Honey Adulteration Research Articles

Honey adulteration detection based on composition and differential scanning calorimetry (DSC) parameters

One of the most important issues regarding the quality of honey is its adulteration through the addition of non-allowed substances. For this reason, this study aimed at assessing the usefulness of DSC combined with sugar composition analysis for the detection of sunflower honey adulteration with five types of syrups (agave, maple, rice, corn, and inverted sugar syrup) in percentages of 5, 10 and 20%. Sunflower honey and the syrup samples showed differences in the fructose and glucose content. For these two sugars, an overall reduction of the concentration was observed following adulteration; a greater difference was observed when the adulteration was made with corn syrup, maple syrup or rice syrup. Authentic honey showed one or two thermal transitions (glass transition and a second transition), and notable different DSC parameters by comparison to syrup samples. The addition of syrup and the increase of the proportion of syrup added determined a slight decrease of the glass transition temperature of honey. The sugar composition and the DSC parameters coupled with PLSR were successful in predicting the percentage of adulteration agent added into honey.

Geographical and entomological differentiation of Philippine honey by multivariate analysis of FTIR spectra

Honey is a product that is sought after by consumers because of its desirable properties. However, due to the high demand and limited supply, honey is often the target of falsification. The properties of honey are ultimately tied to its botanical, geographical, and entomological origins. Thus, accurate knowledge of these information is a step towards identifying honey adulteration. This work reports the successful discrimination of Philippine honey samples based on their geographical and entomological origins through the multivariate analysis of their FTIR spectra. Application of the principal component analysis (PCA) led to the distinct separation of samples. In the presented method, no other preprocessing of the FTIR spectral data was performed, yet the observed discrimination is comparable to works with spectral preprocessing. Application of hierarchical clustering analysis (HCA) also led to the separation of the honey samples into distinct clusters. The observed clustering in the dendrogram was found to correspond more on the species difference rather than the geographical location. This work has successfully demonstrated the discrimination of Philippine honey samples in terms of their geographical and entomological origins using FTIR spectroscopy and chemometrics.

Dielectric properties of honey adulterated by sugar syrup

Abstract Dielectric properties of pure honey adulterated by varying amount of sugar syrup have been measured over 20 Hz to 2 MHz frequency range, at room temperature. The samples were prepared by mixing different proportions of sugar syrup in pure honey. It has been observed that, dielectric constant (ε′) and dielectric loss (ε″) of the mixture increases as percentage of sugar-syrup in honey increases. ε′ of the mixture decreases rapidly with the increase in frequency and achieves static value at higher frequency. ε″ decreases linearly with increase in frequency. The relaxation time corresponding to tanδ peak varies nonlinearly with increase in concentration of sugar syrup in the honey. The dielectric loss at 2 MHz, and the relaxation frequency of the mixture increases linearly with increase in concentration of sugar syrup in honey, which can be used to estimate the amount of adulteration of honey by sugar syrup.

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.

IDENTIFICATION AND AUTHENTICATION OF HONEY USING CHEMOMETRIC ANALYSIS BASED ON ATR-FTIR AND RAMAN SPECTROSCOPY

Objective: This study aims to develop a fast, fitted, and accurate classification method for authenticating honey.&#x0D; Methods: The authentic honey samples were obtained from local beekeepers and distributors, while most of the adulterated honey samples were made from a mixture of fructose syrup, authentic honey, sodium bicarbonate, and sweet soy sauce, while others were received from local distributors. To authenticate the honey, samples were divided into two classes, real honey, and adulterated honey. Similarly, to classify the honey, we categorized two classes, Apis spp. and stingless bee. ATR-FTIR spectra data were collected using Thermo Scientific’s OMNIC FTIR software and processed using Thermo Scientific’s TQ Analyst software by dividing the wavelengths into six regions between 550-4000 cm-1. and Raman spectra data were collected using HORIBA LabSpec 6 software and processed using CAMO’s Unscrambler X10.4 software by dividing the Raman shifts into five regions between 200-3350 cm-1.&#x0D; Results: Our methods effectively authenticate the honey-based on ATR-FTIR and Raman spectra. Based on ATR-FTIR spectra data, the best region of honey’s authenticity is Region 1,3,4,5,6 (2800-3000 cm-1; 1640-1760 cm-1; 1175-1455 cm-1; 950-1175 cm-1; 750-950 cm-1) and the best region for classification is 750-950 cm-1. Based on Raman spectra data, the best region of honey’s authenticity is 970-1150 cm-1 and the best region for classification are 1150-1480 cm-1 and 970-1480 cm-1.&#x0D; Conclusion: This study successfully demonstrated accurate methods based on ATR-FTIR and Raman spectral data to authenticate and classify the honey.

