honey authenticity (15)

 

10929046069?profile=RESIZE_400xHoney is classed as one of the food most susceptible to extension, adulteration and fraud. This review in the Journal of Apicultural Science examines the most used analytical methods for verifying the geographical and botanical origin of honey. These include long established melissopalynological analysis, the analysis of the mineral profile or chemico-physical parameters, to the current state-of-the-art technologies and methods including the metabolomic and genomic approaches, the blockchain or Internet of Things. The review discusses the advantages and limitations of these methods, and highlights the approach that many methods are used in combination because a combined approach usually leads to greater accuracy. 

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This review covers the myriad of analytical techniques that can be used for honey authenticity testing. It includes various detection methods like ISCIRA, NMR, AT-FTIR, Sensors, PCR based assay united with an appropriate multivariate approach. The botanical origin authentication of honey can be determined with the application of δ13C-EA-IRMS and δ13C-LC-IRMS coupled SVM, which discriminate samples based on specific markers. LIBS, NMR, HPTLC, UHPLC, GC, and real-time PCR, can generate data that is then processed with LDA, OPLS, PCA, ANN, CNN or other chemometric tequniques. The generated data discriminate adulterated honey from pure, and it is reported that NMR coupled with PCA can detect 1% of adulterants in honey. The review concludes that there is a long way to go in this field to develop a universal technology for honey authentication and adulteration detection.

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10482284266?profile=RESIZE_400xA Peer reviewed papaer of an interlaboratory comparison of a Liquid Chromatography-Isotope Ratio Mass Spectrometry (LC-IRMS) method for the determination of 13C/12ratios of saccharides in honey has been published.

This paper is based on a European Comission Joint Research Centre report, which was previosly reported on the Food Authenticity Network: EC Publishes report on the Interlaboratory Comparison of LC-IRMS applied on honey - News - FoodAuthenticity

Stable carbon isotope analysis of sugars in honey by LC–IRMS is a useful tool for detecting adulteration of honey with extraneous sugar.

Syrups that mimic the composition of honey that are produced by chemical and/or enzymatic modification of starch or sucrose are difficult to detect (10). If the starting product is obtained from a C4 plant, such as maize or sugar cane, stable carbon isotope ratio analysis (SCIRA) using a combination of an elemental analyzer (EA) and an isotope ratio mass spectrometer (IRMS) offers a possibility to detect additions down to a level of 7% (11).

Sugars originating from C3 plants such as beet root or generated from rice or wheat starch escape detection by SCIRA. Combining LC with IRMS (LC–IRMS) offers new possibilities for detecting honey adulteration with sugars derived from C3 plants and increases the sensitivity for detecting C4 sugars (1213).

Addition of 1% C4 sugars and 10% C3 sugars can be reliably detected using the LC–IRMS approach. Another benefit is that, as the method determines the 13C/12ratios of saccharides in honey, it moves away from reliance on external databases.

The method has gained popularity (14–20) but has never been subjected to multilaboratory validation, until now, which is a prerequisite for further developing it into a standard by a standards-developing organization. This peer reviewed publication reports on an interlaboratory comparison of this method. Read open access paper.

This method has now been accepted as a work item for standardisation by Working Group 6 (Stable Isotope Analysis) of CEN Technical Committee 460 (Food Authenticity).

 

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Droplet digital polymerase chain reation (ddPCR) technology is a PCR method utilising a water-oil emulsion droplet system, where each nanoliter-sized droplet in the emulsion contains the template DNA molecules, essentially serving the same function as individual test tubes or wells in a plate in which the PCR reaction takes place. In this study, ddPCR was used to detect adulteration of acacia honey with canola (rapeseed) honey. DNA extraction from pollen in acacia honey and canola honey was performed using four different pollen treatment methods. A duplex ddPCR method was developed based on the specific target gene in acacia and canola, which permitted detecting up to 1% adulteration of canola in acacia. This method is more rapid and accurate than the accepted microscopy examination of honey pollen, but does not address exogenous sugar adulteration of honey.

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8811352093?profile=RESIZE_400xThis book highlights the use of specific physicochemical parameters, such as sugar content, moisture content, electrical conductivity, acidity, colour, and attributes in the production of honey. It also discusses the use of honey micro-constituents, including volatile compounds, polyphenols, minerals, organic acids, free amino acids and isotopic data, in the determination of the botanical and geographical origins of honey, in combination with chemometrics. It represents the ultimate research guide and reference manual for the determination of honey uniqueness. 

