Abstract

Using High Resolution Mass Spectrometry (HRMS) Approaches to Fight Food Fraud: Don’t Get Stung by Your Manuka Honey!

Joanne Connolly, Omics Business Development, Waters

Understanding, deconvoluting and identifying the biochemical profile of a food sample of interest can help provide manufacturers and regulators with key information in the fight against fraud. The identification of a MS derived biochemical “fingerprint” is an important tool to understanding the ultimate question of “What is normal?” The rapid technological advancement in the field of High Resolution Mass Spectrometry now allows us to accurately profile food and food components. With each new technological development, an increasing amount of data can be generated and collected that helps better understand the chemical profile of the food. Honey is a high value food commodity with recognized nutraceutical properties. In recent years, there has been growing interest in verifying the floral origin of honey, especially in the characterization of unifloral honeys, which are often more valuable than polyfloral honeys. Certain types of unifloral honey (e.g Manuka Honey) claim apparent benefits for human health and are used in the treatment of wounds and diseases because of their healing and antibacterial properties. As such Manuka Honey is a valuable product that is susceptible to adulteration, and hence often sold or blended in a mis-guided or outright faked fashion. This presentation will describe the analytical and data processing workflows for evaluating a pilot study of different unifloral honeys. In order to ensure product integrity throughout the study (i.e. to have greater confidence in our unifloral samples), honeys were sourced directly from beekeepers across Europe with associated pollen count metadata. A SYNAPT G2-Si (Waters Corporation, Wilmslow, UK.) was used to acquire the data post UPLC separation of each diluted honey sample. An ion mobility separation was employed before accurate mass ToF MS/MS acquisition The data was processed using a multivariate analysis approach (Progenesis QI, NonLinear Dynamics, Newcastle, UK. and EZInfo, Umetrics, Crewe, UK.) from