An investigative study on discrimination of honey of various floral and geographical origins using UPLC-QToF MS and multivariate data analysis

Rapid Convolutional Algorithm for the Discovery of Blueberry Honey Authenticity Markers via Nontargeted LC-MS Analysis

Bees produce honey through the collection and transformation of nectar, whose botanical origin impacts the taste, nutritional value, and, therefore, the market price of the resulting honey. This phenomenon has led some to mislabel their honey so that it can be sold at a higher price. Metabolomics has been gaining popularity in food authentication, but rapid data mining algorithms are needed to facilitate the discovery of new authenticity markers. A nontargeted high-resolution liquid chromatography-mass spectrometry (HR/LC-MS) analysis of 262 monofloral honey samples, of which 50 were blueberry honey, was performed. Data mining methods were demonstrated for the discovery of binary single-markers (compound was only detected in blueberry honey), threshold single-markers (compound had the highest concentration in blueberry honey), and interval ratio-markers (the ratio of two compounds was within a unique interval in blueberry honey). A novel convolutional algorithm was developed for the discovery of interval ratio-markers, which trained 14× faster and achieved a 0.2 Matthews correlation coefficient (MCC) units higher classification score than existing open-source implementations. The convolutional algorithm also had classification performance similar to that of a brute-force search but trained 1521× faster. A pipeline for shortlisting candidate authenticity markers from the LC-MS spectra that may be suitable for chemical structure identification was also demonstrated and led to the identification of niacin as a blueberry honey threshold single-marker. This work demonstrates an end-to-end approach to mine the honey metabolome for novel authenticity markers and can readily be applied to other types of food and analytical chemistry instruments.

Changes in health-promoting metabolites associated with high-altitude adaptation in honey

The levels of metabolites in honey are influenced by floral origin, production region, and bee species. However, how environmental factors affect honey quality remains unclear. Based on untargeted metabolomics and using UPLC Q-Orbitrap MS, we analyzed 3596 metabolites in 51 honey samples from Yunnan and Shennongjia. Comparative analysis revealed that geniposidic acid, kynurenic acid and caffieine accumulated at significantly different levels between Shennongjia and Yunnan honey. Based on cluster structure analysis, 36 Yunnan honey samples were divided into two distinct groups by altitude. Notably, quercetin, hyperoside, taxifolin, rutin, tryptophan, astragalin and phenylalanine were higher levels in high-altitude honey (>1700 m), whereas abscisic acid was higher levels in low-altitude honey (≤1700 m). Among these, significantly elevated levels of hyperoside, taxfolin, astragalin, and tryptophan were observed in honey collected from high-altitude areas in Shennongjia. Our findings highlight the effect of altitude on honey health-promoting components, providing valuable insights into honey quality.

Determination of adulteration, geographical origins, and species of food by mass spectrometry

Food adulteration, mislabeling, and fraud, are rising global issues. Therefore, a number of precise and reliable analytical instruments and approaches have been proposed to ensure the authenticity and accurate labeling of food and food products by confirming that the constituents of foodstuffs are of the kind and quality claimed by the seller and manufacturer. Traditional techniques (e.g., genomics-based methods) are still in use; however, emerging approaches like mass spectrometry (MS)-based technologies are being actively developed to supplement or supersede current methods for authentication of a variety of food commodities and products. This review provides a critical assessment of recent advances in food authentication, including MS-based metabolomics, proteomics and other approaches.

Rapid Identification of Corn Sugar Syrup Adulteration in Wolfberry Honey Based on Fluorescence Spectroscopy Coupled with Chemometrics

Honey adulteration has become a prominent issue in the honey market. Herein, we used the fluorescence spectroscopy combined with chemometrics to explore a simple, fast, and non-destructive method to detect wolfberry honey adulteration. The main parameters such as the maximum fluorescence intensity, peak positions, and fluorescence lifetime were analyzed and depicted with a principal component analysis (PCA). We demonstrated that the peak position of the wolfberry honey was relatively fixed at 342 nm compared with those of the multifloral honey. The fluorescence intensity decreased and the peak position redshifted with an increase in the syrup concentration (10-100%). The three-dimensional (3D) spectra and fluorescence lifetime fitting plots could obviously distinguish the honey from syrups. It was difficult to distinguish the wolfberry honey from another monofloral honey, acacia honey, using fluorescence spectra, but it could easily be distinguished when the fluorescence data were combined with a PCA. In all, fluorescence spectroscopy coupled with a PCA could easily distinguish wolfberry honey adulteration with syrups or other monofloral honeys. The method was simple, fast, and non-destructive, with a significant potential for the detection of honey adulteration.