Aberrant in physicochemical properties, functional health and medicinal grades of honeys from different sales outlets in Southwest Nigeria

BackgroundHoney is consumed for the derived benefits as sweetener, for healing wounds, skin burns, regulating blood sugar level, treatments of some ailments, etc. In Nigeria, adulteration of honey is a serious concern, and these adulterated or fake honeys have health implications. This research aims to compare and contrast the physicochemical qualities, functional health and medicinal values of honey samples from local honey parkers, supermarkets and beekeepers’ sales outlets in Southwest Nigeria.ResultsThe darkness of the honey from the three sources was significantly different. The honey samples from the beekeepers have ash contents values of 0.25–1.0 in line with CAC and IHC, whereas 7(46.66%) and 2(13.33%) of honey samples from the supermarkets and local handlers/parkers, respectively, deviated from this standard values. The wound healing development after 8 days of topical treatment of incision wounds with honey from beekeepers shows a good development. 93.3% of the honeys sourced from the beekeepers, 58.06 and 66.05% of the honeys sourced from the supermarkets, and the local honey markets were found to be original.ConclusionThe originality of honey for consumers’ satisfaction is dependent on the complimentary factors of physicochemical properties, functional health and medicinal values. These findings provide information for consumers’ awareness on categorization of honey as original/pure, adulterated and fake and the health implications.

Traceability issues of honey from the consumers’ perspective in Romania

The Romanian honey market is facing a problem related to traceability, especially when honey is produced in more than one country and its origin is indicated as a blend of EC and non-EC honeys. The increase of honey adulteration has consequences on both consumers and honey producers with considerable negative effects. The aim of the study was to identify the factors that influence honey purchasing behaviour and to evaluate consumers’ awareness related to honey adulteration in Romania among selected age segments. An online survey was conducted between 2020-2021 on a sample of 1,233 respondents. The questionnaire covered aspects related to purchasing behaviour and honey adulteration, complemented with socio-demographic questions. The data were evaluated using descriptive, non-parametric and multivariate statistics. The results showed that the most important factors considered during the purchasing process by Romanian honey consumers were health factor and country of origin followed by producer reputation and ecological aspect, while the least important were discounts, promotion and brand reputation. The older respondents are more aware of honey adulteration and know better that crystallisation is an indicator of quality. This study provides important information for policymakers and the whole beekeeping chain in Romania. Education in terms of honey authenticity and traceability will help consumers to choose local honey of high quality and to avoid adulterated products. This consumption and purchasing behaviour will discourage producers from honey counterfeiting.

Detection and Classification of Honey Adulteration Combined with Multivariate Analysis

Honey is a natural sweetener with a yellowish substance made up of bee secretions and plant nectar extracts. Main composition of honey are sugars or carbohydrates and water in the chemical composition and contain a great number of minor components such as minerals, amino acids, proteins, acidity, and pH. Honey adulteration is a global concern due to lack of awareness of people and policies. There is a various method that has been conducted to detect honey adulteration such as SCIRA, DSC, FTIR, NIRS, and NMRand these methods mostly used multivariate data analysis to classify the adulteration of honey. PCA is the most used technique in the classification of honey adulteration where the data obtained is clustered according to adulteration level and type of theadulterant. This paper explains on different methods to detect honey adulteration and common technique used on classification of honey.It can be concluded that PCA is the most used technique based on different method of honey adulteration detection.

