Identification of components of Brazilian honey by 1H NMR and classification of its botanical origin by chemometric methods

Analysis of Free Amino Acid Composition and Honey Plant Species in Seven Honey Species in China

Honey is well-known as a food product that is rich in active ingredients and is very popular among consumers. Free amino acids (FAAs) are one of the important nutritional components of honey, which can be used not only as a nutritional indicator of honey but also as an indicator of plant source identification. In this study, the contents of 20 FAAs in seven types of honey from 11 provinces in China were examined for the first time. The 20 FAAs were analyzed by ultra-performance liquid chromatography-mass spectrometry/mass spectrometry (UPLC-MS/MS). By analyzing 93 honey samples from seven types of honey, the FAAs were found to range from 394.4 mg/kg (linden honey) to 1771.7 mg/kg (chaste honey). Proline ranged from 274.55 to 572.48 mg/kg, and methionine was only present in some of the linden honey, chaste honey, acacia honey, and rape honey. Evaluated by amino acid principal component analysis, multifloral grassland honey had the highest overall evaluation score, acacia and jujube honey were the most similar, while chaste honey was the least similar to the other types of honey. In addition, DNA was extracted from 174 Xinjiang grassland honey samples and different plant leaves for PCR and sequencing to identify the species of nectar plants. As a result, 12 families and 25 species of honey plants were identified. The results confirmed the diversity of FAAs in dissimilar types and sources of honey. This study provides a reference for expanding honey quality standards and verifying the authenticity of honey.

Application of segmented analysis via multivariate curve resolution with alternating least squares to 1H-nuclear magnetic resonance spectroscopy to identify different sugar sources

The different types of sugar employed in the food industry exhibit chemical similarity and are mostly dominated by sucrose. Owing to the sugar origin of and differences in production, the presence of certain minor organic compounds differs. To differentiate between sugars based on their botanical source, geographical origin, or storage conditions, commercial brown sugars and sugar beet extracts were analyzed by ¹H NMR spectroscopy applying a segmented analysis by means of multivariate curve resolution-alternating least squares (MCR-ALS). Principal component analysis and partial least squares-discriminant analysis yielded excellent differentiation between sugars from different sources after the application of this preprocessing strategy; without loss of chemical information and with direct interpretation of the results. By applying a segmented analysis via MCR-ALS to ¹H NMR sugar data, similar spectroscopic profiles could be differentiated. This improved the selectivity of ¹H NMR spectroscopy for sugar source differentiation which can be useful for industrial sugar authentication purposes.

The Importance of Testing the Quality and Authenticity of Food Products: The Example of Honey

The aim of this study was to review methods of honey testing in the assessment of its quality and authenticity. The quality of honey, like other food products, is multidimensional. This quality can be assessed not only on the basis of the characteristics evaluated by the consumer during purchase and consumption, but also on the basis of various physicochemical parameters. A number of research methods are used to verify the quality of honeys and to confirm their authenticity. Obligatory methods of assessing the quality of honey are usually described in legal acts. On the other hand, other, non-normative methods of honey quality assessment are used worldwide; they can be used to determine not only the elementary chemical composition of individual types of honey, but also the biological activity of honey and its components. However, so far, there has been no systematization of these methods together with a discussion of problems encountered when determining the authenticity of honeys. Therefore, the aim of our study was to collect information on the methods of assessing the quality and authenticity of honeys, and to identify the problems that occur during this assessment. As a result, a tabular summary of various research methods was created.

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.

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.

Isolation and Barcoding of Trace Pollen-free DNA for Authentication of Honey

Adulteration and mislabeling of honey to mask its true origin have become a global concern. Pollen microscopy, the current gold standard for identifying honey's geographical and plant origins, is laborious, requires extensive training, and fails to identify filtered honey and honey spiked with pollen from a more favorable plant to disguise its origins. We successfully isolated pollen-free DNA from filtered honey using three types of adsorbents: (i) anti-dsDNA antibodies coupled to magnetic microspheres; (ii) anion-exchange adsorbent; and (iii) ceramic hydroxyapatite. The internal transcribed spacer 2 region of the captured pollen-free DNA was polymerase chain reaction-amplified and subjected to next-generation sequencing. Using an in-house bioinformatics pipeline, initial experiments showed that anion exchange had the greatest capacity to capture trace pollen-free DNA, and it was successfully applied to isolate DNA from five honey samples. Enrichment of trace pollen-free DNA from filtered honey samples opens a new approach for identifying the true origins of honey.

