Classification of Italian honeys by 2D HR-NMR

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.

Impact of genus (Geotrigona, Melipona, Scaptotrigona) in the targeted 1H-NMR organic profile, and authenticity test by interphase emulsion of honey processed in cerumen pots by stingless bees in Ecuador

The biodiversity of Ecuadorian stingless bees is almost 200 species. Traditional pot-honey harvest in Ecuador is mostly done from nests of the three genera selected here Geotrigona Moure, 1943, Melipona Illiger, 1806, and Scaptotrigona Moure, 1942. The 20 pot-honey samples collected from cerumen pots and three ethnic honeys "abeja de tierra", "bermejo", and "cushillomishki" were analyzed for qualitative and quantitative targeted 1H-NMR honey profiling, and for the Honey Authenticity Test by Interphase Emulsion (HATIE). Extensive data of targeted organic compounds (41 parameters) were identified, quantified, and described. The three honey types were compared by ANOVA. Amino acids, ethanol, hydroxymethylfurfural, aliphatic organic acids, sugars, and markers of botanical origin. The number of phases observed with the HATIE were one in Scaptotrigona and three in Geotrigona and Melipona honeys. Acetic acid (19.60 ± 1.45 g/kg) and lactic acid (24.30 ± 1.65 g/kg) were particularly high in Geotrigona honey (in contrast to 1.3 g/kg acetic acid and 1.6 g/kg lactic acid in Melipona and Scaptotrigona), and with the lowest fructose + glucose (18.39 ± 1.68) g/100g honey compared to Melipona (52.87 ± 1.75) and Scaptotrigona (52.17 ± 0.60). Three local honeys were tested using PCA (Principal Component Analysis), two were assigned with a correct declared bee origin, but "bermejo" was not a Melipona and grouped with the Scaptotrigona cluster. However after HCA (Hierarchical Cluster Analysis) the three honeys were positioned in the Melipona-Scaptotrigona cluster. This research supports targeted 1H-NMR-based profiling of pot-honey metabolomics approach for multi-parameter visualization of organic compounds, as well as descriptive and pertained multivariate statistics (HCA and PCA) to discriminate the stingless bee genus in a set of Geotrigona, Melipona and Scaptotrigona honey types. The NMR characterization of Ecuadorian honey produced by stingless bees emphasizes the need for regulatory norms. A final note on stingless bee markers in pot-honey metabolites which should be screened for those that may extract phylogenetic signals from nutritional traits of honey. Scaptotrigona vitorum honey revealed biosurfactant activity in the HATIE, originating a fingerprint Honey Biosurfactant Test (HBT) for the genus in this set of pot-honeys.

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.

Classification of Polish Natural Bee Honeys Based on Their Chemical Composition

The targeted quantitative NMR (qNMR) approach is a powerful analytical tool, which can be applied to classify and/or determine the authenticity of honey samples. In our study, this technique was used to determine the chemical profiles of different types of Polish honey samples, featured by variable contents of main sugars, free amino acids, and 5-(hydroxymethyl)furfural. One-way analysis of variance (ANOVA) was performed on concentrations of selected compounds to determine significant differences in their levels between all types of honey. For pattern recognition, principal component analysis (PCA) was conducted and good separations between all honey samples were obtained. The results of present studies allow the differentiation of honey samples based on the content of sucrose, glucose, and fructose, as well as amino acids such as tyrosine, phenylalanine, proline, and alanine. Our results indicated that the combination of qNMR with chemometric analysis may serve as a supplementary tool in specifying honeys.

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.

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.

Bottom-up cubosome synthesis without organic solvents

HYPOTHESIS

Bottom-up synthesis of cubosomes is more energetically favourable than top-down approaches. However, bottom-up methods often rely on organic solvents such as ethanol as diluents, and lead to concurrent formation of liposomes. We propose using non-toxic diluents such as honey, glycerol and lactic acid for bottom-up cubosome synthesis.

EXPERIMENTS

Cubosomes were prepared using solutions of phytantriol in a range of diluents including choline chloride-glycerol, honey, lactic acid, glycerol, and ethanol. These solutions were added dropwise to water containing the stabiliser, poloxamer 407, following an established method of cubosome synthesis. The resulting structures were characterised using small-angle X-ray scattering, DLS and cryo-TEM.