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Honey is regarded as one of the foods most susceptible to adulteration or mislabelling. Greek researchers have used the Scopus database to determine which issues and methods of authenticity have had most published papers. The result indicated that the determination of botanical origin  was the most studied authenticity issue, and chromatographic methods were the most frequently used for its assessment. This comprehensive review examines other methodologies to assess honey botanical and geographical origin using separation techniques, DNA methods, spectroscopic, elemental and isotopic techniques. Methods for sugar adulteration of honey are not covered.

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This study compared the capabilities of three spectroscopic techniques as fast screening platforms for honey authentication purposes. Multifloral honeys were collected in the three main honey-producing regions of Argentina over four harvesting seasons to give a total of 502 samples. Spectra were run on each of the samples with FT-MIR ( Fourier transform mid-infrared), NIR (near infrared) and FT-Raman  (Fourier transform Raman)  spectroscopy. The spectroscopic platforms were compared on the basis of the classification performance achieved under a supervised chemometric approach. Very good classification scores to distinguish the three Argentian regions were achieved by all the spectroscopies, and a nearly perfect classification was provided by FT-MIR. The results obtained in the present work suggested that FT-MIR had the best potential for fingerprinting-based honey authentication, and demonstrated that sufficient accuracy levels to be commercially useful can be reached.

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In some countries, honey from native, non-domesticated species, such as Asian Apis dorsata and Apis cerana commands a much higher price than honey from the colonies of the domesticated honeybee Apis mellifera, and therefore is more vulnerable to fraud. Slovenian researchers have developed DNA  markers from a single copy ANT (adenine nucleotide translocase) gene using exon-primed intron-crossing (EPIC) primers and a double restriction protocol to obtain sequence information, which can identify the three bee species in honey. The method was developed using small extracts from 25 honeybee tissue samples and 21 honeybee products, and can be used for other bee products such as royal jelly.

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4469437532?profile=RESIZE_400xRaman spectroscopy represents an increasingingly useful technique for food authentication being a fast, reliable non-targeted method, requiring a minimum sample preparation step. However, in the case of honey, there are limitations to its application caused by sugar crystallisation effects and fluorescence in dark coloured honeys. Romanian researchers have developed a simple sample preparation of honey by a 1:1 w/v dilution in distilled water, which overcomes the limitations and gives reliable and reproductible spectra. 

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This paper reviews the latest research using botanical origin, chemical composition and physical properties to characterise and authenticate honey. Melissopanology (pollen identification), sensorial and physicochemical properties combined with statistical analysis or chemometrics are being used to study the characteristics of honey samples and classify them according to different botanical and geographical origins. 

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Honey is the third most adulterated food globally. This study by Australian researchers examined 100 honey samples from Australia (mainland and Tasmania) along with 18 other countries covering Africa, Asia, Europe, North America and Oceania. Carbon isotopic analyses of honey and protein showed that 27% of commercial honey samples tested were of questionable authenticity. The remaining 69 authentic samples were subject to trace element analysis for geographic determination, and were analysed chemometrically. The trace elements Sr, P, Mn and K were the most useful ones to differentiate honey according to its geographic origin. The findings show the common and prevalent issues of honey authenticity and the mislabelling of its geographic origin can be identified using a combination of stable carbon isotopes and trace element concentrations.

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The aim of this study is to evaluate the influence of some adulteration agents (fructose and hydrolysed inulin syrup) on physico-chemical parameters (pH, electrical conductivity, water activity and CIEL*a*b* parameters) and Raman spectra of some honey types (acacia, tilia and polyfloral) from the North East part of Romania.  Unlike physico-chemical analyses and color analysis, which determine only the degree of falsification of honey, Raman analysis enables identification of falsification agent based on specific vibrational bands recorded.

 Read the full paper at: Authenticity of Romanian Honey

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German researchers have developed a simple non-targeted approach to authenticate the plant species origin of monofloral honey using HS-GC-IMS (headspace gas chromatography ion mobility spectrometer) combined with optimised chemometric techniques as a complementary tool to proton NMR profiling. Whereas NMR profiling still requires comparatively precise sample preparation, pH adjustment in particular, HS-GC-IMS fingerprinting may be considered an alternative approach for a truly fully automated, cost-efficient, and in particular highly sensitive method. 

The HS-GC-IMS-based PCA–LDA model was composed of two linear functions of  discrimination        and 10 selected PCs that discriminated rapeseed, acacia, and honeydew honeys with a predictive accuracy of 98.6%.

Read the abstract at: Headspace authentication of honey

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