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.

Polyphenols and Maillard Reaction Products in Dried Prunus spinosa Fruits: Quality Aspects and Contribution to Anti-Inflammatory and Antioxidant Activity in Human Immune Cells Ex Vivo

Dried Prunus spinosa fruits (sloes) are folk phytotherapeutics applied to treat chronic inflammatory disorders. However, their pharmacological potential, activity vectors, and drying-related changes in bioactive components remain unexplored. Therefore, the present research aimed to evaluate the anti-inflammatory and antioxidant effects of dried sloes in ex vivo models of human neutrophils and peripheral blood mononuclear cells (PMBCs) and establish their main active components. It was revealed that the fruit extracts significantly and dose-dependently inhibited the respiratory burst, downregulated the production of elastase (ELA-2) and TNF-α, and upregulated the IL-10 secretion by immune cells under pro-inflammatory and pro-oxidant stimulation. The slightly reduced IL-6 and IL-8 secretion was also observed. The structural identification of active compounds, including 45 phenolics and three Maillard reaction products (MRPs) which were formed during drying, was performed by an integrated approach combining LC-MS/MS, preparative HPLC isolation, and NMR studies. The cellular tests of four isolated model compounds (chlorogenic acid, quercetin, procyanidin B2, and 5-hydroxymethylfurfural), supported by statistical correlation studies, revealed a significant polyphenolic contribution and a slight impact of MRPs on the extracts’ effects. Moreover, a substantial synergy was observed for phenolic acids, flavonoids, condensed proanthocyanidins, and MPRs. These results might support the phytotherapeutic use of dried P. spinosa fruits to relieve inflammation and establish the quality control procedure for the extracts prepared thereof.

Detecting adulteration of stingless bee honey using untargeted 1H NMR metabolomics with chemometrics

As stingless bee honey (SBH) is gaining in popularity in the Malaysian market, it is now prone to adulteration. The higher price of SBH compared to floral honey has led to the use of unusual adulterants such as vinegar and even floral honey to mimic the unique taste and appearance of SBH. Since the current AOAC 998.12 method fails to detect these adulterants as their δ¹³C values are in the range for C₃ plants, untargeted ¹H NMR metabolomics was proposed. Principal component analysis of SBH ¹H NMR fingerprints was able to distinguish authentic SBHs from adulterated ones down to 1% adulteration level for selected adulterants. Discriminant analysis showed promising results in distinguishing the preliminary datasets of authentic SBHs from the adulterated ones, including discriminating SBHs adulterated with different adulterants derived from C₃ and C₄ plants. Hence, to assure any emerging adulterant can be detected, all ¹H NMR regions should be considered.

The Association of Tanacetum parthenium and Salix alba Extracts Reduces Cortex Serotonin Turnover, in an Ex Vivo Experimental Model of Migraine

The mixture of water extracts from Tanacetum parthenium and Salix alba was studied in an ex vivo assessment of neurotoxicity constituted by isolated mouse cortex specimens challenged with K+ 60 mM Krebs–Ringer buffer (neurotoxicity stimulus). The effects of the mixture on lactate dehydrogenase (LDH), nitrite and serotonin levels were investigated. The phytochemical profile of the mixture was also evaluated. A docking approach was conducted to predict, albeit partially, the putative mechanism underlying the observed effects. The extracts displayed a good profile of polyphenolic compounds (22 chromatographic peaks detected), with caftaric acid and epicatechin being the prominent phenols. In isolated cortex, the association of T. parthenium and S. alba extracts was effective in reducing the K+ 60 mM-induced levels of LDH and nitrites, whereas the neurotoxicity stimulus-induced serotonin depletion was prevented by the treatment. Regarding the inhibition of serotonin catabolism, epicatechin (44.65 µg/mg) and caftaric acid (10.51 µg/mg) were putatively the main compounds involved in the inhibition of monoamineoxidase-A, which is known to play a master role in serotonin turnover. Collectively, the results of the present study point to the efficacy of the present extract mixture as an innovative pharmacological tool to prevent the onset of migraine.

