Detection of honey adulteration by sugar syrups using one-dimensional and two-dimensional high-resolution nuclear magnetic resonance

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

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

FTIR-ATR detection method for emerging C3-plants-derivated adulterants in honey: Beet, dates, and carob syrups

The European Union Publications Office has recently presented a report on the European Union's coordinated action with the Joint Research Centre to determine certain fraudulent practices in the honey sector, in which it has been indicated that 74% of the samples analyzed, imported from China, and 93% of the samples analyzed, imported from Turkey, the two largest honey producers worldwide, presented at least one indicator of exogenous sugar or suspicion of being adulterated. This situation has revealed the critical state of the problem of honey adulteration worldwide and the need to develop analytical techniques for its detection. Even though the adulteration of honey is carried out in a general way with sweetened syrups derived from C4 plants, recent studies have indicated the emerging use of syrups derived from C3 plants for the adulteration of honey. This kind of adulteration makes it impossible to analyze its detection using official analysis techniques. In this work, we have developed a fast, simple, and economical method based on the Fourier transform infrared spectroscopy technique, with attenuated total reflectance, for the qualitative, quantitative, and simultaneous determination of beetroot, date, and carob syrups, derived from of C3 plants; whose available bibliography is very scarce and analytically not very conclusive for its use by the authorities. The proposed method has been based on the establishment of the spectral differences between honey and the mentioned syrups at eight different points in the spectral region between 1200 and 900 cm^-1 of the mid-infrared, characteristic of the vibrational modes of carbohydrates in honey, which allows the pre-discrimination of the presence or absence of the syrups studied, and their subsequent quantification, with precision levels lower than 2.0% of the relative standard deviation and relative errors lower than 2.0% (m/m).

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.

Detection of honey adulteration using benchtop 1H NMR spectroscopy

High magnetic field NMR spectroscopy featuring the use of superconducting magnets is a powerful analytical technique for the detection of honey adulteration. Such high field NMR systems are, however, typically housed in specialised laboratories, require cryogenic coolants, and necessitate specialist training to operate. Benchtop NMR spectrometers featuring permanent magnets are, by comparison, significantly cheaper, more mobile and can be operated with minimal expertise. The lower magnetic fields used in such systems, however, result in limited spectral resolution, which diminishes their ability to perform quantitative composition analysis. These limitations may be overcome by implementing a recently developed field-invariant model-based fitting method which is defined by the underlying quantum mechanical properties of the nuclear spin system; this method is applied here to quantify the sugar composition of honey using benchtop ¹H NMR (43 MHz) spectroscopy. The detection of adulteration of 26 honey samples with brown rice syrup is quantitatively demonstrated to a minimum adulterant concentration of 5 wt%. Honey adulteration with corn syrup, glucose syrup and wheat syrup was also quantitatively detected using this approach. Our NMR detection of adulteration was shown to be invariant with time over 60 days of storage.

Multivariate Statistical Models for the Authentication of Traditional Balsamic Vinegar of Modena and Balsamic Vinegar of Modena on 1H-NMR Data: Comparison of Targeted and Untargeted Approaches

This work aimed to compare targeted and untargeted approaches based on NMR data for the construction of classification models for Traditional Balsamic Vinegar of Modena (TBVM) and Balsamic Vinegar of Modena (BVM). Their complexity in terms of composition makes the authentication of these products difficult, which requires the employment of several time-consuming analytical methods. Here, ¹H-NMR spectroscopy was selected as the analytical method for the analysis of TVBM and BVM due to its rapidity and efficacy in food authentication. ¹H-NMR spectra of old (>12 years) and extra-old (>25 years) TVBM and BVM (>60 days) and aged (>3 years) BVM were acquired, and targeted and untargeted approaches were used for building unsupervised and supervised multivariate statistical modes. Targeted and untargeted approaches were based on quantitative results of peculiar compounds present in vinegar obtained through qNMR, and all spectral variables, respectively. Several classification models were employed, and linear discriminant analysis (LDA) demonstrated sensitivity and specificity percentages higher than 85% for both approaches. The most important discriminating variables were glucose, fructose, and 5-hydroxymethylfurfural. The untargeted approach proved to be the most promising strategy for the construction of LDA models of authentication for TVBM and BVM due to its easier applicability, rapidity, and slightly higher predictive performance. The proposed method for authenticating TBVM and BVM could be employed by Italian producers for safeguarding their valuable products.

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.

