Objective Definition of Monofloral and Polyfloral Honeys Based on NMR Metabolomic Profiling

Botanical honey recognition and quantitative mixture detection based on Raman spectroscopy and machine learning

The development of new approaches for honey recognition, based on spectroscopic techniques, presents a huge market potential especially because of the fast development of portable equipment. As an emerging approach, the association between Raman spectroscopy and Artificial Intelligence (i.e. Machine Learning algorithms) for food and beverages recognition starts to prove its efficiency, becoming an important candidate for the development of a practical application. Through this study, new recognition models for the rapid and efficient botanical differentiation of investigated honey varieties were developed, allowing the correct prediction of each type in a percentage better than 81%. The performances of the constructed models were expressed in terms of precision, sensitivity, and specificity. Moreover, through this approach, the detection of honey mixtures was possible to be made and an estimative percentage of the mixture components was obtained. Thus, the applicative potential of this new approach for honey recognition as well as a qualitative and quantitative estimation of the honey mixture was demonstrated.

Automatic nmr-based protocol for assessment of honey authenticity

¹H NMR analysis of organic extracts of honey is a powerful technique to confirm its botanical origin, thanks to the presence of signals that are specific to each floral typology. Similarly, signals from bee metabolites provide an important tool to verify honey entomological origin. Here, we present a method for honey screening that does not require any detailed analysis of the NMR spectrum for the detection and quantification of such markers. Our approach is based on the measurement of two spectral parameters, named entomological factor (EF) and aromatic factor (AF), calculated by integration of well-defined regions of the NMR spectrum. The values of EF and AF can reveal direct or indirect dilution of honey with sugar syrups. This method was tested on honeys of different floral origins and could identify all adulterated samples previously recognized by official techniques. Notably, several samples found compliant by official methods were proven non-genuine by the proposed approach.

NMR and HPLC profiling of bee pollen products from different countries

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Analysis using NMR and HPLC can be used to identify countries of origin but not producing years of bee pollens.

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Flavanoids in bee pollens from different countries were characterized using HPLC.

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Bee pollens from Spain and Australia were high in sucrose and adenosine.

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Bee pollens from China were high in trigonelline, uridine, and cytidine.

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Only the bee pollens from China contained acetic acid.

Bee pollen, a beehive product collected from flowers by honeybees, contains over 250 biological substances, and has attracted increasing attention as a functional food. However, commercial bee pollen products are often multifloral, and samples from different countries vary significantly. There is no universal standard for objective quality assessment of bee pollen based on its chemical composition. Here, we report metabolomic analysis of 11 bee pollen samples from Spain, China, and Australia for quality control. The characteristics of the samples depend on the sucrose, nucleoside, amino acid, and flavanol concentrations. Bee pollen samples from Spain and Australia had higher sucrose and adenosine concentrations, whereas those from China had higher trigonelline, uridine, and cytidine concentrations. Interestingly, acetic acid was only detected in samples from China. These components can be used to identify the country of origin. The obtained profiles of the samples will contribute to universal standard development for bee pollen products.

NMR-Based Approaches in the Study of Foods

In this review, the three different NMR-based approaches usually used to study foodstuffs are described, reporting specific examples. The first approach starts with the food of interest that can be investigated using different complementary NMR methodologies to obtain a comprehensive picture of food composition and structure; another approach starts with the specific problem related to a given food (frauds, safety, traceability, geographical and botanical origin, farming methods, food processing, maturation and ageing, etc.) that can be addressed by choosing the most suitable NMR methodology; finally, it is possible to start from a single NMR methodology, developing a broad range of applications to tackle common food-related challenges and different aspects related to foods.

Differentiation of Monofloral Honey Using Volatile Organic Compounds by HS-GCxIMS

Honey is a natural product and can be described by its botanical origin, determined by the plants from which the bees collect nectar. It significantly influences the taste of honey and is often used as a quality criterion. Unfortunately, this opens up the possibility of food fraud. Currently, various methods are used to check the authenticity of monofloral honey. The laborious, manual melissopalynology is considered an essential tool in the verification process. In this work, the volatile organic compounds obtained from the headspace of honey are used to prove their authenticity. The headspace of 58 honey samples was analyzed using a commercial easy-to-use gas chromatography-coupled ion mobility spectrometer with a headspace sampler (HS-GCxIMS). The honey samples were successfully differentiated by their six different botanical origins using specific markers with principal component analysis in combination with linear discriminant analysis. In addition, 15 honey-typical compounds were identified using measurements of reference compounds. Taking a previously published strategy, retention times of marker compounds were correlated with GC-coupled mass spectrometry (GC-MS) measurements to assist in the identification process.