Identification of novel unique mānuka honey markers using high-resolution mass spectrometry-based metabolomics

Mānuka honey is a valuable commodity produced by bees foraging the flowers of Leptospermum scoparium, a bush native to New Zealand and Australia. Due to its high value and proven health benefits, authenticity fraud in the sale of this food is a significant risk, as recounted in the literature. Four compulsory natural products must be present at minimum concentrations to authenticate mānuka honey (3-phenyllactic acid, 2'-methoxyacetophenone, 2-methoxybenzoic acid, and 4-hydroxyphenyllactic acid). However, spiking other kinds of honey with these compounds and/or the dilution of mānuka honey with other varieties may result in fraud going undetected. In this work, liquid chromatography coupled with high-resolution mass spectrometry and a metabolomics-based strategy has allowed us to tentatively identify 19 natural products -putative mānuka honey markers-, nine of which are reported for the first time. Chemometric models applied to these markers allowed the detection of both spiking and dilution fraud attempts of mānuka honey, even at 75% mānuka honey purity. Thus, the herein-reported methodology can be employed in the prevention and detection of mānuka honey adulteration even at low levels, and the tentatively identified markers presented in this work proved valuable for mānuka honey authentication procedures.

Evaluation of the Antioxidant Activities and Phenolic Profile of Shennongjia Apis cerana Honey through a Comparison with Apis mellifera Honey in China

This study evaluates the phenolic profile as well as the antioxidant properties of Shennongjia Apis cerana honey through a comparison with Apis mellifera honey in China. The total phenolic content (TPC) ranges from 263 ± 2 to 681 ± 36 mg gallic acid/kg. The total flavonoids content (TFC) ranges from 35.9 ± 0.4 to 102.2 ± 0.8 mg epicatechin/kg. The correlations between TPC or TFC and the antioxidant results (FRAP, DPPH, and ABTS) were found to be statistically significant (p < 0.01). Furthermore, the phenolic compounds are quantified and qualified by high performance liquid chromatography-high resolution mass spectrometry (HPLC-HRMS), and a total of 83 phenolic compounds were tentatively identified in this study. A metabolomics analysis based on the 83 polyphenols was carried out and subjected to principal component analysis and orthogonal partial least squares-discriminant analysis. The results showed that it was possible to distinguish Apis cerana honey from Apis mellifera honey based on the phenolic profile.

A non-targeted metabolomic strategy for characterization of the botanical origin of honey samples using headspace gas chromatography-ion mobility spectrometry

In this work, characterization of the botanical origin of honey was carried out using headspace gas chromatography coupled to ion mobility spectrometry (HS-GC-IMS). The proposed methodology was applied for the analysis of 89 samples from ten different botanical origins. A total of 15 volatile compounds could be identified, namely, 3-methyl-1-butanol, heptanal, valeraldehyde, octanal, trans-2-hexenal, nonanal, hexanal, benzaldehyde, 2-heptanone, 2-butanone, 2-hexanone, 6-methyl-5-hepten-2-one, 2-pentanone, ethyl acetate and linalool. The analytical method was characterized in terms of limits of detection and quantification, and precision, in order to quantify the identified compounds. Compounds were quantified using the sum of the protonated monomer and proton-bound dimer and logarithmic regression (R² > 0.98), although the establishment of a concentration threshold that would allow creation of classification rules was not possible since there was variability within the group. Consequently, the establishment of chemometric models was necessary. A non-targeted strategy using 275 features is proposed. Orthogonal partial least squares-discriminant analysis (OPLS-DA) allowed the differentiation of five botanical origins: thousand flowers, rosemary, albaida, orange blossom, and "others" (rest of the investigated botanical origins, since a limited number of samples was available). A success validation rate of 100% allowed the classification of 14 honeys with unknown botanical origin.