Mechanisms and Health Aspects of Food Adulteration: A Comprehensive Review

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

Fourier transform infrared spectroscopy and chemometrics for the rapid screening of economically motivated adulteration of honey spiked with corn or rice syrup

Due to its high price, increased consumption, and limited production, honey has been a main target for economically motivated adulteration (EMA). An approach combining Fourier-Transform infrared spectroscopy (FTIR) and chemometrics was evaluated to develop a rapid screening tool to detect potential EMA of honey with either rice or corn syrup. A single-class soft independent modeling of class analogy (SIMCA) model was developed using a diverse set of commercial honey products and an authentic set of honey samples collected at four different U.S. Department of Agriculture (USDA) honey sample collection locations. The SIMCA model was externally validated with a set of calibration-independent authentic honey, typical commercial honey control samples, and those spiked with rice and corn syrups in the 1-16% concentration range. The authentic honey and typical commercial honey test samples were correctly predicted with an 88.3% classification rate. High accuracy was found in predicting the rice and corn syrup spiked samples above the 7% concentration range, yielding 97.6% and 94.8% correct classification rates, respectively. This study demonstrated the potential for a rapid and accurate infrared and chemometrics method that can be used to rapidly screen for either rice or corn adulterants in honey in less than 5 min.

A Comprehensive HPTLC-Based Analysis of the Impacts of Temperature on the Chemical Properties and Antioxidant Activity of Honey

Honeys are commonly subjected to a series of post-harvest processing steps, such as filtration and/or radiation treatment and heating to various temperatures, which might affect their physicochemical properties and bioactivity levels. Therefore, there is a need for robust quality control assessments after honey processing and storage to ensure that the exposure to higher temperatures, for example, does not compromise the honey's chemical composition and/or antioxidant activity. This paper describes a comprehensive short-term (48 h) and long-term (5 months) study of the effects of temperature (40 °C, 60 °C and 80 °C) on three commercial honeys (Manuka, Marri and Coastal Peppermint) and an artificial honey, using high-performance thin-layer chromatography (HPTLC) analysis. Samples were collected at baseline, at 6 h, 12 h, 24 h and 48 h, and then monthly for five months. Then, they were analysed for potential changes in their organic extract HPTLC fingerprints, in their HPTLC-DPPH total band activities, in their major sugar composition and in their hydroxymethylfurfural (HMF) content. It was found that, while all the assessed parameters changed over the monitoring period, changes were moderate at 40 °C but increased significantly with increasing temperature, especially the honeys' HPTLC-DPPH total band activity and HMF content.

Hyperspectral Microscopy Technology to Detect Syrups Adulteration of Endemic Guindo Santo and Quillay Honey Using Machine-Learning Tools

Honey adulteration is a common practice that affects food quality and sale prices, and certifying the origin of the honey using non-destructive methods is critical. Guindo Santo and Quillay are fundamental for the honey production of Biobío and the Ñuble region in Chile. Furthermore, Guindo Santo only exists in this area of the world. Therefore, certifying honey of this species is crucial for beekeeper communities-mostly natives-to give them advantages and competitiveness in the global market. To solve this necessity, we present a system for detecting adulterated endemic honey that combines different artificial intelligence networks with a confocal optical microscope and a tunable optical filter for hyperspectral data acquisition. Honey samples artificially adulterated with syrups at concentrations undetectable to the naked eye were used for validating different artificial intelligence models. Comparing Linear discriminant analysis (LDA), Support vector machine (SVM), and Neural Network (NN), we reach the best average accuracy value with SVM of 93% for all classes in both kinds of honey. We hope these results will be the starting point of a method for honey certification in Chile in an automated way and with high precision.

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,R_average²=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.

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.

Monofloral honey authentication by voltammetric electronic tongue: A comparison with 1H NMR spectroscopy

Proton-nuclear-magnetic-resonance-spectroscopy (¹H NMR) is the widely accepted reference method for monitoring honey adulteration; however, the need to find cheaper, faster, and more environmentally friendly methodologies makes the voltammetric-electronic-tongue (VET) a good alternative. The present study aims to demonstrate the ability of VET (in comparison with ¹H NMR) to predict the adulteration of honey with syrups. Samples of monofloral honeys (citrus, sunflower and heather, assessed by pollen analysis) simulating different levels of adulteration by adding syrups (barley, rice and corn) from 2.5 to 40% (w/w) were analyzed using both techniques. According to the indicators (slope, intercept, regression coefficient-R², root mean square error of prediction-RMSEP) of the partial-least-squares (PLS) regression models, in general terms, the performance of these models obtained by both techniques was good, with an average error lower than 5% in both cases. These results support the use of VET as a screening technique to easily detect honey adulteration with syrups.