Metabolomic Profiling of Malaysian and New Zealand Honey Using Concatenated NMR and HRMS Datasets

This study aims to compare the metabolomic profiles of Malaysian and New Zealand honey while determining their anti-oncogenic activity for potential prophylactic functions. Metabolomics tools including multivariate analysis were applied on concatenated LC-HRMS and NMR datasets to afford an intensive chemical profile of honey samples and have a snapshot of the bioactive metabolites in the respective collections. Malaysian samples were found to have higher sugar and polyphenolic content, while New Zealand samples afforded higher concentration of low molecular weight (MW) lipids. However, New Zealand honey collected from the northern islands had higher concentration of acetylated saccharides, while those from the southern islands yielded higher low MW phenolic metabolites that were comparable to Malaysian honey. Mild anti-oncogenic compounds against breast cancer cell line ZR75 were putatively identified in Malaysian honey that included earlier described antioxidants such as gingerdiol, 2-hexylphenol-O-β-D-xylopyranoside, plastoquinone, tropine isovalerate, plumerinine, and 3,5-(12-phenyl-8-dodecenyl)resorcinol, along with several phenolic esters and lignans.

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.

Determination of Floral Origin Markers of Latvian Honey by Using IRMS, UHPLC-HRMS, and 1H-NMR

The economic significance of honey production is crucial; therefore, modern and efficient methods of authentication are needed. During the last decade, various data processing methods and a combination of several instrumental methods have been increasingly used in food analysis. In this study, the chemical composition of monofloral buckwheat (Fagopyrum esculentum), clover (Trifolium repens), heather (Calluna vulgaris), linden (Tilia cordata), rapeseed (Brassica napus), willow (Salix cinerea), and polyfloral honey samples of Latvian origin were investigated using several instrumental analysis methods. The data from light stable isotope ratio mass spectrometry (IRMS), ultra-high performance liquid chromatography coupled with high-resolution mass spectrometry (UHPLC-HRMS), and nuclear magnetic resonance (NMR) analysis methods were used in combination with multivariate analysis to characterize honey samples originating from Latvia. Results were processed using the principal component analysis (PCA) to study the potential possibilities of evaluating the differences between honey of different floral origins. The results indicate the possibility of strong differentiation of heather and buckwheat honeys, and minor differentiation of linden honey from polyfloral honey types. The main indicators include depleted δ¹⁵N values for heather honey protein, elevated concentration levels of rutin for buckwheat honey, and qualitative presence of specific biomarkers within NMR for linden honey.

NMR Tracing of Food Geographical Origin: The Impact of Seasonality, Cultivar and Production Year on Data Analysis

The traceability of typical foodstuffs is necessary to protect high quality of traditional products. It is well-known that several factors could influence metabolites content in certified foods, but soil composition, altitude, latitude and coded production protocols constitute the territorial conditions responsible for the peculiar organoleptic and nutritional properties of labelled foods. Instead, regardless of origin, seasonality, cultivar, collection year can affect all agricultural products, so it is appropriate to include them in data analysis in order to obtain a correct interpretation of the differences linked to growing areas alone. Therefore, it is useful to use a flexible all-round technique, and NMR spectroscopy coupled with multivariate statistical analysis is considered a powerful means of assessing food authenticity. The purpose of this review is to investigate the relevance of year, cultivar, and seasonal period in the determination of food geographical origin using NMR spectroscopy. The strategy for testing these three factors may differ from author to author, but a preliminary study of cultivar or collection year effects on NMR spectra is the most popular method before starting the geographical characterization of samples. In summary, based on the available literature, the most significant influence is due to cultivar, followed by harvesting year, however seasonality is not considered a source of variability in data analysis.

Making salty cucumbers and honeyed apples by applying the sonochemical method

Sonochemistry was applied in the last few years for coating surfaces of various substrates for imparting desired properties to the surface. In the current paper the coating of cucumbers with NaCl nanoparticles and apples with honey nanoparticles was accomplished by applying the sonochemical method. In both coating the nanoparticles were deposited from aqueous solutions. The products were characterized by Inductively coupled plasma, Dynamic light scattering, Scanning electron microscopy, and Nuclear magnetic resonance.

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.

1 H-NMR analysis, antioxidant activity, and α-amylase and α-glucosidase inhibitory potential of ten common Thai edible flowers

BACKGROUND

Edible flowers have been used in cooking as foods as well as medicine alternatives. The objectives were to identify a major class of organic compounds in edible flowers by applying proton nuclear magnetic resonance (¹ H-NMR) metabolite fingerprinting and to evaluate bioactivity including antioxidant and α-amylase and α-glucosidase inhibitory activities.

