Pure Honey Samples Research Articles

Melissopalynological knowledge regarding the uniflorality of honeys: the case of under-represented pollen

Melissopalynological knowledge, regarding the uniflorality of honeys, in terms of pollen content, depends on the plant source, but, in many cases, it is difficult to establish the threshold of pollen percentage for a honey to be considered unifloral. This research aims to increase knowledge for the classification of unifloral honeys through pollen analysis. The use of fresh experimental beehives placed under controlled conditions in 11 different specific areas of Euphorbia resinifera (resin spurge) enabled the production of the purest possible honey samples, which could then serve as a reference for testing uniflorality of this type of honey. The experimentally obtained results of the pollen analysis of these pure honey samples revealed a low absolute number of pollen grains and a low percentage of pollen of E. resinifera, which confirms the under-representation of the pollen of E. resinifera in honey labelled as this type. The pollen analysis enabled a combination of pollen grains to be established which greatly facilitates the differentiation of this honey type. Furthermore, we propose that the threshold that delineates the percentage of pollen necessary for this honey type to be considered unifloral can be lower than 10% if combined with a high percentage of the sum of Ceratonia siliqua/Eryngium f./Capsella f./Lactuca f. pollen. Using this approach, the uniflorality was tested of ten honey samples sold commercially as resin spurge using the aforementioned criteria.

ASSESSMENT OF THE INFLUENCE OF HEALTHY FOODS ON THE ANTIOXIDANT CAPACITY AND IMMUNE RESPONSE OF THE ORGANISM

The analyzes carried out in this research were performed on meadow and mountain honey samples from Bosnia and Herzegovina. The geographical origin of the analyzed honey samples is the area of Mount Majevica in the Tuzla Canton and the area of Livno, Canton 10, Sarajevo, Sarajevo Canton and Vlašić, Central Bosnia Canton. Honey samples were collected from 8 different localities. During the analysis, all honey samples were up to one year old. With the aim of testing the antioxidant capacity of honey, the polyphenol content and total antioxidant capacity were determined. The total antioxidant capacity of honey was determined by the indirect FRAP (Ferric Reducing Antioxidant Power) method, and the polyphenol content by the Folin-Ciocalteu method. The results showed that there is a significant correlation between the antioxidant capacity of the analyzed honey samples and the content of polyphenols in them. In order to compare the antioxidant activity and polyphenol content of pure honey samples (without additives) with honey mixtures (additions: ginger, garlic, sweet wormwood, propolis), mixtures were prepared for all samples, namely 5 mixtures of honey and ginger; 5 mixtures of honey and garlic, 5 mixtures of honey and sweet wormwood and 3 mixtures of honey and propolis. Analyzing the results, a comparative analysis of the polyphenol content and total antioxidant capacity in honey samples before and after the addition of wormwood, ginger, garlic and propolis was performed, and the influence of the mixture's standing time on the tested parameters was also examined. The results of these analyzes showed that additives to honey significantly affect its antioxidant activity and polyphenol content, and that standing the prepared mixtures can lead to an additional increase or decrease of the tested parameters.

Study on Thermal Decomposition Mechanisms of Indian Honey of Different Origins using Differential Scanning Calorimetry and Vector Elemental Analyzer Techniques

Background: This paper reports the thermal decomposition mechanisms of five pure honey samples of Indian origin using Differential Scanning Calorimetry (DSC) and Euro Vector Elemental Analyzer (EVEA) techniques. Methods: We have identified three transition temperatures along with a change in specific heat capacity (ΔCp), change in enthalpy (ΔH), and Gibbs free energy (ΔG) of these samples. Finally, samples were subjected to thermal elemental analyses to quantify the released amount of O, N, C, and H. Since glucose, fructose, and sucrose are the principal ingredients, they are present in the honey sample, along with good numbers of other organic molecules in small quantities. In addition, we have also recorded the C, H, and O percentages of pure glucose, fructose, and sucrose powders and correlated the ratio of C/H and C/O with honey samples. Results: The decomposition temperature for honey samples lies between 113.83°C and 127.07°C range. The ratios of these elements help us to ascertain the purity of these samples as well as to identify the dominating percentage of principal ingredients present in the given honey sample. Conclusion: The obtained experimental results were further used to identify the source of origin and medicinal quality and stability of honey samples.