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.

Study on stable carbon isotope fractionation of rape honey from rape flowers (Brassica napus L.) to its unifloral ripe honey

A new idea and strategy for honey traceability and identification was provided by studying the carbon isotope fractionation of rape honey and its components in the different ripening process, as well as the fractionation from rape flowers, stamens, nectar to rape honey. The results showed the moisture content of rape honey continued to decrease, and the glucose and fructose content continued to increase during the ripening process. The δ¹³C of rape honey and its protein were less affected by honey ripeness, while the δ¹³C of sugars in rape honey were greatly affected by this. At the same time, the fractionation of carbon isotope from rape flowers to honey was significant. The δ¹³C of rape honey and its protein, disaccharide, fructose, and glucose had a strong correlation, and the δ¹³C of rape honey and its components were mainly related to rape flowers and its stamens.

Identifying type of sugar adulterants in honey: Combined application of NMR spectroscopy and supervised machine learning classification

Nuclear magnetic resonance (NMR) is a powerful analytical tool which can be used for authenticating honey, at chemical constituent levels by enabling identification and quantification of the spectral patterns. However, it is still challenging, as it may be a person-centric analysis or a time-consuming process to analyze many honey samples in a limited time. Hence, automating the NMR spectral analysis of honey with the supervised machine learning models accelerates the analysis process and especially food chemistry researcher or food industry with non-NMR experts would benefit immensely from such advancements. Here, we have successfully demonstrated this technology by considering three major sugar adulterants, i.e., brown rice syrup, corn syrup, and jaggery syrup, in honey at varying concentrations. The necessary supervised machine learning classification analysis is performed by using logistic regression, deep learning-based neural network, and light gradient boosting machines schemes.

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NMR helps to identify the fingerprints of honey chemical constituents.

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Combined NMR and ML tools can determine the type of adulteration in honey.

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Supervised classification schemes, Logistic regression, DNN, and LGBM are utilized.

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Corn, brown rice, and jaggery adulterations are discriminated in honey.

Attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy coupled with chemometric analysis for detection and quantification of adulteration in lavender and citronella essential oils

INTRODUCTION

The growing consumer interest in "naturals" led to an increased application of essential oils (EOs). The market outbreak induced the intensification of EO adulterations, which could affect their quality.

OBJECTIVES

Nowadays, little is known about the illegal practice of adulteration of EOs with vegetable oils. Therefore, the application of mid-infrared spectroscopy coupled with chemometrics was proposed for the detection of EO counterfeits.

MATERIALS AND METHODS

Two EOs, three seed oils, and their mixtures were selected to build the adulteration model. EO-adulterant mixtures for model calibration and validation were analyzed by attenuated total reflectance-Fourier transform infrared (ATR-FTIR) spectroscopy. The spectral data were analyzed with principal component analysis (PCA) and partial least-squares (PLS) regression.

RESULTS

PCA allowed the discrimination of the EO and adulterant percentages by explaining 97.47% of the total spectral variance with two principal components. A PLS regression model was generated with three factors explaining 97.73% and 99.69% of the total variance in X and Y, respectively. The root mean square error of calibration and the root mean square error of cross-validation were 0.918 and 1.049, respectively. The root mean square error of prediction value obtained from the external validation set was 1.588 and the coefficients of determination R² _CAL and R² _CV were 0.997 and 0.996, respectively.

CONCLUSIONS

The results highlighted the robustness of the developed method in quantifying counterfeits in the range from 0 to 50% of adulterants, disregarding the type of EO and adulterant employed. The present work offers a research advance and makes an important impact in phytochemistry, revealing an easily applicable method for EO quality assessment.

Detection of syrup adulterants in manuka and jarrah honey using HPTLC-multivariate data analysis