Evaluation of chemometric classification and regression models for the detection of syrup adulteration in honey

Honey is a highly desirable commodity hence a target of adulteration to increase its bulk. Spectroscopic methods and chemometrics offer a fast, easy, and simple approach to the detection of syrup adulterations in honey. The idiosyncrasies associated with different instrumental data suggest varying performance for each algorithm. The objective of this study was to evaluate the performance of five commonly used classification and regression algorithms for the detection of syrup adulteration in honey. The prediction performance of the classification and regression algorithms was evaluated using data obtained from Fourier-transform infrared spectrometer with a Horizontal Attenuated Total Reflectance (HATR) Accessory analysis of honey samples. Gradient boosted discriminant analysis (GBDA) and Support vector machines discriminant analysis (SVMDA) were able to differentiate between adulterated and pure honey samples with an external validation set prediction accuracies of 0.988 and 0.981, respectively. Application of feature selection method did not lead to improved prediction accuracies. Gradient boosted regression (GBR) initialized with a ridge regression predicted the percentage level of adulteration with a regression coefficient of 1.000. The RMSE for the optimal GBR was 2.183 and 0.018 for before and after feature selection with the partial least squares (PLS) algorithm, respectively. Ensemble methods are generally better for both classification and regression of honey using FTIR-HATR spectroscopic data.

Physicochemical properties and detection of glucose syrup adulterated Kelulut ( Heterotrigona itama ) honey using Near‐Infrared spectroscopy

In this study, the physicochemical properties and the precision of adulterated honey detection by NIRS were assessed. A total of 11 adulteration ratio samples were produced with glucose syrup. The ash and moisture content of the honey samples were found to be in accordance with the Malaysian Standard, including all adulterated ones. The pH value and HMF content complied with the standard except for the pH of 100% adulterated honey and HMF content of 30–100% adulteration. NIR spectra were scanned at 900–1700 nm, 1848 spectra were recorded, and the data were pre-processed to reduce the unwanted spectral interference. Chemometric techniques and machine learning were used as prediction tools for adulteration levels. This includes principal component analysis (PCA), k-nearest neighbors (kNN) and random forest. The accuracy of the prediction samples was above 90%. Hence, this was satisfactory for rapid detection of Kelulut honey adulteration. Novelty impact statement Kelulut honey is becoming increasingly popular and in-demand due to its health advantages. Kelulut honey adulteration is more likely as a result of this. The NIRS can detect Kelulut honey adulteration more quickly than any other method with greater efficiency and reliability. Honey adulteration may be detected rapidly and non-destructively using NIRS and chemometrics. The results demonstrate that employing this technology as a screening method in routine laboratories might be useful for quality control assessments.

Authentication of pure and adulterated Sumbawa monofloral honey using ultraviolet-visible spectroscopy

Sumbawa monofloral honey is one of the most expensive honeys in Indonesia. In this present study, ultraviolet-visible (UV-vis) spectroscopy was utilized to discriminate between authentic pure Sumbawa monofloral honey and its adulterated one. The adulterated honey samples were created by intentionally mixing pure authentic Sumbawa monofloral honey with high fructose corn syrup (HFCS) at a level of 10% (v/v). A total of 50 samples of pure and adulterated samples were prepared. The UV-vis spectral data of each sample were obtained by using a UV-vis spectrometer in the range of 190-1100 nm. The result of PCA (principal component analysis) using the first two PCs with the CEV (cumulative explained variance) of 93% shows that the pure and adulterated Sumbawa honey samples could be separated. All pure samples lied in the negative of PC1 (PC1<0) and all adulterated samples were in the positive of PC1 (PC1>0). In conclusion, this result suggested us the possible application of UV-vis spectroscopy for authentication of Sumbawa honey adulterated with HFCS in a simple and low-cost method.

Study of Quality Factor (Q) and Phase Angle (θ) obtained per electrical impedance to characterization and detection adulteration in honey.

The quality control of honey is very important for the food industry and for the final consumer. Honey has constantly been the target of adulteration, usually with the aim of illegally increasing profit. However, the analytical methods used in the fraud investigation sometimes are very complex and beyond time-consuming. Electrical measurements appear as a good alternative to the traditional methods of analysis due to its practicality and low costs. In order to show the applicability of some electrical parameters derived from the impedance technique for honey quality control analysis, in the present study, samples were purposefully adulterated with syrup to simulate adulteration conditions. Then, they were submitted to electrical impedance analysis with frequency scanned from 10 Hz to 2 MHz. The impedance module (Z), the phase angle and the quality factor (Q) data were collected and studied. The results showed that there is a linear correlation between the frequencies relative to the peaks of the Q for samples and adulterant concentration with coefficient of determination of ~0.992. The proposal to use the Q as a property to be used in methodologies of analysis is the main contribution from this study, since it is traditionally little mentioned in the literature.