Assessing food authenticity through protein and metabolic markers

This chapter aims to address an issue of ancient origins, but more and more topical in a globalized world in which consumers and stakeholders are increasingly aware: the authenticity of food. Foods are systems that can also be very complex, and verifying the correspondence between what is declared and the actual characteristics of the product is often a challenging issue. The complexity of the question we want to answer (is the food authentic?) means that the answer is equally articulated and makes use of many different analytical techniques. This chapter will consider the chemical analyses of foods aimed at guaranteeing their authenticity and will focus on frontier methods that have been developed in recent years to address the need to respond to ever-increasing guarantees of authenticity. Targeted and non-targeted approaches will be considered for verifying the authenticity of foods, through the study of different classes of constituents (proteins, metabolites, lipids, flavors). The numerous approaches available (proteomics, metabolomics, lipidomics) and the related analytical techniques (LC-MS, GC-MS, NMR) are first described from a more general point of view, after which their specific application for the purposes of authentication of food is addressed.

Analytical Strategies for LC-MS-Based Untargeted and Targeted Metabolomics Approaches Reveal the Entomological Origins of Honey

A comprehensive liquid chromatography-mass spectrometry (LC-MS)-based metabolomics approach was developed to discriminate honey harvested from Apis mellifera ligustica Spinola (A. mellifera) and Apis cerana cerana Fabricius (A. cerana). Based on an untargeted strategy, ultrahigh-performance liquid chromatography electrospray ionization quadrupole orbitrap high-resolution mass spectrometry (UPLC Q-Orbitrap) was combined with chemometrics techniques to screen and identify tentative markers from A. mellifera and A. cerana honey. In targeted metabolomics analysis, a sensitive method of solid-phase extraction followed by ultrahigh-performance liquid chromatography coupled with triple quadrupole tandem mass spectrometry (UPLC-MS/MS) was established for quantifying three markers, and the results showed that 3-amino-2-naphthoic acid and methyl indole-3-acetate could be considered markers of A. cerana honey, as they were present in higher amounts in A. cerana honey than in A. mellifera honey, whereas kynurenic acid was determined to be a marker of A. mellifera honey. This work highlights critical information for the authentication of A. cerana and A. mellifera honey.

MIR spectroscopy versus MALDI-ToF-MS for authenticity control of honeys from different botanical origins based on soft independent modelling by class analogy (SIMCA) - A clash of techniques?

In this study, highly reproducible MIR spectroscopy and highly sensitive MALDI-ToF-MS data were directly compared for the metabolomic profiling of monofloral and multifloral honey samples from three different botanical origins canola, acacia, and honeydew. Subsequently, three different classification models were applied to the data of both techniques, PCA-LDA, PCA- kNN, and soft independent modelling by class analogy (SIMCA) as class modelling technique. All monofloral external test set samples were classified correctly by PCA-LDA and SIMCA with both data sets, while multifloral test set samples could only be identified as outliers by the SIMCA technique, which is a crucial aspect in the authenticity control of honey. The comparison of the two used analytical techniques resulted in better overall classification results for the monofloral external test set samples with the MIR spectroscopic data. Additionally, clearly more multifloral external samples were identified as outliers by MIR spectroscopy (91.3%) as compared to MALDI-ToF-MS (78.3%). The results indicate that the high reproducibility of the used MIR technique leads to a generally better ability of separating monofloral honeys and in particular, identifying multifloral honeys. This demonstrates that benchtop-based techniques may operate on an eye-level with high-end laboratory-based equipment, when paired with an optimal data analysis strategy.

Non-Targeted Screening Approaches for Profiling of Volatile Organic Compounds Based on Gas Chromatography-Ion Mobility Spectroscopy (GC-IMS) and Machine Learning

Due to its high sensitivity and resolving power, gas chromatography-ion mobility spectrometry (GC-IMS) is a powerful technique for the separation and sensitive detection of volatile organic compounds. It is a robust and easy-to-handle technique, which has recently gained attention for non-targeted screening (NTS) approaches. In this article, the general working principles of GC-IMS are presented. Next, the workflow for NTS using GC-IMS is described, including data acquisition, data processing and model building, model interpretation and complementary data analysis. A detailed overview of recent studies for NTS using GC-IMS is included, including several examples which have demonstrated GC-IMS to be an effective technique for various classification and quantification tasks. Lastly, a comparison of targeted and non-targeted strategies using GC-IMS are provided, highlighting the potential of GC-IMS in combination with NTS.

Floral origin modulates the content of a lipid marker in Apis mellifera honey

The compound DAGE (DiAcyl Glyceryl Ether, 1-stearyl-2,3-dioleoyl glycerol), present in Apis mellifera honey, is a lipidic entomological marker secreted by the salivary glands of worker bees. Its content was determined by NMR, analyzing the organic extracts of a number of Italian honeys of different floral typology. We have found that the DAGE content is related to the botanical origin of honey. This dependence on floral typology was further confirmed by a linear correlation (R² > 0.83) observed between the content of DAGE and the enzymatic activity of invertase and diastase in honey. Also these enzymes originate from bee salivary secretions and their concentrations in honey are known to depend on the floral source. DAGE content appears to be a sensitive parameter to some forms of honey manipulations, as indicated by the results of artificial bee-feeding experiments. This suggests its possible use as indicator of honey authenticity.