Metabolic profiling on the analysis of different parts of Schisandra chinensis based on UPLC-QTOF-MS with comparative bioactivity assays

The Schisandra chinensis is an important edible plant, and previous phytochemical research focused on the S. chinensis fruit (SF) due to its long history as traditional Chinese medicine. Schisandra chinensis fruit was used as an astringent tonic to astringe the lungs and the kidneys, replenish energy, promote the production of body fluids, tonify the kidney, and induce sedation. The components of S. chinensis, such as its stems (SS), leaves (SL), and roots (SR), have drawn little attention regarding their metabolites and bioactivities. In this study, a strategy of combining a chemical database with the Progenesis QI informatics platform was applied to characterize the metabolites. A total of 332 compounds were tentatively identified, including lignans, triterpenoids, flavonoids, tannins, and other compound classes. Heatmap and principal component analysis (PCA) showed remarkable differences in different parts of the plants. By multiple orthogonal partial least-squares discriminant analyses (OPLS-DA), 76 compounds were identified as potential marker compounds that differentiate these different plant parts. Based on the variable influence on the projection score from OPLS-DA, the active substances including gomisin D, schisandrol B, schisantherin C, kadsuranin, and kadlongilactone F supported the fact that the biological activity of the roots was higher than that of the fruit. These substances can be used as marker compounds in the plant roots, which likely contribute to their antioxidant and anti-inflammatory activities. The plant roots could be a new medicinal source that exhibits better activity than that of traditional medicinal parts, which makes them worth exploring.

Integration of lipidomics and metabolomics for the authentication of camellia oil by ultra-performance liquid chromatography quadrupole time-of-flight mass spectrometry coupled with chemometrics

The integration of lipidomics and metabolomics approaches, based on UPLC-QTOF-MS technology coupled with chemometrics, was established to authenticate camellia oil adulterated with rapeseed oil, peanut oil, and soybean oil. Lipidomics revealed that the glyceride profile provides a prospective authentication of camellia oil, but no characteristic markers were available. Sixteen characteristic markers were identified by metabolomics. For camellia oil, all six markers were sapogenins of oleanane-type triterpene saponins. Lariciresinol, sinapic acid, doxercalciferol, and an unknown compound were identified as markers for rapeseed oil. Characteristic markers in peanut oil were formononetin, sativanone, and medicarpin. In the case of soybean oil, the characteristic markers were dimethoxyflavone, daidzein, and genistein. The established OPLS-DA and OPLS prediction models were highly accurate in the qualitative and quantitative analyses of camellia oil adulterated with 5% other oils. These results indicate that the integration of lipidomics and metabolomics approaches has great potential for the authentication of edible oils.

Honey authenticity: the opacity of analytical reports - part 1 defining the problem

The composition of honey, a complex natural product, challenges analytical methods attempting to determine its authenticity particularly in the face of sophisticated adulteration. Of the advanced analytical techniques available, only isotope ratio mass spectrometry (IRMS) is generally accepted for its reproducibility and ability to detect certain added sugars, with nuclear magnetic resonance (NMR) and high-resolution mass spectrometry (HRMS) being subject to stakeholder differences of opinion. Herein, recent reviews of honey adulteration and the techniques to detect it are summarised in the light of which analytical reports are examined that underpinned a media article in late 2020 alleging foreign sugars in UK retailers' own brand honeys. The requirement for multiple analytical techniques leads to complex reports from which it is difficult to draw an overarching and unequivocal authenticity opinion. Thus arose two questions. (1) Is it acceptable to report an adverse interpretation without exhibiting all the supporting data? (2) How may a valid overarching authenticity opinion be derived from a large partially conflicting dataset?

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.

Phytochemicals and Volatiles in Developing Pelargonium ‘Endsleigh’ Flowers

Rose-scented geranium (Pelargonium sp.) have been the subject of many studies concerning essential oil, unlike the edible flowers of scent Pelargonium despite the beneficial properties of chemical compounds present in such organ and the fragrances produced. Therefore, we investigated organic compounds, total phenols and the antioxidant activity of the flowers with particular attention to the volatile compounds emitted by flowers of Pelargonium ‘Endsleigh’ at three stages of development. We identified several major phenols and organic compounds that revealed significative difference compared with other Pelargonium flowers, and employing the solid-phase micro extraction (SPME) methodology we determined that the production of volatile compounds decreases during the flower development. In addition, the emission of volatiles is accomplished mainly by the sepals. In conclusion, to use Pelargonium ‘Endsleigh’ flowers, it is important in ensuring that they have not yet opened and that the sepals are in place.