FINDINGS

Cubosomes were successfully formed using a range of non-toxic diluents. This demonstrates that harmful organic solvents like ethanol are not required, and that the diluents need not be hydrotropes. Furthermore, unlike ethanol, these other diluents allowed formation of cubosomes without concurrent formation of liposomes. Given the huge potential for cubosomes in drug delivery, this new method offers a potentially useful low-cost, low-toxicity synthesis option.

A review on the application of spectroscopy to the condiments detection: from safety to authenticity

Condiments are the magical ingredients that make the food present a richer taste. In recent years, due to the increasing consciousness of food safety and human health, much progress has been made in developing rapid and nondestructive techniques for the evaluation of food condiments safety, authentication, and traceability. The potential of spectroscopy techniques, such as near-infrared (NIR), mid-infrared (MIR), Raman, fluorescence, inductively coupled plasma (ICP), and hyperspectral imaging techniques, has been widely enhanced by numerous applications in this field because of their advantages over other analytical techniques. Following a brief introduction of condiment and safety basics, this review mainly focuses on recent vibrational and atomic spectral applications for condiment nondestructive analysis and evaluation, including (1) chemical hazards detection; (2) microbiological hazards detection; and (3) authenticity concerns. The review shows current spectroscopies to be effective tools that will play indispensable roles for food condiment evaluation. In addition, online/real-time applications of these techniques promise to be a huge growth field in the near future.

Determination of the Geographical Origin of Maltese Honey Using 1H NMR Fingerprinting

The price of honey, as a highly consumed natural product, depends on its botanical source and its production environment, causing honey to be vulnerable to adulteration through mislabeling and inappropriate, fraudulent production. In this study, a fast and simple approach is proposed to tackle this issue through non-target one dimensional zg30 and noesypr1d 1H NMR fingerprint analysis, in combination with multivariate data analysis. Results suggest that composition differences in sugars, amino acids, and carboxylic acid were sufficient to discriminate between the tested honey of Maltese origin and that of non-local origin. Indeed, all chemometric models based on noesypr1d analysis of the whole fraction honey showed better prediction in geographical discrimination. The possibility of discrimination was further investigated through analysis of the honey's phenolic extract composition. The partial least squares models were deemed unsuccessful to discriminate, however, some of the linear discriminant analysis models achieved a prediction accuracy of 100%. Lastly, the best performing models of both the whole fraction and the phenolic extracts were tested on five samples of unknown geographic for market surveillance, which attained a high agreement within the models. Thus, suggesting the use of non-target 1H NMR coupled with the multivariate-data analysis and machine learning as a potential alternative to the current time-consuming analytical methods.

Eucalyptus honey: Quality parameters, chemical composition and health-promoting properties

Eucalyptus honey is an important unifloral honey commercialized worldwide and much desired by consumers due to the medicinal properties attributed to it because of the plant from which it is produced. In general, eucalyptus honey has been classified as being rich in pollen grains from the eucalyptus tree as well as having physicochemical characteristics that, in a way, have made it stand out from other honeys. Similar to other types of honey, eucalyptus honey can suffer contaminations and adulterations that compromise its quality, safety and authenticity. Thus, detailed knowledge of the composition and properties of this monofloral honeys is of great importance. With this background, the aim of this review is to present and discuss recent data regarding the physicochemical characteristics, chemical and health-promoting properties of eucalyptus honey as well as microbial contamination, authenticity, processing and adulteration.

Phytochemical profiles and classification of Agave syrups using 1H-NMR and chemometrics

BACKGROUND

Agave syrups are natural sweeteners that are highly desirable for human consumption because they have low glycemic index. In this work, we explored the potential of 1H-NMR-Chemometrics as a useful tool in the identification and differentiation of Agave syrups. Also, we evaluated the phytochemical screening and antioxidant capacity of Agave syrup compared to other natural sweeteners.

RESULTS

The phytochemical screening stands out for Agave syrups containing higher levels of metabolites with antioxidant activity, mainly saponins, glycosides, and terpenoids. Agave syrup antioxidant activity was in a range from 10% to 53%, while the total phenolic content was from 24 to 300 EAG/100 g, and condensed tannins were between 240 and 1,900 mg CE/g. Additionally, 1H-NMR spectroscopy was used to characterize syrup profiles and chemometrics. PCA group analyses allowed the sweeteners' classification by origin and kind of Agave.

