivariate analyses resulted in the visualization of honey grouping and classification based on their functional group. The bands at 1800–650 cm−1 spectral region were selected for successful discrimination of clusters. Based on spectral differences, cluster analysis (CA) is also capable of grouping and separating pure from contaminated honey. Principle component analysis was able to visualize the differentiation of deliberately adulterated honey and commercially available from authentic ones. According to the results of our investigation, using FTIR analysis methods along with multivariate statistical analysis of the data could be considered useful fingerprinting procedures for identifying samples of pure and adulterated honey.