Optical spectroscopy methods combined with multivariate statistical analysis for the classification of Cretan thyme, multi-floral and honeydew honey

BACKGROUND

The botanical origin of honey attracts both commercial and research interest. Consumers' preferences and medicinal uses of particular honey types drive the demand for the determination of their authenticity with regard to their botanical origin. This study presents the discrimination of thyme, multi-floral. and honeydew honeys by Fourier-transform infrared (FTIR) and ultraviolet (UV) absorption spectroscopy combined with multivariate statistical analysis. UV absorption spectroscopy was applied without any dilution of the sample using a custom-made cuvette. FTIR and UV absorption spectroscopic data were processed by means of the orthogonal partial least squares discriminant analysis.

RESULTS

The optimal classification of floral and honeydew honeys was accomplished with UV spectroscopy with a successful estimation of 92.65% for floral honey and 91.30% for honeydew honey. The discrimination of thyme versus the multi-floral honey was best achieved with FTIR, with a correct classification of 95.56% and 100% for multi-floral and thyme honey respectively. Furthermore, our findings revealed the region of 2400-4000 cm^-1 of the FTIR spectra as the most significant for this discrimination.

CONCLUSION

This work demonstrates that optical spectroscopic techniques in combination with multivariate statistical analysis can be a rapid, low-cost, easy-to-use approach for the determination of the botanical origin of honey without sample pretreatment. © 2021 Society of Chemical Industry.

[Analysis of differences between unifloral honeys from different botanical origins based on non-targeted metabolomics by ultra-high performance liquid chromatography-quadrupole time-of-flight mass spectrometry]

不同的蜜源植物具有结构多样的次生代谢产物。该研究以8种不同蜜源单花蜜(洋槐蜜、枣花蜜、荆条蜜、椴树蜜、荞麦蜜、麦卢卡蜜、枸杞蜜、益母草蜜)为研究对象,建立了基于超高效液相色谱-四极杆飞行时间质谱技术(UPLC-Q-TOF-MS^E)的非靶向代谢组学方法,考察了不同蜜源中次生代谢产物的差异。该研究采用固相萃取前处理方法和UPLC-Q-TOF-MS^E方法,获得不同蜜源单花蜜的植物代谢组信息,并构建了多变量统计分析模型,对不同来源的单花蜜进行模式识别和差异分析,发现洋槐蜜、枣花蜜、荆条蜜、椴树蜜、荞麦蜜、麦卢卡蜜相互间存在不同程度的显著差异。结合模型的变量重要性投影、方差分析与最大差异倍数值,根据精确前体离子和碎片离子质量信息检索Chemspider、HMDB数据库,该研究筛选并鉴定出32个代谢差异化合物,其中黄酮类化合物18个、酚酸类化合物7个、苯苷与萜苷类化合物6个、甾体类化合物1个;研究发现麦卢卡蜜和荞麦蜜以黄酮类化合物为主要差异代谢物,荆条蜜中酚酸类化合物为特征性表达,苯苷与萜苷类化合物主要为椴树蜜的特征代谢物。该研究从植物代谢组学角度初步揭示了不同单花蜜的代谢产物差异性以及特征化合物,为基于化学分析技术的蜂蜜溯源识别与质量评价提供了有效的研究策略。

Chemical Analysis Combined with Multivariate Statistical Methods to Determine the Geographical Origin of Milk from Four Regions in China