RESULTS

A total of 32 metabolites including five organic acids, eight sugars, 11 amino acids and eight phenolic compounds were identified by using ¹ H-NMR at 600 MHz. The aromatic proton of phenolic compounds was observed at the low-field region ranged between δ 5.5 ppm and 10.0 ppm. Total phenolic content (TPC) of the extracts of edible flowers ranged from 6.0 mg GAE g^-1 dry weight (DW) to 47.97 mg GAE g^-1 DW. Antioxidant activity varied from 140.5 mmol L^-1 TE g^-1 DW to 985.2 mmol L^-1 TE g^-1 DW. The extracts from edible flowers showed dose-dependent inhibition against enzyme. The extract of African marigold (Tagetes erecta L.) had the highest half maximal inhibitory concentration (IC₅₀ ) against α-amylase at 0.13 mg mL^-1 while the extract of flower of Sesban agasta (Sesbania grandiflora Desv.) had the lowest IC₅₀ against α-amylase at 0.63 mg mL^-1 . Moderate correlation was found between IC₅₀ values against α-glucosidase and antioxidant activity values (R²  = 0.66) (P < 0.05).

CONCLUSION

¹ H-NMR application provided metabolomic fingerprinting of edible flowers and the in vitro inhibitory potential activity against key enzymes related to diabetes indicated that specific edible flowers have a potential use for dietary management during the early stages of hyperglycaemia. © 2021 Society of Chemical Industry.

Opportunities and Constraints in Applying Artificial Neural Networks (ANNs) in Food Authentication. Honey—A Case Study

The present work aims to test the potential of the application of Artificial Neural Networks (ANNs) for food authentication. For this purpose, honey was chosen as the working matrix. The samples were originated from two countries: Romania (50) and France (53), having as floral origins: acacia, linden, honeydew, colza, galium verum, coriander, sunflower, thyme, raspberry, lavender and chestnut. The ANNs were built on the isotope and elemental content of the investigated honey samples. This approach conducted to the development of a prediction model for geographical recognition with an accuracy of 96%. Alongside this work, distinct models were developed and tested, with the aim of identifying the most suitable configurations for this application. In this regard, improvements have been continuously performed; the most important of them consisted in overcoming the unwanted phenomenon of over-fitting, observed for the training data set. This was achieved by identifying appropriate values for the number of iterations over the training data and for the size and number of the hidden layers and by introducing of a dropout layer in the configuration of the neural structure. As a conclusion, ANNs can be successfully applied in food authenticity control, but with a degree of caution with respect to the “over optimization” of the correct classification percentage for the training sample set, which can lead to an over-fitted model.

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

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

Quality, composition and health-protective properties of citrus honey: A review

Citrus honey is one of the most important monofloral honeys produced and consumed worldwide. This honey has pleasant sensorial characteristics, which include light color and typical aroma and flavor. Besides that, several constituents such as minerals, phenolic and volatile compounds, amino acids, sugars, enzymes, vitamins, methylglyoxal and organic acids are found in citrus honey. Moreover, potential biological properties have been associated with citrus honey. All these factors make it highly desired by consumers, increasing its market value, which can stimulates the practice of fraud. Also, citrus honey is susceptible to contamination and to inadequate processing. All these factors can compromise the quality, safety and authenticity of citrus honey. In this sense, this review aims to update and to discuss, for the first time, the data available in the literature about the physicochemical and the sensorial characteristics, composition, health properties, contamination, authenticity and adulteration of citrus honey. With this background, we aim to provide data that can guide future researches related to this honey.

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.

Water Extract from Inflorescences of Industrial Hemp Futura 75 Variety as a Source of Anti-Inflammatory, Anti-Proliferative and Antimycotic Agents: Results from In Silico, In Vitro and Ex Vivo Studies

Industrial hemp (Cannabis sativa) is traditionally cultivated as a valuable source of fibers and nutrients. Multiple studies also demonstrated antimicrobial, anti-proliferative, phytotoxic and insecticide effects of the essential oil from hemp female inflorescences. On the other side, only a few studies explored the potential pharmacological application of polar extracts from inflorescences. In the present study, we investigated the water extract from inflorescences of industrial hemp Futura 75 variety, from phytochemical and pharmacological point of view. The water extract was assayed for phenolic compound content, radical scavenger/reducing, chelating and anti-tyrosinase effects. Through an ex vivo model of toxicity induced by lipopolysaccharide (LPS) on isolated rat colon and liver, we explored the extract effects on serotonin, dopamine and kynurenine pathways and the production of prostaglandin (PG)E₂. Anti-proliferative effects were also evaluated against human colon cancer HCT116 cell line. Additionally, antimycotic effects were investigated against Trichophyton rubrum, Trichophyton interdigitale, Microsporum gypseum. Finally, in silico studies, including bioinformatics, network pharmacology and docking approaches were conducted in order to predict the putative targets underlying the observed pharmacological and microbiological effects. Futura 75 water extract was able to blunt LPS-induced reduction of serotonin and increase of dopamine and kynurenine turnover, in rat colon. Additionally, the reduction of PGE₂ levels was observed in both colon and liver specimens, as well. The extract inhibited the HCT116 cell viability, the growth of T. rubrum and T. interdigitale and the activity of tyrosinase, in vitro, whereas in silico studies highlighting the inhibitions of cyclooxygenase-1 (induced by carvacrol), carbonic anhydrase IX (induced by chlorogenic acid and gallic acid) and lanosterol 14-α-demethylase (induced by rutin) further support the observed pharmacological and antimycotic effects. The present findings suggest female inflorescences from industrial hemp as high quality by-products, thus representing promising sources of nutraceuticals and cosmeceuticals against inflammatory and infectious diseases.