CONCLUSION

Thus, we conclude that 1H-NMR and chemometrics can be used for identifying, differentiating, and classifying Agave syrups. Besides, Agave syrups contain significant amounts of antioxidative components and can be considered as an effective source of antioxidant.

Classification of Raw Stingless Bee Honeys by Bee Species Origins Using the NMR- and LC-MS-Based Metabolomics Approach

The official standard for quality control of honey is currently based on physicochemical properties. However, this method is time-consuming, cost intensive, and does not lead to information on the originality of honey. This study aims to classify raw stingless bee honeys by bee species origins as a potential classifier using the NMR-LCMS-based metabolomics approach. Raw stingless bee honeys were analysed and classified by bee species origins using proton nuclear magnetic resonance (¹H-NMR) spectroscopy and an ultra-high performance liquid chromatography-quadrupole time of flight mass spectrometry (UHPLC-QTOF MS) in combination with chemometrics tools. The honey samples were able to be classified into three different groups based on the bee species origins of Heterotrigona itama, Geniotrigona thoracica, and Tetrigona apicalis. d-Fructofuranose (H. itama honey), β-d-Glucose, d-Xylose, α-d-Glucose (G. thoracica honey), and l-Lactic acid, Acetic acid, l-Alanine (T. apicalis honey) ident d-Fructofuranose identified via ¹H-NMR data and the diagnostic ions of UHPLC-QTOF MS were characterized as the discriminant metabolites or putative chemical markers. It could be suggested that the quality of honey in terms of originality and purity can be rapidly determined using the classification technique by bee species origins via the ¹H-NMR- and UHPLC-QTOF MS-based metabolomics approach.

1H-NMR Profiling and Chemometric Analysis of Selected Honeys from South Africa, Zambia, and Slovakia

Honey is the natural sweet substance produced by honeybee from nectar or honeydew, exhibiting several nutritional and health benefits. It contains a complex mixture of compounds in different proportions, with sugars being the main component. The physicochemical characteristics of ten honeys were evaluated; represented by five, three, and two from South Africa, Slovakia, and Zambia, respectively. The range of values for the pH (3.75–4.38), electrical conductivity (99–659 µS/cm), and moisture content (14.2–17.7%) are within the recommended limits for quality honeys. ¹H-NMR (Nuclear Magnetic Resonance) profiling of the honeys in D₂O was determined, and the data were analysed by chemometrics. This method is fast, reproducible, and sample pre-treatment is not necessary. The ¹H-NMR fingerprints of various chemical shift regions showed similarity or dissimilarity across geographical origins that are useful for identification, detection of adulteration, and quality control. The principal component analysis PCA and partial linear square discriminant analysis PLS-DA of the ¹H-NMR profiles successively categorises the honeys into two chemically related groups. The R² values are higher than the corresponding Q² values for all samples, confirming the reliability of the model. Honeys in the same cluster contain similar metabolites and belong to the same botanic or floral origin.

Independent components analysis to increase efficiency of discriminant analysis methods (FDA and LDA): Application to NMR fingerprinting of wine

Discriminant analysis (DA) methods, such as linear discriminant analysis (LDA) or factorial discriminant analysis (FDA), are well-known chemometric approaches for solving classification problems in chemistry. In most applications, principle components analysis (PCA) is used as the first step to generate orthogonal eigenvectors and the corresponding sample scores are utilized to generate discriminant features for the discrimination. Independent components analysis (ICA) based on the minimization of mutual information can be used as an alternative to PCA as a preprocessing tool for LDA and FDA classification. To illustrate the performance of this ICA/DA methodology, four representative nuclear magnetic resonance (NMR) data sets of wine samples were used. The classification was performed regarding grape variety, year of vintage and geographical origin. The average increase for ICA/DA in comparison with PCA/DA in the percentage of correct classification varied between 6±1% and 8±2%. The maximum increase in classification efficiency of 11±2% was observed for discrimination of the year of vintage (ICA/FDA) and geographical origin (ICA/LDA). The procedure to determine the number of extracted features (PCs, ICs) for the optimum DA models was discussed. The use of independent components (ICs) instead of principle components (PCs) resulted in improved classification performance of DA methods. The ICA/LDA method is preferable to ICA/FDA for recognition tasks based on NMR spectroscopic measurements.