Traceability of milk origin in China is conducive to the implementation of the protection of regional products. In order to distinguish milk from different geographical distances in China, we traced the milk of eight farms in four neighboring provinces of China (Inner Mongolia autonomous region, Hebei, Ningxia Hui autonomous and Shaanxi), and multivariate data analysis was applied to the data including elemental analysis, stable isotope analysis and fatty acid analysis. In addition, orthogonal partial least squares discriminant analysis (OPLS-DA) is used to determine the optimal classification model, and it is explored whether the combination of different technologies is better than a single technical analysis. It was confirmed that in the inter-provincial samples, the combination of the two techniques was better than the analysis using a single technique (fatty acids: R² = 0.716, Q² = 0.614; fatty acid-binding isotopes: R² = 0.760, Q² = 0.635). At the same time, milk produced by farms with different distances of less than 11 km in each province was discriminated, and the discriminant distance was successfully reduced to 0.7 km (Ningxia Hui Autonomous Region: the distance between the two farms was 0.7 km, R² = 0.771, Q² = 0.631). For short-distance samples, the combination multiple technologies are not completely superior to a single technique, and sometimes, it is easy to cause model over-fitting.

Honey Phenolic Compound Profiling and Authenticity Assessment Using HRMS Targeted and Untargeted Metabolomics

Honey consumption is attributed to potentially advantageous effects on human health due to its antioxidant capacity as well as anti-inflammatory and antimicrobial activity, which are mainly related to phenolic compound content. Phenolic compounds are secondary metabolites of plants, and their content in honey is primarily affected by the botanical and geographical origin. In this study, a high-resolution mass spectrometry (HRMS) method was applied to determine the phenolic profile of various honey matrices and investigate authenticity markers. A fruitful sample set was collected, including honey from 10 different botanical sources (n = 51) originating from Greece and Poland. Generic liquid-liquid extraction using ethyl acetate as the extractant was used to apply targeted and non-targeted workflows simultaneously. The method was fully validated according to the Eurachem guidelines, and it demonstrated high accuracy, precision, and sensitivity resulting in the detection of 11 target analytes in the samples. Suspect screening identified 16 bioactive compounds in at least one sample, with abscisic acid isomers being the most abundant in arbutus honey. Importantly, 10 markers related to honey geographical origin were revealed through non-targeted screening and the application of advanced chemometric tools. In conclusion, authenticity markers and discrimination patterns were emerged using targeted and non-targeted workflows, indicating the impact of this study on food authenticity and metabolomic fields.

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.

Use of elemental profiles to verify geographical origin and botanical variety of Spanish honeys with a protected denomination of origin

Honey with Protected Denomination of Origin (PDO) could be an attractive target for fraudsters. Elemental profiles by Energy Dispersive-X Ray Fluorescence were processed by multivariate methods to classify 183 PDO honeys produced in three regions of Spain (Liébana, Granada, Tenerife). Additional honey samples (18) produced in a fourth region without PDO (El Bierzo) separated well from the PDO clusters. The manganese content was a discriminant marker of Liébana PDO and El Bierzo, that could also be differentiated from each other. Within each region, distinct clusters revealed differences between dark vs light varieties, multi- vs uni-floral honey and producers of the same PDO. The developed models were validated with 131 samples produced outside the PDO regions and El Bierzo. The proposed classification approach could be implemented as a fast screening tool to support pollen analysis in honey authentication. The reduced number of observations in some light honey models affected their performance.

Geographical Origin Discrimination of Monofloral Honeys by Direct Analysis in Real Time Ionization-High Resolution Mass Spectrometry (DART-HRMS)

An untargeted method using direct analysis in real time and high resolution mass spectrometry (DART-HRMS) combined to multivariate statistical analysis was developed for the discrimination of two monofloral (chestnut and acacia) honeys for their geographical origins-i.e., Italy and Portugal for chestnut honey and Italy and China for acacia honey. Principal Component Analysis, used as an unsupervised approach, showed samples of clusterization for chestnut honey samples, while overlapping regions were observed for acacia honeys. Three supervised statistical approaches, such as Principal Components-Linear Discriminant Analysis, Partial Least Squares-Discriminant Analysis and k-nearest neighbors, were tested on the dataset gathered and relevant performances were compared. All tested statistical approaches provided comparable prediction abilities in cross-validation and external validation with mean values falling between 89.2-98.4% for chestnut and between 85.8-95.0% for acacia honey. The results obtained herein indicate the feasibility of the DART-HRMS approach in combination with chemometrics for the rapid authentication of honey's geographical origin.