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.

Design and manufacturing a microcontroller based measurement device for honey adulteration detection

The aim of this study is the detection of adulteration in honey by a microcontroller measurement device. For this purpose, 18 pure honey samples from Sidr, Locoweed and Citrus honey were prepared, whose physicochemical characteristics, including moisture content and ⁰Brix, pH, color and ash content were measured. To detect impurities in honey, electrical conductivity and light refraction were used as inputs of an artificial neural network (ANN) classifier. This allowed fraud detection in honey by a portable instrument developed by enclosing the electrical conductivity and light sensors connected to a smartphone application. The results revealed that increasing impurities caused a decrease in ⁰Brix and an increase in the pH of honey. The highest value of moisture was 17.5% (for Locoweed) and the highest value of pH was 4.69 (for Sidr) at 75% impurity concentration. Citrus showed the highest value of ash and electrical conductivity of 0.36 and 415 ppm, respectively. The lowest error of training the ANN classifier of 13% was obtained with 14 neurons as the optimal number of neurons in the hidden layer. The honey samples with fructose concentrations of 0% (pure honey), 10%, 25%, 50%, 75% and 100% (pure fructose) were successfully classified, with correct classification rates of 100%, 83.3%, 80%, 87.5%, 100% and 100%, respectively. This reveals the acceptable performance of the designed device in detecting honey adulteration.

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.

Rapid, reliable and easy-to-perform chemometric-less method for rice syrup adulterated honey detection using FTIR-ATR

The adulteration of honey (Apis mellifera) is a global problem due to its economic, commercial and health implications. The world's leading beekeeping organisation, APIMONDIA, considers that the detection of adulteration in honey is a problem that has not yet been resolved. This evidence of the importance of the intensive development of analytical techniques that allow the unequivocal detection of adulterants in honey, especially those whose use as honey adulterants has recently emerged. This work aims to develop a fast, easy-to-perform, low-cost analytical method to qualitatively and quantitatively determine rice syrup using the Fourier transform infrared spectroscopy (FTIR) technique with attenuated total reflectance (ATR) mode without complex mathematical procedures and sophisticated sample preparation. This study involved the analysis of 256 intentionally rice-syrup-adulterated honey samples and 92 pure honey samples of bee multifloral honey from Spain. The method, based strictly on the determination of the absorbance directly from the samples, at 1013 cm−1 The methodology used no need for previous treatments or preparations and demonstrated the scope for the unequivocal detection of rice syrup in adulterated honey containing equal to or higher than 3% (m/m) or more of this adulterant. Using the Exponential Plus Linear model (r = 0.998) shows high accuracy and precision, in terms of relative error (0.32%, m/m) and coefficient of variation (1.4%). The results of this study have led to the establishment of a maximum absorbance threshold of 0.670 for honey without rice syrup.

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.

Hyperspectral imaging for non-destructive detection of honey adulteration

Honey adulteration causes serious economic losses for the industry, and it is difficult to detect various syrup adulteration. There is no doubt that the research on honey adulteration has scientific significance for maintaining the market order of honey and syrup and protecting the legitimate rights and interests of consumers. Hyperspectral images from pure and adulterated samples were captured using a hyperspectral imaging system (400–1000 nm). In this study, hyperspectral imaging and chemometrics were combined to detect honey adulteration, and the prediction model of honey adulteration detection was established. The pure nectar mixed 0 %, 5 %, 10 %, 15 %, 20 %, 30 % and 40 % of adulterants (fructose syrup and sucrose solution). By samples set partitioning based on joint X-Y distances (SPXY), the pure honey samples and the data fusion adulterated samples were assigned to the calibration set (560) and validation set (280) at the ratio of 2:1. The collected hyperspectral images were analyzed by principal component analysis (PCA) to preliminarily detect honey adulteration. Based on effective wavelengths, the adulterated sample analysis model of pure honey and adulterated honey was established. The results showed that the classification accuracy of LIBSVM model for honey adulteration was 92.5 %, which realized the detection of honey adulteration. Partial least square regression (PLSR) was used to establish adulteration level prediction model. The validation accuracy of this model was 0.84 and the root mean square error (RMSEV) of validation was 5.26 %. Therefore, it is feasible to detect honey adulteration by hyperspectral imaging. This method has the advantages of accuracy, simplicity, and greenness.