High-Performance Thin-Layer Chromatography (HPTLC) was used in a chemometric investigation of the derived sugar and organic extract profiles of two different honeys (Manuka and Jarrah) with adulterants. Each honey was adulterated with one of six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrups) in five different concentrations (10%, 20%, 30%, 40%, and 50% w/w). The chemometric analysis was based on the combined sugar and organic extract profiles’ datasets. To obtain the respective sugar profiles, the amount of fructose, glucose, maltose, and sucrose present in the honey was quantified and for the organic extract profile, the honey’s dichloromethane extract was investigated at 254 and 366 nm, as well as at T (Transmittance) white light and at 366 nm after derivatisation. The presence of sugar syrups, even at a concentration of only 10%, significantly influenced the honeys’ sugar and organic extract profiles and multivariate data analysis of these profiles, in particular cluster analysis (CA), principal component analysis (PCA), principal component regression (PCR), partial least-squares regression (PLSR) and Machine Learning using an artificial neural network (ANN), were able to detect post-harvest syrup adulterations and to discriminate between neat and adulterated honey samples. Cluster analysis and principal component analysis, for instance, could easily differentiate between neat and adulterated honeys through the use of CA or PCA plots. In particular the presence of excess amounts of maltose and sucrose allowed for the detection of sugar adulterants and adulterated honeys by HPTLC-multivariate data analysis. Partial least-squares regression and artificial neural networking were employed, with augmented datasets, to develop optimal calibration for the adulterated honeys and to predict those accurately, which suggests a good predictive capacity of the developed model.

Cornflower Honey as a Model for Authentication of Unifloral Honey Using Classical Methods Combined with Plant-Based Marker Substances Such as Lumichrome

According to legislation, unifloral honeys are characterized by their organoleptic, physicochemical, and microscopic properties. Melissopalynology is the established method for identifying the pollen taken up with the floral nectar by forager bees and is used for authentication of the nectar sources in honey. For cornflower honey (Centaurea cyanus), the pollen input does not correlate with the nectar input, because the nectar is produced both in floral and in extrafloral nectaries. The well-known cornflower marker lumichrome has now also been detected in the extrafloral nectar. Therefore, lumichrome is a suitable marker substance for cornflower honey. Four different methods for the sole analysis of lumichrome in honey were validated and compared. Studies over nine years have shown that unifloral cornflower honey should contain approximately 35 mg/kg lumichrome. For a further differentiated cornflower honey specific verification, other nonvolatile compounds like 7-carboxylumichrome and volatiles, such as 3,4-dihydro-3-oxoedulan I and 3,4-dihydro-3-oxoedulan II, should be analyzed. This enables a more specific accuracy for the classification of unifloral cornflower honey.

A Novel qNMR Application for the Quantification of Vegetable Oils Used as Adulterants in Essential Oils

Essential oils (EOs) are more and more frequently adulterated due to their wide usage and large profit, for this reason accurate and precise authentication techniques are essential. This work aims at the application of qNMR as a versatile tool for the quantification of vegetable oils potentially usable as adulterants or diluents in EOs. This approach is based on the quantification of both 1H and 13C glycerol backbone signals, which are actually present in each vegetable oil containing triglycerides. For the validation, binary mixtures of rosemary EO and corn oil (0.8-50%) were prepared. To verify the general feasibility of this technique, other different mixtures including lavender, citronella, orange and peanut, almond, sunflower, and soy seed oils were analyzed. The results showed that the efficacy of this approach does not depend on the specific combination of EO and vegetable oil, ensuring its versatility. The method was able to determine the adulterant, with a mean accuracy of 91.81 and 89.77% for calculations made on 1H and 13C spectra, respectively. The high precision and accuracy here observed, make 1H-qNMR competitive with other well-established techniques. Considering the current importance of quality control of EOs to avoid fraudulent practices, this work can be considered pioneering and promising.

Novel Strategy for the Recognition of Adulterant Vegetable Oils in Essential Oils Commonly Used in Food Industries by Applying 13C NMR Spectroscopy

Essential oils (EOs) are valuable products commonly employed in the food industry and intensively studied as biopreservatives for the extension of food shelf-life. Unfortunately, EOs might be counterfeit to increase industrial profits. Among the possible adulterants, vegetable oils (VOs) must be considered for their characteristics and low costs. We aimed to apply nuclear magnetic resonance (NMR) spectroscopy for the detection and identification of VOs in mixtures with EOs. This innovative strategy is based on comparing the peak area ratio matrices of characteristic VO ¹³C NMR fatty acid signals with those of adulterated EOs. The identification of the VOs was achieved by calculating the matrix similarity at different confidence levels. The strategy demonstrated the capacity to efficiently recognize the presence of adulteration and the type of VO adulterant in mixtures. Thus, the method was applied to 20 commercial EOs, and VOs were detected and then identified in four samples.