Environmental gut bacteria in European honey bees (Apis mellifera) from Australia and their relationship to the chalkbrood disease

We report on aerobic “environmental” bacteria isolated from European honey bees (Apis mellifera). We determined the number of culturable aerobic bacteria in the gut of nurse bees sampled from locations around Australia. Bees from healthy colonies had 10⁷–10⁸ aerobic bacteria per g of bee gut, while bees from colonies with chalkbrood consistently had significantly fewer bacteria (10⁴–10⁵ bacteria per g). When colonies recovered from chalkbrood, bacterial numbers returned to normal levels, suggesting that counting aerobic bacteria in the gut could be used to predict an outbreak of the disease. Furthermore, Western Australian bees from the “Better Bees” program (bred to promote hygienic behaviour) had significantly higher numbers of aerobic gut bacteria compared to regular bees from healthy colonies. Bacteria with the ability to inhibit the chalkbrood pathogen were found in most bees from regular colonies (> 60%) but only in a few “Better Bees” (10%). Phylogenetic analysis of aerobic bacterial isolates that inhibited the chalkbrood pathogen revealed a close relationship (>97% sequence identity) to the genera Bacillus, Klebsiella, Pantoea, Hafnia, and Enterobacter (bacteria that have previously been isolated from honey bees), but we also isolated Maccrococcus and Frigoribacterium species (bacteria that were not previously identified in bees). Finally, we investigated the ability of bacteria to inhibit the chalkbrood fungus Ascosphaera apis. Mass spectroscopy analysis revealed that the bee gut isolates Frigoribacterium sp. and Bacillus senegalensis produce gluconic acid. We further found that this simple sugar is involved in chalkbrood fungal hyphal lysis and cytoplasmic leakage. Our findings suggest that “environmental” gut bacteria may help bees to control the chalkbrood pathogen.

Geographical origin and botanical type honey authentication through elemental metabolomics via chemometrics

The trace and rare earth elements content of 93 honeys of different botanical type and origin have been studied through ICP-MS. Discriminant Analysis (DA) was successful for botanical type and geographical origin classification while Cluster Analysis (CA) was successful only for botanical type. Through Probabilistic Neural Network (PNN) analysis, 85.3% were correctly classified by the network according to their geographical origin and 73.3% according to their organic characterization. A Partial Least Squares (PLS) model was constructed, giving a prediction accuracy of more than 95%. Information obtained using Rare Earths (Y, La, Ce, Pr, Nd, Sm, Eu, Gd, Tb, Dy, Ho, Er, Tm, Yb, Lu) and trace elements (Li, Mg, Mn, Ni, Co, Cu, Sr, Ba, Pb) via chemometric evaluation facilitated classification of honey samples.

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.

Metabolic Profiling of Different Parts of Acer truncatum from the Mongolian Plateau Using UPLC-QTOF-MS with Comparative Bioactivity Assays

Acer truncatum is an important ornamental, edible, and medicinal plant resource in China. Previous phytochemical research has focused on the leaf (AL) due to its long history as a tea for health. Other parts such as the branch (ABr), bark (ABa), fruit (AF), and root (AR) have drawn little attention regarding their metabolites and bioactivities. The strategy of an in-house chemical library combined with Progenesis QI informatics platform was applied to characterize the metabolites. A total of 98 compounds were characterized or tentatively identified, including 63 compounds reported from this species for the first time. Principal component analysis showed the close clustering of ABr, ABa, and AR, indicating that they share similar chemical components, while AL and AF clustered more distantly. By multiple orthogonal partial least-squares discriminant analyses (OPLS-DA), 52 compounds were identified as potential marker compounds differentiating these different plant parts. The variable influence on projection score from OPLS-DA revealed that catechin, procyanidins B2 or B3, and procyanidins C1 or C2 are the significant metabolites in ABa extracts, which likely contribute to its antioxidant and cytotoxic activities.

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.