AUTENTIKASI CEPAT MADU HUTAN KALIMATAN TIMUR DENGAN ATR-FTIR SPEKTROSKOPI KOMBINASI ANALISIS KEMOMETRIKA

Honey adulteration is mostly conducted by the addition of sucrose. In this study, the authentication of honey was conducted using ATR-FTIR and chemometrics. Pure honey samples (MA) were collected from nine regions in East Kalimantan. The ATR-FTIR spectra of these samples were then compared to sucrose-adulterated honey (MS), which were prepared in the sucrose concentration from 2.5 to 50% (v / v).The data analysis was performed using chemometrics techniques: 1) Principle Component Analysis (PCA) method, 2) classification with Discriminant Analysis (DA), and 3) regression with (PCR) and (PLS). As a result, PCA was able to visualize the differences between MS and MA. DA analysis was able to distinguish MS and MA at wave numbers from 1200 to 800 cm-1 with 92.5% performance index. Quantitative calibration models of the sucrose-adulterated honey could be obtained from PLS and PCR, while the best calibration model was obtained with the PLS method from the 2nd derivative spectra. In summary, sucrose-adulterated honey from East Kalimantan can be authenticated using ATR-FTIR method in combination with chemometric analysis.

Detection of honey adulteration – The potential of UV-VIS and NIR spectroscopy coupled with multivariate analysis

Honey is usually adulterated by cheaper, commercially available sugar syrups with similar chemical composition. Adulterated honeys are frequently marked as natural and are valued in the same way as pure honeys and as a result, there is a growing need for rapid, simple, and accurate analytical methods to evaluate their characteristics and authenticity. In this work potential of UV-VIS and NIR spectroscopy coupled with PLS modelling and ANN modelling was analysed for the detection of adulteration and quantification of the physical and chemical proprieties of pure and adulterated honey samples. Acacia honey samples from Krapina-Zagorje County (Croatia) were adulterated by corn syrup (10–90%). The results indicate that PLS models developed in this work, except that for moisture content, can be used efficiently for screening (7<RER<20). ANN modelling provided accurate and simultaneous prediction of the properties of honey with correlation coefficients for validation above 0.8 so that these results show significant performance of ANNs modelling in the prediction of the physical and chemical properties of pure honey samples and adulterations based on combined UV-VIS NIR spectra.

Rheological determination of molecular weight of honey adulterated with high fructose corn syrup

Adulteration of honey represents an essential quality assurance concern in the utilisation of this natural product. Honey was serially diluted with high fructose corn syrup and analysed for its rheological profiles at 27 °C. Pure honey at ambient temperatures exhibits a peculiar non-Newtonian rheological behaviour while the behaviour of fructose syrup tends towards Newtonian. The adulteration of honey with this material drags its viscosity towards Newtonian flow behaviour. The results show that the Carreau-Yasuda model correlates the rheology of pure honey samples. Parameters of the Carreau-Yasuda model were observed to reflect the quality of the samples and thus aided their classification. Modified with the insertion of the Mark-Houwink’s relation, the Carreau-Yasuda model yielded average molecular weight data on the samples. The Structural Kinetic Model was utilised to give rheological structural information and a second independent assessment of average molecular weight from the rheograms. The average molecular weight of 202 g/mol was obtained using the Structural Kinetic model, while 205 g/mol was obtained using the amended Carreau-Yasuda model.

Comparative Study of the Nutritional Compositions of Pure and Adulterated Honey Samples Collected from Kano South Senatorial District of Kano State, Nigeria