¹H and ¹³C NMR-based sugar profiling with chemometric analysis and antioxidant activity of herbhoneys and honeys

BACKGROUND

Herbhoneys, relatively new bee products, are expected to have interesting medicinal properties. However, there is still a lack of data concerning their composition and antioxidant properties. ¹H and ¹³C NMR spectroscopy coupled with chemometric analysis (PCA and PLS-DA) and antioxidant assays (DPPH-ESR and ORAC-FL) were used to study 25 samples of Polish herbhoneys and honeys.

RESULTS

Antioxidant activity varied among the samples. The best properties were exhibited by cocoa and instant coffee herbhoneys. The contents of total polyphenols and total carotenoids in the studied samples were found to be 70-1340 mg GAE kg⁻¹ and 0-28.05 mg kg⁻¹ respectively. No significant differences between herbhoney and honey samples were found in their sugar profiles. The PCA of ¹³C NMR spectra of the samples in DMSO-d6 resulted in sample clustering due to sucrose content.

CONCLUSION

Herbhoneys have similar antioxidant properties to traditional honeys, being therefore of equal nutritional value. There was a noticeable influence of the extract concentration on the observed antioxidant effect. For samples with high antioxidant activity, polyphenols were responsible for the observed effect. Sample clustering due to sucrose content in the NMR-PCA study allowed effortless detection of adulteration.

Qualitative and Quantitative Control of Honeys Using NMR Spectroscopy and Chemometrics

400 MHz nuclear magnetic resonance (NMR) spectroscopy and multivariate data analysis techniques were used in the context of food surveillance to measure 328 honey samples with 1H and 13C NMR. Using principal component analysis (PCA), clusters of honeys from the same botanical origin were observed. The chemical shifts of the principal monosaccharides (glucose and fructose) were found to be mostly responsible for this differentiation. Furthermore, soft independent modeling of class analogy (SIMCA) and partial least squares discriminant analysis (PLS-DA) could be used to automatically classify spectra according to their botanical origin with 95–100% accuracy. Direct quantification of 13 compounds (carbohydrates, aldehydes, aliphatic and aromatic acids) was additionally possible using external calibration curves and applying TSP as internal standard. Hence, NMR spectroscopy combined with chemometrics is an efficient tool for simultaneous identification of botanical origin and quantification of selected constituents of honeys.

High-performance liquid chromatographic phenolic compound fingerprint for authenticity assessment of honey

BACKGROUND

Phenolic compound profiles of 20 honeys of different botanical origin (eucalyptus, citrus, chestnut and linden) were obtained by high-performance liquid chromatography with ultraviolet detection after solid phase extraction, in order to evaluate the effectiveness of the fingerprint method for monofloral honey discrimination.

RESULTS

A total of 58 peaks were detected at λ = 280 nm. Distinctive phenolic compound profiles were obtained in which both the nature and the relative amount of the detected compounds were characteristic for different botanical source honeys. In order to detect sample groupings, chromatographic peak areas were submitted to principal component analysis. Then linear discriminant analysis was carried out on the first three principal components. In addition, linear discriminant analysis was carried out on the 58 variables, allowing the selection of five variables able to discriminate honeys of different botanical origin.

CONCLUSION

The chemometric evaluation of the phenolic compound profiles yielded classification models able to group honey samples according to their floral source with an excellent degree of agreement. The main advantage of the fingerprint approach with respect to traditional methods is that it does not require time-consuming identification and quantification of the analytes. The method proved to be effective for the assessment of honey authenticity.

Characterization of markers of botanical origin and other compounds extracted from unifloral honeys

The possibility of tracing the botanical and geographical origin of products such as honey has become more important because of market globalization. As a consequence, numerous analytical methods have been applied to the determination of honey authenticity. The scope of the present work is to chromatographically purify and characterize 23 compounds from organic extracts of unifloral (chestnut, linden, orange, acacia, eucalyptus, honeydew) and polyfloral honeys. Of these compounds, 17 were identified as specific markers and were used for botanical discrimination in a previous study based on multivariate statistical analysis of proton nuclear magnetic resonance ((1)H NMR) data. Together with the botanical markers, 6 other substances were isolated and characterized using NMR and mass spectrometry. These phytochemicals belong to several classes, that is, terpenes, organic acids, flavonoids, and others. For the first time, a diacylglyceryl ether and 5 other compounds present in different types of honey were identified and characterized.