HPTLC Fingerprinting-Rapid Method for the Differentiation of Honeys of Different Botanical Origin Based on the Composition of the Lipophilic Fractions

Bee honey possess various nutritional and medicinal functions, which are the result of its diverse chemical composition. The numerous bioactive compounds in honey come from flower nectar; thus, the identification of the specific chemical profiles of honey samples is of great importance. The lipophilic compounds from eight monofloral honeys (rape, buckwheat, clover, willow, milk thistle, dandelion, raspberry and sweet yellow clover) were investigated. Analyses of the lipophilic fractions obtained by UAE (ultrasound assisted extraction) and SPE (solid phase extraction) extractions were performed using high-performance thin layer chromatography (HPTLC). Chromatographic and cluster analyses allowed the identification of a unique, colorful pattern of separated compounds with specific Rf values on the HPTLC plate for each type of monofloral honey. HPTLC is a simple and effective visual method of analysis, and it can serve as a basis for authenticating different types of honey.

Emerging approach for analytical characterization and geographical classification of Moroccan and French honeys by means of a voltammetric electronic tongue

Moroccan and French honeys from different geographical areas were classified and characterized by applying a voltammetric electronic tongue (VE-tongue) coupled to analytical methods. The studied parameters include color intensity, free lactonic and total acidity, proteins, phenols, hydroxymethylfurfural content (HMF), sucrose, reducing and total sugars. The geographical classification of different honeys was developed through three-pattern recognition techniques: principal component analysis (PCA), support vector machines (SVMs) and hierarchical cluster analysis (HCA). Honey characterization was achieved by partial least squares modeling (PLS). All the PLS models developed were able to accurately estimate the correct values of the parameters analyzed using as input the voltammetric experimental data (i.e. r>0.9). This confirms the potential ability of the VE-tongue for performing a rapid characterization of honeys via PLS in which an uncomplicated, cost-effective sample preparation process that does not require the use of additional chemicals is implemented.

Determining the geographical origin of common buckwheat from China by multivariate analysis based on mineral elements, amino acids and vitamins

This study aimed to establish a method for distinguishing the geographical origin of common buckwheat from Inner Mongolia, Shanxi and Shaanxi Provinces in China. Three chemical families including mineral elements, vitamins and amino acids of 48 samples from different geographical origins were analyzed by principal component analysis (PCA), cluster analysis (CA) and linear discriminate analysis (LDA) for this purpose. LDA clearly discriminated the geographical origin of common buckwheat samples grown in three regions, and gave a high correct classification rate of 95.8% and satisfactory cross-validation rate of 91.7%. Some variables (Mn, VPP, Se, Gly, Cu, Asp, Fe, and Ala) significantly contributed to the ability to discriminate the geographical origin of the common buckwheat. These results demonstrated that the proposed method is a powerful tool for controlling the geographical origin of common buckwheat by governmental administration and protecting consumers from improper domestic labeling. However, the discriminant method still needs to be further validated using more reliable data.

Strategy for comparative untargeted metabolomics reveals honey markers of different floral and geographic origins using ultrahigh-performance liquid chromatography-hybrid quadrupole-orbitrap mass spectrometry

Honey discrimination based on floral and geographic origins is limited by the ability to determine reliable markers because developing hypothetical substances in advance considerably limits the throughput of metabolomics studies. Here, we present a novel approach to screen and elucidate honey markers based on comparative untargeted metabolomics using ultrahigh-performance liquid chromatography-hybrid quadrupole-orbitrap mass spectrometry (UHPLC-Q-Orbitrap). To reduce metabolite information losses during sample preparation, the honey samples were dissolved in water and centrifuged to remove insoluble particles prior to UHPLC-Q-Orbitrap analysis in positive and negative electrospray ionization modes. The data were pretreated using background subtraction, chromatographic peak extraction, normalization, transformation and scaling to remove interferences from unwanted biases and variance in the experimental data. The pretreated data were further processed using principal component analysis (PCA) and a three-stage approach (t-test, volcano plot and variable importance in projection (VIP) plot) to ensure marker authenticity. A correlation between the molecular and fragment ions with a mass accuracy of less than 1.0ppm was used to annotate and elucidate the marker structures, and the marker responses in real samples were used to confirm the effectiveness of the honey discrimination. Moreover, we evaluated the data quality using blank and quality control (QC) samples based on PCA clustering, retention times, normalized levels and peak areas. This strategy will help guide standardized, comparative untargeted metabolomics studies of honey and other agro-products from different floral and geographic origins.