Quantification of Methylglyoxal in Manuka Honey ― A Simple HPTLC Based Approach

Introduction
Honey ― a natural product, is one of the most frequently tested food products. In recent years, manuka honey has gained popularity because of its high antibacterial activity. MGO has been identified as one of the major contributors to its antibacterial activity. MGO is present in high concentrations in manuka honey and is directly responsible for its potency. This makes the manuka honey exclusive and high-priced as compared to the other traditional kinds of honey.

In this article, the quantification of MGO using HPTLC with subsequent substance confirmation by MS measurement is presented.

Experimental
Six different commercially available manuka honey samples were analyzed. MGO shows a mesomeric effect and reacts immediately with water to form either methylglyoxal monohydrate or methylglyoxal dihydrate in aqueous environments. Only a small amount of around 1% MGO remains unreacted. Direct detection of MGO in manuka honey is found to be difficult using conventional methods. In this application, MGO is converted to stable 2-methylquinoxaline by derivatizing it with 1,2-phenylenediamine.

Experimental Conditions
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Results and Discussion
As demonstrated, MGO can be identified and quantified in different honey samples within the concentration range of 50 mg to 600 mg/kg. The conversion of MGO into the more stable compound 2-methylquinoxaline allows for an easy evaluation of the MGO content. The recovery study showed a detectable MGO amount of around 90%.

Conclusion
The analysis of MGO in a complex and challenging food matrix, such as honey was described. Target analyte could be easily separated and detected without time-consuming and labor-intensive sample preparation. The flexible set-up enabled a combination with MS measurements.

To summarize, a fast, cheap, and simple quantification of methylglyoxal can be accomplished with HPTLC. This application demonstrates the main advantages of the method, such as quick sample preparation, high matrix tolerance, and high-throughput.