The application of high resolution diffusion NMR to the analysis of manuka honey

The application of DOSY (Diffusion Ordered SpectroscopY) NMR as a technique for the virtual separation of key components of manuka honey and the implications for future discriminatory analysis of honey types is reported for the first time. The scope and the limitations of DOSY NMR are considered using the recently conceived DOSY Tool Box processing software and preliminary anti-bacterial data for the different honey types is reported.

¹H-NMR simultaneous identification of health-relevant compounds in propolis extracts

INTRODUCTION

Propolis is a resinous substance collected by bees from exudates of different plants that is rich in well-known health-relevant phenolic compounds such as flavonoids and phenolic acids. Propolis extracts are very complex matrices difficult to study. Different analytical methods are usable to analyse propolis extracts and to obtain chemical fingerprint but to our knowledge NMR has not previously been used for this purpose.

OBJECTIVE

This study aims to demonstrate that it is possible to use ¹H-NMR for the simultaneous recognition of phenolic compounds in complex matrices, such as propolis extracts, using appropriate tools for spectra pre-treatment and analysis.

METHODOLOGY

In this work 12 typical phenolic propolis compounds (apigenin, chrysin, galangin, kaempferol, quercetin, naringenin, pinocembrin, pinostrobin, caffeic acid, cinnamic acid, p-coumaric acid and ferulic acid) were considered as reference compounds and their presence in samples was verified by HPLC-MS. A simple ¹H-NMR sequence was used to obtain spectra of samples. Spectra were pre-treated by using an appropriate tool for spectra alignment and analysed by using software for the study of spectra originated from complex matrices. Sixty-five propolis samples were used to test the proposed identification procedure.

RESULTS

Ten out of 12 considered compounds were identified as statistically significant in most of the samples.

CONCLUSION

This work suggests that it is possible to efficiently use ¹H-NMR, coupled with appropriate spectral analytical tools, for the simultaneous detection of phenolic compounds in complex matrices.

A rapid differentiation between oak honeydew honey and nectar and other honeydew honeys by NMR spectroscopy

An NMR-based approach for rapid differentiation of oak honeydew honey from all other honey types (floral and other honeydew honeys) was proposed. It is based on the identification of the signals of the protons and the carbon of the methylene group of quercitol in the (1)H and (13)C NMR spectra of honey. The presence of quercitol was supported by TOCSY spectroscopy. Quercitol is a deoxyinositol which is regarded as a good taxonomic marker for the genus Quercus. All samples of oak honeydew honey contained quercitol, while in floral honey samples and honeydew honeys from fir and spruce it was absent. In addition, the described approach is promising with respect to quercitol quantification in honey by qNMR.

Characterization of the volatile profile of thistle honey using headspace solid-phase microextraction and gas chromatography-mass spectrometry

In this study, a headspace solid-phase microextraction method was developed for the characterization of the volatile fraction of thistle honey and compared with a dynamic headspace extraction method. A DVB/CAR/PDMS fibre was used. The effects of extraction time, equilibration time and salt addition on extraction yield were evaluated. The volatile fraction of seven Italian thistle honey samples was extracted under the optimized conditions and analyzed by gas chromatography-mass spectrometry. Characterization of the volatile profile was performed in terms of nature and relative amount of the extracted compounds. A total of 40 compounds, belonging to different chemical classes, were identified. The relative amounts of 16 compounds found in all the analyzed thistle honeys, i.e. nonanal, furfural, decanal, 3,6-dimethyl-2,3,3a,4,5,7a-hexahydrobenzofuran, benzaldehyde, α-linalool, lilac aldehyde (isomer IV), hotrienol, phenylacetaldehyde, 4-oxoisophorone, benzyl alcohol, 2-phenylethanol, a not identified compound, octanoic acid, nonanoic acid and methyl anthranilate, were calculated and submitted to statistical analysis, in order to define for each compound a typical range. On the basis of the obtained data, a characteristic set of values was defined for thistle honey volatile fingerprint. The developed model proved to be effective in recognizing the botanical origin of thistle honey.

