Conference Paper

Honey & Honey Adulteration Detection: A Review

In proceeding of: 11th International Congress on Engineering and Food - Athens, Greece, 2011 (iCEF11), Volume: 3

ABSTRACT Honey is an ancient valuable food and in most cases has enchanted its consumers by its medic characteristics. It consists mainly of sugars but also contains some amounts of acids, nitrogenous compounds, phenolic contents, HMF, minerals and water. Honey composition according to the studied literature is mainly dependant on its floral source and differs in various honeys. Honey adulteration is a complex problem which currently has a significant economic impact and undeniable nutritional and organoleptic consequences. There are many methods utilized for honey adulteration detection by most researchers, for instance Gas Chromatography (GC) and Liquid Chromatography (LC) analysis, Near Infrared Transflectance (NIR) spectroscopy, Fourier Transform Infrared (FTIR) spectroscopy with Attenuated Total Reflectance (ATR), Protein characterization, High-Performance Anion-Exchange Chromatography with Pulsed Amperometric Detection (HPAEC-PAD), Liquid Chromatography Coupled to Isotope Ratio Mass Spectrometry (HPLCIRMS), Calorimetric methods (Application of DSC), Stable Carbon Isotope Ratio Analysis (SCIRA), Fourier Transform (FT) Raman spectroscopy and Microscopic detection. These methods are all applicable and provide useful information about each aspect of honey authenticity but in order to have an overall and accurate result we should not rely on each technique solely but also do some of them concomitantly. These mentioned methods are described briefly in this review.

3 Bookmarks
 · 
611 Views
  • [Show abstract] [Hide abstract]
    ABSTRACT: As a natural product, honey has been prone to adulteration. Adulteration of honey by substituting with cheap invert sugars is a critical issue in the honey industry. Fourier Transform (FT) Raman Spectroscopy was used to detect adulterants such as cane and beet invert in honey. FT Ra man spectrum of adulterated samples were characterized and the region between 200 and 1600 cm−1 (representing carbohydrates and amino acid fractions) was used for quantitative and discriminant analysis. Partial least squares, and principal component regression analysis were used for quantitative analysis while linear discriminant analysis and canonical variate analysis (CVA) were used for discriminant analysis. FT-Raman spectroscopy was efficient in predicting beet and cane invert adulterants (R2>0.91) in all three floral types of honey considered. Classification of adulterants in honey using CVA gave a minimum classification accuracy of about 96%.
    Food Chemistry 01/2002; · 3.33 Impact Factor
  • [Show abstract] [Hide abstract]
    ABSTRACT: A method previously validated in our laboratory to study the oligosaccharide profile of honey was used in this work to detect adulterations of corn syrups (CS) and high fructose corn syrups (HFCS) in genuine honey samples. High molecular weight oligosaccharides (DP3-DP16) of 9 sugar syrups and 25 honey samples were analysed by HPAEC-PAD. Samples were previously treated with activated charcoal to remove mono and disaccharides. This method enabled the detection of honey adulterations with CS down to 5%. Adulterations of honeys with HFCS with different degrees of isomerisation (20% and 40%) were also detected.
    Food Chemistry - FOOD CHEM. 01/2008; 107(2):922-928.
  • [Show abstract] [Hide abstract]
    ABSTRACT: Fourier transform infrared (FTIR) spectroscopy with attenuated total reflectance (ATR) accessory was used to quantify three different adulterants (corn syrup, high fructose corn syrup and inverted sugar) in honeys of four different locations of México (Chiapas, Oaxaca, Estado de México and Morelos). The optimal calibrations for the three adulterants were developed with partial least squares (PLS). The developed models were validated with different independent data sets being the standard error of prediction (SEP) between 1.5 and 2.1 for corn syrup, 2.1–3.0 for high fructose corn syrup and 1.4–2.5 for inverted sugar, showing the applicability of these models to the detection and quantification of adulterants in honey bee. Classification of the Mexican honeys from the four different states was carried out with soft independent modeling class analogy giving 100% correct classification rate and no false positive results were obtained.
    Food Research International. 01/2009;

Full-text

View
861 Downloads
Available from
May 30, 2014