Honey, the sweet, nutritious substance produced by honeybees, is of good nutritional and medicinal importance to consumers. The present study aimed at comparing the nutritional compositions of pure and adulterated honey samples collected directly from the honey beekeepers from Kano South Senatorial District of Kano State, Nigeria. For this purpose, a total of three samples each were randomly collected from three randomly selected local governments of the Senatorial District and analyzed for some nutritional values. Similarly, an adulterated honey sample was randomly collected from each of the three local governments. The mean nutritional values obtained for the pure sample were as follows: Moisture content (17.69-18.02%); crude fat (0.89-1.29%); crude protein (0.02-0.13%); ash content (0.44-0.51%); carbohydrate content (80.16-80.75%); energy values (1386.613-1396.157 KJ/100 g). While the adulterated honey sample recorded the following results: Moisture content (24.01%); crude fat (0.94%); crude protein (1.26%); ash content (2.64%); carbohydrate content (71.15%); energy values (1247.847 KJ/100 g). These results indicate that the pure honey sample analyzed are of good quality, as such could be used for both nutritional and medicinal purposes, having met the international honey standards. On the other hand, the adulterated honeys should always be used with care since they usually fall below the international honey standards.

Detection of adulteration in Chinese honey using NIR and ATR-FTIR spectral data fusion.

The aim of this study is to find a fast, accurate, and effective method for the detection of adulteration in honey circulating in the market. Near-infrared spectroscopy and mid-infrared spectroscopy data on natural honey and syrup-adulterated honey were integrated in the experiment. A method for identifying natural honey and syrup-adulterated honey was established by incorporating these data into a Support Vector Machine (SVM). In this experiment, 112 natural pure honey samples of 20 common honey types from 10 provinces in China were collected, and 112 adulterated honey samples with different percentages of syrup (10, 20, 30, 40, 50, and 60%) were prepared using six types of syrup commonly used in industry. The total number of samples was 224. The near- and mid-infrared spectral data were obtained for all samples. The raw spectra were pre-processed by First Derivative (FD) transform, Second Derivative (SD) transform, Multiple Scattering Correction (MSC), and Standard Normal Variate Transformation (SNVT). The above-corrected data underwent low-level and intermediate-level data fusion. In the last step, Grid Search (GS), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO) were employed as the optimization algorithms to find the optimal penalty parameter c and the optimal kernel parameter g for the SVM, and to establish the best SVM-based detection model for natural honey and syrup-adulterated honey. The results reveal that, compared to low-level data fusion, intermediate-level data fusion significantly improves the detection model. With the latter, the accuracy, sensitivity and specificity of the optimal SVM model all reach 100%, which makes it ideal for the identification of natural honey and syrup-adulterated honey.

Fast and robust quality assessment of honeys using near infrared spectroscopy

In this present study, near infrared spectroscopy (NIRS) is employed to detect adulteration and predict soluble solids content (SSC) of raw honey samples based on reflectance spectral data. Diffuse reflectance near infrared data were acquired in wavelength range from 1000 to 2500 nm. Adulterated honey, made by mixing pure honey with commercial sugars, was detected and classified using principal component analysis (PCA). On the other hand, SSC content as predicted from pure honey samples using partial least square regression (PLSR). Standard normal variate (SNV) was applied as spectra correction method. The results showed that PCA based on spectra data, can accurately detect adulteration and classify honey samples with total explained variance from 2 principal components is 97%. Moreover, SSC of pure honey samples can be predicted with achieved correlation coefficient (r) of 0.96 and residual predictive deviation (RPD) index of 2.88 for raw un-corrected spectra, while r = 0.98 and RPD = 3.67 for corrected SNV spectra data. It may conclude that near infrared spectroscopy, can be used as fast and robust method to evaluate quality of raw honey samples.

ZnCdSe-CdTe quantum dots: A "turn-off" fluorescent probe for the detection of multiple adulterants in an herbal honey.

To enhance the power of untargeted detection, a "turn-off" fluorescent probe with double quantum dots (QDs) was developed and coupled with chemometrics for rapid detection of multiple adulterants in an herbal (Rhus chinensis Mill., RCM) honey. The double water-soluble ZnCdSe-CdTe QDs have two separate and strong fluorescent peaks, which can be quenched by honey and extraneous adulterants with varying degrees. Class models of pure RCM honey samples collected from 6 different producing areas (n = 122) were developed using one-class partial least squares (OCPLS). Four extraneous adulterants, including glucose syrup, sucrose syrup, fructose syrup, and glucose-fructose syrup were added to pure honey samples at the levels of 0.5% to 10% (w/w). As a result, the OCPLS model using the second-order derivative (D2) spectra could detect 1.0% (w/w) of different syrups in RCM honey, with a sensitivity of 0.949. The double water-soluble QDs, which can be adjusted for analysis of other water-soluble food samples, has largely extended the capability of traditional fluorescence and will provide a potentially more sensitive and specific analysis method for food frauds.