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

The importance of honey adulteration detection has recently increased owing to the limited production levels in recent years and the relative high price of honey; therefore, this illegal practice has become more and more attractive to producers. Hence, the need has arisen for more effective analytical methods aiming at detecting honey adulteration. The present research presents an effective method to detect adulteration in honey falsified by intentional addition of different concentrations of commercial sugar syrups, using one-dimensional (1D) and two-dimensional (2D) nuclear magnetic resonance (NMR) coupled with multivariate statistical analysis. Sixty-three authentic and 63 adulterated honey samples were analyzed. To prepare adulterated honeys, seven different sugar syrups normally used for nutrition of bees were used. The best discriminant model was obtained by 1D spectra, and leave-one-out cross-validation showed a predictive capacity of 95.2%. 2D NMR also furnished acceptable results (cross-validation correct classification 90.5%), although the (1)H NMR sequence is preferable because it is the simplest and fastest NMR technique.

Nuclear magnetic resonance and chemometrics to assess geographical origin and quality of traditional food products

In this globalization era, the opening of the markets has put at almost everybody's disposal a wide variety of foods, allowing everybody to taste food flavors and aromas from different nations. Notwithstanding this opportunity, countries try to preserve their markets by developing protection policies. A few countries have adopted different denominations to label their "typical food" products in order to give them additional value. Besides, the term "typical food" is widely thought of as something anchored to the local traditions, with geographical meaning and made with typical raw materials. Then a "typical food" starts to be considered "traditional" when it is made following specific and old recipes. As a matter of fact, these products acquire particular organoleptic characteristics that are not reproducible when produced in different places. In this review, NMR studies coupled to multivariate statistical analysis are presented with the aim of determining geographical origin and key quality characteristics.

1H nuclear magnetic resonance spectra of chloroform extracts of honey for chemometric determination of its botanical origin

In this work, we present a new NMR study, coupled with chemometric analysis, on nonvolatile organic honey components. The extraction method is simple and reproducible. The 1H NMR spectra of chloroform extracts acquired with a fast and new pulse sequence were used to characterize and differentiate by chemometric analysis 118 honey samples of four different botanical origins (chestnut, acacia, linden, and polyfloral). The spectra collection, processing, and analysis require only 30 min. The 1H spectrum provides a fingerprint for each honey type, showing many characteristic peaks in all spectral regions. Principal component analysis (PCA) and projection to latent structures by partial least squares-discriminant analysis (PLS-DA) were performed on selected signals of the spectra to discriminate the different botanical types and to identify characteristic metabolites for each honey type. A distinct discrimination among samples was achieved. According to the distance to model criterion, there was no overlap between the four models, which proved to be specific for each honey type. The PLS-DA model obtained has a correlation coefficient R2 of 0.67 and a validation correlation coefficient Q2 of 0.77. The discriminant analysis allowed us to classify correctly 100% of the samples. A classification index can be calculated and used to determine the floral origin of honey as an alternative to the melissopalinology test and possibly to determine the percentage of various botanical species in polyfloral samples. Preliminary data on the identification of marker compounds for each botanical origin are presented.

Geographical characterization of polyfloral and acacia honeys by nuclear magnetic resonance and chemometrics

The importance of geographical origin determination is an increasing and pressing requirement for all foods. Honey is one of the largest studied foods due to its nutritional and medicinal properties in a correct diet. In this paper, a total of 41 honey samples (polyfloral and acacia) from different countries have been analyzed in terms of (1)H NMR spectroscopy coupled with multivariate statistical methods. Unsupervised principal component analysis resulted as an efficient tool in distinguishing (1)H NMR spectra of polyfloral and acacia honey samples and for geographical characterization of the latter ones. Hierarchical projection to latent structures discriminant analysis was successfully applied for the discrimination among polyfloral honey samples of different geographical origins. (13)C NMR spectroscopy was applied to honey samples with the aim to investigate possible sugar isoforms differentiation. Our preliminary data indicated a different isoforms ratio between betaFP and betaFF only for polyfloral Argentinean samples, while Hungarian samples showed resonance shifts for some carbons of alphaFF, betaFP, betaFF, and alphaGP isoforms for both varieties. These data confirmed the potentiality of (13)C spectroscopy in food characterization, especially in sugar-based foods.