Honey authenticity: analytical techniques, state of the art and challenges

Honey is a high-value, globally consumed, food product featuring a high market price strictly related to its origin. Moreover, honey origin has to be clearly stated on the label, and quality schemes are prescribed based on its geographical and botanical origin. Therefore, to enhance food quality, it is of utmost importance to develop analytical methods able to accurately and precisely discriminate honey origin. In this study, an all-time scientometric evaluation of the field is provided for the first time using a structured keyword on the Scopus database. The bibliometric analysis pinpoints that the botanical origin discrimination was the most studied authenticity issue, and chromatographic methods were the most frequently used for its assessment. Based on these results, we comprehensively reviewed analytical techniques that have been used in honey authenticity studies. Analytical breakthroughs and bottlenecks on methodologies to assess honey quality parameters using separation, bioanalytical, spectroscopic, elemental and isotopic techniques are presented. Emphasis is given to authenticity markers, and the necessity to apply chemometric tools to reveal them. Altogether, honey authenticity is an ever-growing field, and more advances are expected that will further secure honey quality.

Compositional identification and authentication of Chinese honeys by 1H NMR combined with multivariate analysis

Honey authentication has been becoming more and more important and necessary to the honey producers, the consumers and the market regulatory authority due to its favorite organoleptic and healthy properties, high value and increasing export but prevalent falsification practice for economic motivation in China and the potential health risk of adulterated honey. In this study, we obtained the spectral profiles of 90 authentic and 75 adulterated Chinese honey samples by means of high resolution nuclear magnetic resonance (NMR) spectroscopy, and 65 kinds of major and minor components in honey were identified and quantified from their NMR spectra. Combining with the multivariate statistical analyses including principal component analysis (PCA), linear discriminant analysis (LDA), and orthogonal partial least squared-discriminant analysis (OPLS-DA), the discrimination models were successfully established to identify the adulterated honeys from the authentic ones with an accurate rate of 97.6%. Furthermore, the corresponding volcano plot was used to screen out 8 components including proline, xylobiose, uridine, β-glucose, melezitose, turanose, lysine and an unknown component, which are responsible for the differentiation between the authentic and adulterated honeys and will help to control Chinese domestic honey market.

Ultra-Clean Pure Shift 1H-NMR applied to metabolomics profiling

Even though Pure Shift NMR methods have conveniently been used in the assessment of crowded spectra, they are not commonly applied to the analysis of metabolomics data. This paper exploits the recently published SAPPHIRE-PSYCHE methodology in the context of plant metabolome. We compare single pulse, PSYCHE, and SAPPHIRE-PSYCHE spectra obtained from aqueous extracts of Physalis peruviana fruits. STOCSY analysis with simplified SAPPHIRE-PSYCHE spectra of six types of Cape gooseberry was carried out and the results attained compared with classical STOCSY data. PLS coefficients analysis combined with 1D-STOCSY was performed in an effort to simplify biomarker identification. Several of the most compromised proton NMR signals associated with critical constituents of the plant mixture, such as amino acids, organic acids, and sugars, were more cleanly depicted and their inter and intra correlation better reveled by the Pure Shift methods. The simplified data allowed the identification of glutamic acid, a metabolite not observed in previous studies of Cape gooseberry due to heavy overlap of its NMR signals. Overall, the results attained indicated that Ultra-Clean Pure Shift spectra increase the performance of metabolomics data analysis such as STOCSY and multivariate coefficients analysis, and therefore represent a feasible and convenient additional tool available to metabolomics.

Chemical profiling and multivariate data fusion methods for the identification of the botanical origin of honey

The characterization of 72 Italian honey samples from 8 botanical varieties was carried out by a comprehensive approach exploiting data fusion of IR, NIR and Raman spectroscopies, Proton Transfer Reaction - Time of Flight - Mass Spectrometry (PTR-MS) and electronic nose. High-, mid- and low-level data fusion approaches were tested to verify if the combination of several analytical sources can improve the classification ability of honeys from different botanical origins. Classification was performed on the fused data by Partial Least Squares - Discriminant Analysis; a strict validation protocol was used to estimate the predictive performances of the models. The best results were obtained with high-level data fusion combining Raman and NIR spectroscopy and PTR-MS, with classification performances better than those obtained on single analytical sources (accuracy of 99% and 100% on test and training samples respectively). The combination of just three analytical sources assures a limited time of analysis.

Sensory and chemical profiles of Finnish honeys of different botanical origins and consumer preferences

The sensory-chemical profiles of Finnish honeys (labeled as buckwheat, cloudberry-bog, lingonberry, sweet clover, willowherb and multifloral honeys) were investigated using a multi-analytical approach. The sensory test (untrained panel, n = 62) was based on scaling and check-all-that-apply (CATA) methods accompanied with questions on preference and usage of honey. The results were correlated with corresponding profiles of odor-active compounds, determined using gas chromatography coupled with mass spectrometry/olfactometry (GC-MS/O). Botanical origins and chemical compositions including sugars were evaluated using NMR spectroscopy. A total of 73 odor-active compounds were listed based on GC-O. Sweet and mild honeys with familiar sensory properties were preferred by the panelists (PCA, R²X(1) = 0.7) while buckwheat and cloudberry-bog honeys with strong odor, flavor and color were regarded as unfamiliar and unpleasant. The data will give the honey industry novel information on honey properties in relation to the botanical origin, and consumer preference.