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.

Determination of invert syrup adulterated in acacia honey by terahertz spectroscopy with different spectral features

BACKGROUND

Invert syrup is a common adulterant in honey falsification, thus generating risk for consumers. Most of the methods developed are tedious and time-consuming for manufactures and consumers. However, terahertz spectroscopy provides analytical information in a simple, rapid, and environmentally friendly manner. Subsequently, 3 kinds of terahertz spectroscopic characteristics data, the absorption coefficient, the slope of the absorption coefficient spectra, and the area of the absorption coefficient spectra, were employed for determination of acacia honey adulterated with invert syrup.

RESULTS

Single linear regression (SLR) models with different terahertz spectroscopic features were adopted to predict the syrup adulterant proportion in acacia honey. The best SLR model used the area of the absorption coefficient, displaying an adjusted correlation coefficient of 0.985 and a root-mean-square error of 3.201. Meanwhile, multiple linear regression (MLR) models using a successive projections algorithm for variables selection were implemented. The MLR model considered the integral area of the absorption coefficient spectra, as the inputs yielded the best result with less variables selected, higher R c 2 and R p 2 , lower root-mean-square error of calibration and prediction, as well as higher residual predictive deviation.

CONCLUSIONS

The results indicate terahertz spectroscopy combined with the integral area of the absorption coefficient spectra is reliable enough for invert syrup proportion quantification in acacia honey and is also a rapid and nondestructive determination method for other honey adulterants. © 2019 Society of Chemical Industry.

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.

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.

Honey authentication using rheological and physicochemical properties

The aim of this study was to evaluate the influence of honey botanical origins on rheological parameters. In order to achieve the correlation, fifty-one honey samples, of different botanical origins (acacia, polyfloral, sunflower, honeydew, and tilia), were investigated. The honey samples were analysed from physicochemical (moisture content, fructose, glucose and sucrose content) and rheological point of view (dynamic viscosity-loss modulus G″, elastic modulus G', complex viscosity η*, shear storage compliance-J' and shear loss compliance J″). The rheological properties were predicted using the Artificial Neural Networks based on moisture content, glucose, fructose and sucrose. The models which predict better the rheological parameters in function of fructose, glucose, sucrose and moisture content are: MLP-1 hidden layer is predicting the G″, η* and J″, respectively, MLP-2 hidden layers the J', while MLP-3 hidden layers the G', respectively. The physicochemical and rheological parameters were submitted to statistical analysis as follows: Principal component analysis (PCA), Linear discriminant analysis (LDA) and Artificial neural network (ANN) in order to evaluate the usefulness of the parameters studied for honey authentication. The LDA was found the suitable method for honey botanical authentication, reaching a correct cross validation of 94.12% of the samples.

Determination of honey adulteration with beet sugar and corn syrup using infrared spectroscopy and genetic-algorithm-based multivariate calibration

BACKGROUND

Fourier transform infrared spectroscopy (FTIR) equipped with attenuated total reflectance accessory was used to determine honey adulteration. Adulterated honey samples were prepared by adding corn syrup, beet sugar and water as adulterants to the pure honey samples in various amounts. The spectra of adulterated and pure honey samples (n = 209) were recorded between 4000 and 600 cm^-1 wavenumber range.

RESULTS

Genetic-algorithm-based inverse least squares (GILS) and partial least squares (PLS) methods were used to determine honey content and amount of adulterants. Results indicated that the multivariate calibration generated with GILS could produce successful models with standard error of cross-validation in the range 0.97-2.52%, and standard error of prediction between 0.90 and 2.19% (% w/w) for all the components contained in the adulterated samples. Similar results were obtained with PLS, generating slightly larger standard error of cross-validation and standard error of prediction values.