A Screening Method Based on Headspace-Ion Mobility Spectrometry to Identify Adulterated Honey.

Nowadays, adulteration of honey is a frequent fraud that is sometimes motivated by the high price of this product in comparison with other sweeteners. Food adulteration is considered a deception to consumers that may have an important impact on people’s health. For this reason, it is important to develop fast, cheap, reliable and easy to use analytical methods for food control. In the present research, a novel method based on headspace-ion mobility spectrometry (HS-IMS) for the detection of adulterated honey by adding high fructose corn syrup (HFCS) has been developed. A Box–Behnken design combined with a response surface method have been used to optimize a procedure to detect adulterated honey. Intermediate precision and repeatability studies have been carried out and coefficients of variance of 4.90% and 4.27%, respectively, have been obtained. The developed method was then tested to detect adulterated honey. For that purpose, pure honey samples were adulterated with HFCS at different percentages (10–50%). Hierarchical cluster analysis (HCA) and principal component analysis (PCA) showed a tendency of the honey samples to be classified according to the level of adulteration. Nevertheless, a perfect classification was not achieved. On the contrary, a full classification (100%) of all the honey samples was performed by linear discriminant analysis (LDA). This is the first time the technique of HS-IMS has been applied for the determination of adulterated honey with HFCS in an automatic way.

The Effect of Sugar Beefeeding on the Concentrations of 5-Hydroxymethylfurfural in Syrian Honey

Samples of Syrian honey were collected directly from beekeepers from across the country. A total of sixteen samples were collected as follows: eight samples of natural nectar honey and eight samples of adulterated honey produced through sugar beefeeding for four months. The study is based on the measurement of 5-Hydroxymethylfurfural (HMF) concentrations in newly collected samples and after one year of storing in room temperature using a methodology based on HPLC with UV detector according to the European Honey Commission. The study showed that HMF concentrations were significantly higher in the adulterated honey samples produced through sugar beefeeding compared with pure honey samples, and after one year of storing in room temperature the concentrations of HMF increased dramatically in all samples and the increase in adulterated samples was significantly higher than that of pure samples. The concentration of newly collected pure samples ranged between (1.3-4.8) mg /kg and those of adulterated samples ranged between (6.6-20.2) mg /kg and after one year of storing the concentration of HMF of pure samples ranged between (7.7-20.3) mg /kg and those of adulterated samples ranged between (40.6-85.2)mg/kg and exceeded the international standards which allow an optimal specification of 40 mg per kg.

Rheological Modelling of the Effects of Sucrose Adulterant on Nigerian Honey

Rheology has been applied as sensitive tool for material characterisation since flow behaviour correlates closely with composition, molecular weight and molecular weight distribution. Pure honey was serially diluted with different percentages of sucrose: 10%, 50%, 70% and 90% and each were analysed rheologically at two different temperatures. Pure honey exhibits thixotropic time dependent rheological behaviour, while the behaviour of sucrose tends towards Newtonian. The addition of adulterant to honey drags the viscosity of the resulting fluid towards Newtonian. The rheological curve fitting was done using the Power Law and Carreau-Yasuda models. The results show that the Carreau-Yasuda model fitted better the rheology of pure honey samples. Conversely, the rheology of sucrose solution was predicted better with Power Law model.

Glass transition temperature of honey as a function of water content as determined by differential scanning calorimetry.

The glass transition of pure and diluted honey and the glass transition of the maximally freeze-concentrated solution of honey were investigated by differential scanning calorimetry (DSC). The glass transition temperature, of the pure honey samples accepted as unadulterated varied between -42 and -51 degrees C. Dilution of honey to 90 wt % honey content resulted in a shift of the glass transition temperature by -13 to -20 degrees C. The concentration of the maximally freeze-concentrated honey solutions, as expressed in terms of honey content is approximately 102-103%, i.e., slightly more concentrated in sugars than honey itself. The application of DSC measurements of and in characterization of honey may be considered, but requires systematic study on a number of honeys.