Modeling of Antioxidant Activity, Polyphenols and Macronutrients Content of Bee Pollen Applying Solid-State 13C NMR Spectra

An application of solid ¹³C nuclear magnetic resonance (NMR) spectroscopy for the determination of macronutrients, total polyphenols content, antioxidant activity, N C S elements, and pH in commercially available bee pollens is reported herein. Solid-state ¹³C NMR spectra were recorded for homogenized pollen granules without chemical treatment or dissolution of samples. By combining spectral data with the results of reference analyses, partial least squares models were constructed and validated separately for each of the studied parameters. To characterize and compare the models’ quality, the relative standard errors of prediction (RSEP) were calculated for calibration and validation sets. In the case of the analysis of protein, fat and reducing sugars, these errors were in the 1.8–2.5% range. Modeling the elemental composition of bee pollen on the basis of ¹³C NMR spectra resulted in RSEP_cal/RSEP_val values of 0.3/0.6% for the sum of NHCS elements, 0.3/0.4% for C, 1.8/1.9% for N, and 4.2/6.1% for S quantification. Analyses of total phenolics and ABTS antioxidant activity resulted in RSEP values in the 2.7–3.5% and 2.8–3.8% ranges, respectively, whereas they were 1.4–2.1% for pH. The obtained results demonstrate the usefulness of ¹³C solid-state NMR spectroscopy for direct determination of various important physiochemical parameters of bee pollen.

Classification and Prediction of Bee Honey Indirect Adulteration Using Physiochemical Properties Coupled with K-Means Clustering and Simulated Annealing-Artificial Neural Networks (SA-ANNs)

The higher demand and limited availability of honey led to different forms of honey adulteration. Honey adulteration is either direct by addition of various syrups to natural honey or indirect by feeding honey bees with sugar syrups. Therefore, a need has emerged for reliable and cost-effective quality control methods to detect honey adulteration in order to ensure both safety and quality of honey. In this study, honey is adulterated by feeding honey bees with various proportions of sucrose syrup (0 to 100%). Various physiochemical properties of the adulterated honey are studied including sugar profile, pH, acidity, moisture, and color. The results showed that increasing sucrose syrup in the feed resulted in a decrease in glucose and fructose contents significantly, from 33.4 to 29.1% and 45.2 to 35.9%, respectively. Sucrose content, however, increased significantly from 0.19 to 1.8%. The pH value increased significantly from 3.04 to 4.63 with increase in sucrose feed. Acidity decreased slightly but nonsignificantly with increase in sucrose feed and varied between 7.0 and 4.00 meq/kg for 0% and 100% sucrose, respectively. Honey’s lightness (L value) also increased significantly from 59.3 to 68.84 as sucrose feed increased. Other color parameters were not significantly changed by sucrose feed. K-means clustering is used to classify the level of honey adulteration by using the above physiological properties. The classification results showed that both glucose content and total sugar content provided 100% accurate classification while pH values provided the worst results with 52% classification accuracy. To further predict the percent honey adulteration, simulated annealing coupled with artificial neural networks (SA-ANNs) was used with sugar profile as an input. RBF-ANN was found to provide the best prediction results with SSE = 0.073, RE = 0.021, and overall R2 = 0.992. It is concluded that honey sugar profile can provide an accurate and reliable tool for detecting indirect honey adulteration by sucrose solution.

Recent advances in analytical techniques for the detection of adulteration and authenticity of bee products - A review

Bee products have been considered as functional foods for a long time in China because of their wide range of biological activity. China has the largest number of bee colonies and the highest production of bee products in the world. Major bee products include honey, royal jelly, propolis and bee pollen. In recent years, consumption of bee products in China has been increasing due to an increased public awareness of their nutritional and health benefits. With the development of the Chinese economy and the improvement of people's living standards, high-end and gift-oriented products have become more popular and bee products are one of the options. However, the production of bee products cannot increase rapidly in short term and this is a driver for substantial economic-motivated adulteration. This is compounded by globalisation of supply chains which has also resulted in a rise in bee products fraud. These illicit products are eroding market prices and consumer trust, causing significant damage to the beekeeping industry. In order to provide information or solutions for regulators and consumers, in this article, we review he characteristics of bee products in China and the current situation regarding adulteration and authenticity of bee products. Moreover, advances in analytical techniques for detection of adulteration and authenticity of bee products including sensory techniques, DNA methods, isotope ratio mass spectrometry, spectroscopic techniques and mass spectrometry are reviewed. Finally, the applications and limitations of analytical methods in authentication are critically assessed. Suggestions are also put forward for the future management of China's bee products industry.

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.