CONCLUSION

The fact that the models were generated with several honey samples coming from various different botanical and geographical origins, quite successful results were obtained for the detection of adulterated honey samples with a simple Fourier transform infrared spectroscopy technique. Having a genetic algorithm for variable selection helped to build somewhat better models with GILS compared with PLS. © 2018 Society of Chemical Industry.

Nuclear Magnetic Resonance (NMR) Spectroscopy in Food Science: A Comprehensive Review

Nuclear magnetic resonance (NMR) spectroscopy is a robust method, which can rapidly analyze mixtures at the molecular level without requiring separation and/or purification steps, making it ideal for applications in food science. Despite its increasing popularity among food scientists, NMR is still an underutilized methodology in this area, mainly due to its high cost, relatively low sensitivity, and the lack of NMR expertise by many food scientists. The aim of this review is to help bridge the knowledge gap that may exist when attempting to apply NMR methodologies to the field of food science. We begin by covering the basic principles required to apply NMR to the study of foods and nutrients. A description of the discipline of chemometrics is provided, as the combination of NMR with multivariate statistical analysis is a powerful approach for addressing modern challenges in food science. Furthermore, a comprehensive overview of recent and key applications in the areas of compositional analysis, food authentication, quality control, and human nutrition is provided. In addition to standard NMR techniques, more sophisticated NMR applications are also presented, although limitations, gaps, and potentials are discussed. We hope this review will help scientists gain some of the knowledge required to apply the powerful methodology of NMR to the rich and diverse field of food science.

Food fingerprints - A valuable tool to monitor food authenticity and safety

In recent years, food frauds and adulterations have increased significantly. This practice is motivated by fast economical gains and has an enormous impact on public health, representing an important issue in food science. In this context, this review has been designed to be a useful guide of potential biomarkers of food authenticity and safety. In terms of food authenticity, we focused our attention on biomarkers reported to specify different botanical or geographical origins, genetic diversity or production systems, while at the food safety level, molecular evidences of food adulteration or spoilage will be highlighted. This report is the first to combine results from recent studies in a format that allows a ready overview of metabolites (<1200 Da) and potentially molecular routes to monitor food authentication and safety. This review has therefore the potential to unveil important aspects in food adulteration and safety, contributing to improve the current regulatory frameworks.

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.

Terahertz time-domain attenuated total reflection spectroscopy applied to the rapid discrimination of the botanical origin of honeys

A new technique to identify the floral resources of honeys is demanded. Terahertz time-domain attenuated total reflection spectroscopy combined with chemometrics methods was applied to discriminate different categorizes (Medlar honey, Vitex honey, and Acacia honey). Principal component analysis (PCA), cluster analysis (CA) and partial least squares-discriminant analysis (PLS-DA) have been used to find information of the botanical origins of honeys. Spectral range also was discussed to increase the precision of PLS-DA model. The accuracy of 88.46% for validation set was obtained, using PLS-DA model in 0.5-1.5THz. This work indicated terahertz time-domain attenuated total reflection spectroscopy was an available approach to evaluate the quality of honey rapidly.

Honeydew Honeys: A Review on the Characterization and Authentication of Botanical and Geographical Origins

The commercial interest in honeydew honeys (from the secretions of plants or the excretions of plant-sucking insects found on plants) is increasing because of their higher therapeutic properties compared with those of most blossom honeys (from nectar). However, honeydew honeys have been less studied than blossom honeys. In this work, studies carried out to characterize and authenticate honeydew honeys by their botanical and geographical origins have been reviewed. The identification of honey origins has been approached by two ways: by the analysis of chemical markers and by the development of analytical methodologies combined with multivariate analyses. Some compounds have been suggested as specific botanical markers of several honeydew honeys, such as quercitol and trans-oak lactone for oak honey, 2-aminoacetophenone and propylanisol for holm oak honey, and 1-chloro-octane and tridecane for pine honey. The presence of 3-carene and an unidentified compound in samples was proposed as a way discriminate between Greek and Turkish pine honeys. Chemometric analyses have been applied on chemical compositions and on physicochemical, microscopic, and spectral parameters and have proved to be valuable methods for authenticating honeydew honeys. Analytical methods based on spectral information are suitable for the routine control of honeydew-honey origins because they are fast and require easy sample preparations.

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.