Determination of the Carbohydrate Profile and Invertase Activity of Adulterated Honeys after Bee Feeding

The higher demand for honey from consumers, combined with its limited availability, has led to different types of honey adulteration, causing substantial economic as well as negative impacts on consumers’ nutrition and health. Therefore, a need has emerged for reliable and cost-effective quality control methods to detect honey adulteration to ensure both the safety and quality of honey. To simulate the process with those applied by beekeepers in real-time, bee colonies were fed with different types of bee feeding (sugar syrup, candy paste and commercial syrup). The produced samples were analyzed for their carbohydrate profile and their invertase activity with the aim to find the effects of bee feeding on the quality of the final product. Honey samples produced after feeding with commercial syrup presented low fructose (22.9 %) and glucose (31.7 %) concentrations and high content of maltose (20.1%), while the samples that came from bee feeding with sugar syrup and candy paste had high concentrations of sucrose (6.2 % and 3.2 %, respectively), exceeding in some cases the legislative limits. Moreover, the samples coming from sugar feeding had lower values of invertase activity, while the group with inverted syrup was clearly discriminated through multi-discriminant analysis. The invertase activity of control samples was found at 153.7 U/kg, which was significantly higher compared to the other groups. The results showed that bee feeding during honey production might lead to adulteration, which can be detected through routine analyses, including the carbohydrate profile and the invertase activity.

Identification of Stingless Bee Honey Adulteration Using Visible-Near Infrared Spectroscopy Combined with Aquaphotomics.

Honey is a natural product that is considered globally one of the most widely important foods. Various studies on authenticity detection of honey have been fulfilled using visible and near-infrared (Vis-NIR) spectroscopy techniques. However, there are limited studies on stingless bee honey (SBH) despite the increase of market demand for this food product. The objective of this work was to present the potential of Vis-NIR absorbance spectroscopy for profiling, classifying, and quantifying the adulterated SBH. The SBH sample was mixed with various percentages (10–90%) of adulterants, including distilled water, apple cider vinegar, and high fructose syrup. The results showed that the region at 400–1100 nm that is related to the color and water properties of the samples was effective to discriminate and quantify the adulterated SBH. By applying the principal component analysis (PCA) on adulterants and honey samples, the PCA score plot revealed the classification of the adulterants and adulterated SBHs. A partial least squares regression (PLSR) model was developed to quantify the contamination level in the SBH samples. The general PLSR model with the highest coefficient of determination and lowest root means square error of cross-validation ( and ) was acquired. The aquaphotomics analysis of adulteration in SBH with the three adulterants utilizing the short-wavelength NIR region (800–1100 nm) was presented. The structural changes of SBH due to adulteration were described in terms of the changes in the water molecular matrix, and the aquagrams were used to visualize the results. It was revealed that the integration of NIR spectroscopy with aquaphotomics could be used to detect the water molecular structures in the adulterated SBH.

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

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

The rapid detection of acacia honey adulteration by alternating current impedance spectroscopy combined with 1H NMR profile

Honey adulteration is one of the most prominent issues in food production. Herein, the alternating current (AC) impedance spectroscopy combined with 1H NMR profile were used to reveal the adulterants in acacia honey. Variation of electrical parameters such as solution resistance (Rs), double layer capacitance (CD), charge transfer resistance (RCT) and 1H NMR profile for 10%–90% (w/w) syrups or rape honey adulterated acacia honey samples were analyzed. We clearly demonstrate that the difference of the AC impedance between acacia honey and adulterated honey is obvious, and the levels of RCT of acacia honey samples are always smaller than those of syrups and adulterated honey. Furthermore, the relaxation peaks shift toward the lower frequency zone and gradually increase in intensity with increasing proportion of syrup or rape honey. Also, 1H NMR profile can clearly indicate the adulterant and adulteration proportion, supporting the results of the AC impedance spectroscopy measurement. And the effectiveness of impedance measurement can also be proved by principal component analysis. Our results provide new insights into the authenticity identification of acacia honey and prove the AC impedance measurement combined with NMR is a rapid, nondestructive method for honey authenticity verification.