A comprehensive analysis of 13C isotope ratios data of authentic honey types produced in China using the EA-IRMS and LC-IRMS

In the current study, we have comprehensively analyzed different kinds of pure honey which was produced in various areas in China according to δ13C-EA -IRMS (AOAC method 998.12) and δ13C-LC-IRMS (proposed by the Intertek laboratory in Europe) methods. As for the δ13C-EA -IRMS method, the study was confirmed that the C4 sugar of all authentic honey samples was qualified. Further inter-laboratory comparison experiments using the δ13C-LC-IRMS method found that all authentic honey samples had Δδ13C (‰) values within the naturally occurring range of ± 1‰ for Δδ13C (‰) fru-glu. However, about 70% samples had Δδ13C (‰) values outside the range of ± 2.1‰ for Δδ13C (‰) max., indicating that a large proportion of pure honey in China can't pass the δ13C-LC-IRMS test, although these honeys were extracted from unadulterated sources. Based on the present findings, we consider that the δ13C-LC-IRMS method is not appropriate to reliably detect adulterated honeys with C3 sugars in China.

Two-dimensional subband Steiglitz-McBride algorithm for automatic analysis of two-dimensional nuclear magnetic resonance data

Rapid, accurate, and automatic quantitation of two-dimensional nuclear magnetic resonance(2D-NMR) data is a challenging problem. Recently, a Bayesian information criterion based subband Steiglitz-McBride algorithm has been shown to exhibit superior performance on all three fronts when applied to the quantitation of one-dimensional NMR free induction decay data. In this paper, we demonstrate that the 2D Steiglitz-McBride algorithm, in conjunction with 2D subband decomposition and the 2D Bayesian information criterion, also achieves excellent results for 2D-NMR data in terms of speed, accuracy, and automation-especially when compared in these respects to the previously published analysis techniques for 2D-NMR data.

NMR carbohydrate profile in tracing acacia honey authenticity

The sugar profile in honey can be used as a fingerprint to confirm the authenticity or reveal the adulteration of the product by sweetener addition. In this work, we have accurately determined the profile of 20 minor saccharides in a set of 46 European acacia honeys using a recently proposed NMR approach based on the CSSF-TOCSY experiment. Comparison of this reference profile with the sugar composition of several Chinese honey samples of the same declared botanical origin has revealed important differences. A detailed analysis of the saccharide profile of these Chinese honeys suggests product adulteration by overfeeding bee colonies with industrial sugars syrups during the main nectar flow period.

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.

Establishing authenticity of honey via comprehensive Romanian honey analysis

Assessing the authenticity of honey is a serious problem that has gained much interest internationally because honey has frequently been subject to various fraudulent practices, including mislabelling of botanical and geographical origin and mixing with sugar syrups or honey of lower quality. To protect the health of consumers and avoid competition, which could create an unstable market, consumers, beekeepers and regulatory bodies are interested in having reliable analytical methodologies to detect non-compliant honey. This paper gives an overview of the different approaches used to assess the authenticity of honey, specifically by the application of advanced instrumental techniques, including spectrometric, spectroscopic and chromatographic methods coupled with chemometric interpretation of the data. Recent development in honey analysis and application of the honey authentication process in the Romanian context are highlighted, and future trends in the process of detecting and eliminating fraudulent practices in honey production are discussed.

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.

Novel polyclonal antibody-based rapid gold sandwich immunochromatographic strip for detecting the major royal jelly protein 1 (MRJP1) in honey

Honey adulteration is becoming increasingly alarming incidents in food safety. Monitoring and detecting adulteration face greater challenges. Honey contains the major royal jelly proteins (MRJP) secreted by bee workers. To detect honey adulteration fast and accurately, a rapid gold sandwich immunochromatographic strip (GSIS) was developed based on two specific polyclonal antibodies (PoAbs) against the MRJP1, the most abundant protein of all MRJPs. We determined the best of pH value (pH 8.6) and PoAb SP-1 amount (5 μg/mL) in conjunction with colloidal. The cut-off value (sensitivity) of GSIS in detecting MRJP1 is 2.0 μg/mL in solution. Validation analysis with RJ, milk vetch honey, acacia honey and honey adulteration containing rice syrup and corn syrup with different ratios demonstrated that the GSIS could show consistent Test line (T line) when the test samples contain more than 30% pure honey or MRJP1 0.4 mg/g. The validation results by isotope ratio mass spectrometry on the same pure and all adulteration milk vetch honey samples showed the same information of GSIS test. The qualitative assay GSIS provided a valuable new way for honey authenticity and laid the foundation for the future application of GSIS with monoclonal antibodies in honey authentication.