Sugar Profiling of Honeys for Authentication and Detection of Adulterants Using High-Performance Thin Layer Chromatography.

Molecules : a Journal of Synthetic Chemistry and Natural Product Chemistry
Md Khairul IslamCornelia Locher

Abstract

Honey adulteration, where a range of sugar syrups is used to increase bulk volume, is a common problem that has significant negative impacts on the honey industry, both economically and from a consumer confidence perspective. This paper investigates High-Performance Thin Layer Chromatography (HPTLC) for the authentication and detection of sugar adulterants in honey. The sugar composition of various Australian honeys (Manuka, Jarrah, Marri, Karri, Peppermint and White Gum) was first determined to illustrate the variance depending on the floral origin. Two of the honeys (Manuka and Jarrah) were then artificially adulterated with six different sugar syrups (rice, corn, golden, treacle, glucose and maple syrup). The findings demonstrate that HPTLC sugar profiles, in combination with organic extract profiles, can easily detect the sugar adulterants. As major sugars found in honey, the quantification of fructose and glucose, and their concentration ratio can be used to authenticate the honeys. Quantifications of sucrose and maltose can be used to identify the type of syrup adulterant, in particular when used in combination with HPTLC fingerprinting of the organic honey extracts.

References

Jan 31, 2013·Journal of Agricultural and Food Chemistry·Elisabetta SchievanoStefano Mammi
Sep 3, 2013·Food Chemistry·Russell FrewRobert Van Hale
Feb 27, 2014·Journal of Agricultural and Food Chemistry·Karyne M RogersPam Rogers
May 17, 2014·Spectrochimica Acta. Part A, Molecular and Biomolecular Spectroscopy·Quansheng ChenJiewen Zhao
Sep 25, 2016·Food Chemistry·Amna Jabbar SiddiquiAtta-Ur- Rahman
Feb 6, 2017·Food Chemistry·Saber AmiryMohammad Alizadeh
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Oct 15, 2019·Food Chemistry·Elisabeta-Irina Geana, Corina Teodora Ciucure
Sep 1, 2017·Comprehensive Reviews in Food Science and Food Safety·Sónia SoaresIsabel Mafra

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BETA
nuclear magnetic resonance
NMR

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visionCATS
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