Near-infrared spectroscopy with chemometrics for identification and quantification of adulteration in high-quality stingless bee honey

This study presents a simple, rapid, and non-destructive approach of combining near-infrared spectroscopy (NIRS) with chemometrics for the evaluation of adulteration levels in stingless bee honey (SBH). Three high-quality SBH samples were directly obtained from the northern part of Malaysia and then adulterated with water (W) and apple cider vinegar (AC) at an adulteration range of 4.76%–50%. The NIRS analysis was performed at the spectral region of 700–1100 ​nm. The chemometric tools used include principal component analysis (PCA), hierarchical cluster analysis (HCA), principal component analysis-linear discriminant analysis (PCA-LDA), and partial least squares regression (PLSR). Using the first three principal components (PCs) with a total of 97.95% of explained variance, a complete distinction between adulterants, pure, and adulterated samples was achieved. PCA-LDA model with 100% classification accuracy in prediction was able to discriminate each SBH adulterated with W and AC. A general PLSR model to quantify the level of adulteration was developed. The best prediction model used 7 factors with a high correlation coefficient ‘R’ and low root mean square error of prediction ‘RMSEP’ (RP ​= ​0.995 and RMSEP ​= ​1.350%). These results confirm the combined ability of NIRS at region 700–1100 ​nm and chemometrics to effectively discriminate and quantify adulterated SBHs.

Identification and quantification of adulterated honey by Raman spectroscopy combined with convolutional neural network and chemometrics

In this study, Raman spectroscopy combined with convolutional neural network (CNN) and chemometrics was used to achieve the identification and quantification of honey samples adulterated with high fructose corn syrup, rice syrup, maltose syrup and blended syrup, respectively. The shallow CNNs utilized to analyze honey mixed with single-variety syrup classified samples into four categories by the adulteration concentration with more than 97% accuracy, and the general CNN model for simultaneously detecting honey adulterated with any type of syrup obtained an accuracy of 94.79%. The established CNNs had the best performance compared with several chemometric classification algorithms. In addition, partial least square regression (PLS) successfully predicted the purity of honey mixed with single syrup, while coefficients of determination and root mean square errors of prediction were greater than 0.98 and less than 3.50, respectively. Therefore, the proposed methods based on Raman spectra have important practical significance for food safety and quality control of honey products.

Quantitative determination of acacia honey adulteration by terahertz-frequency dielectric properties as an alternative technique

The dielectric characteristics in the terahertz region contribute to a revealing insight into the material components and provide intermolecular information. The dielectric properties of adulterated honey, described as the real and imaginary parts of the complex dielectric constant (Re[ε] and Im[ε]), were obtained from 0.3 to 1.5 THz. The relationship between invert syrup proportions and complex dielectric constants at different frequencies implied the possibility of using the dielectric property as an indicator of honey authenticity. The selected effective dielectric variables of Re[ε] and Im[ε] and their combination were chosen by stability competitive adaptive reweighted sampling (SCARS) algorithm and then used to establish PLS models. The accuracy and uncertainty result revealed SCARS-PLS model based on the combination of Re[ε] and Im[ε] is the best model relatively. These findings indicated the potential utility of this rapid, non-destructive, and on-site method for authenticity verification.

Raman spectroscopy combined with machine learning algorithms to detect adulterated Suichang native honey

Zhejiang Suichang native honey, which is included in the list of China’s National Geographical Indication Agricultural Products Protection Project, is very popular. This study proposes a method of Raman spectroscopy combined with machine learning algorithms to accurately detect low-concentration adulterated Suichang native honey. In this study, the native honey collected by local beekeepers in Suichang was selected for adulteration detection. The spectral data was compressed by Savitzky–Golay smoothing and partial least squares (PLS) in sequence. The PLS features taken for further analysis were selected according to the contribution rate. In this study, three classification modeling methods including support vector machine, probabilistic neural network and convolutional neural network were adopted to correctly classify pure and adulterated honey samples. The total accuracy was 100%, 100% and 99.75% respectively. The research result shows that Raman spectroscopy combined with machine learning algorithms has great potential in accurately detecting adulteration of low-concentration honey.