Don’t get stung by your Manuka Honey!

One of our Waters colleagues, Dr. Joanne Connolly, gave an interesting presentation at the 38th BMSS Annual Meeting 2017 in Manchester last month. The research involved the use of Progenesis QI in a non-targeted metabolomics approach to honey analysis and floral marker elucidation. Previous blog posts have discussed how Progenesis QI was used to detect food fraud across a wide diversity of foods in an untargeted approach.

The problem

There have been a number of food scandals in recent years, the more serious resulting in fatalities.

Why do people adulterate food?

The main reason is for financial gain, to make the key ingredient ‘go a bit further’, for substitutions, or to cut manufacturing costs.  Sometimes, it is worse with deliberate maliciousness such as reputation damage – trying to destroy a competitor’s reputation or even terrorism.

Either way, laboratories need to develop ways to test food products in an untargeted way, to explain the differences between genuine and fraudulent products.

An example: Manuka and commercial honey

Every honey is a unique, complex matrix made up of plant and bee secondary metabolites including flavonoids, phenolics and sugars.  Each honey has its own “fingerprint” which will differ depending on region, forage targets and biological properties.

A lot of honey found commercially has honey from several plant species and is known as polyfloral or multifloral honey. The term unifloral describes a honey that is derived from one plant species, and unifloral honey is becoming of more commercial interest as consumers appreciate the possibility to choose between different honey types.

Leptospermum scoparium Leptospermum scoparium

In addition to this, there have been studies discussing therapeutic or technological uses of certain honey varieties which also contributes to the demand of a reliable determination of their botanical origin. Some of the unifloral honeys are sold at premium prices, and are therefore a target for food fraud to occur (e.g. adulteration, mislabelling).   Manuka honey falls into this group.  Manuka honey is made from the nectar of Leptospermum scoparium or Manuka bush, a shrub native to New Zealand and Southern Australia.  It is reported to have biocidal activity and a unique antibacterial non-peroxide activity (NPA).  Suppliers have to demonstrate this activity for labelling and to attract premium price.

Current approaches

Understanding, deconvoluting and identifying the biochemical profile of a food sample of interest can help give manufacturers and regulators key information in the fight against fraud. Many different analytical techniques were used to determine the floral origin of honey, including MS, NIR, FT-IR, and Raman spectroscopic fingerprinting, and NMR.

Another possible approach is untargeted metabolomics, as hinted at earlier.

Identification of a MS-derived biochemical “fingerprint” is an important tool for understanding the question of “What is normal?”

Where does Progenesis QI fit in?

In this recent application note, an LC-MS metabolomics approach was taken to chemically profile four different types of unifloral honey.  Progenesis QI was used in untargeted analysis of LC-MS data (HDMSe in ESI+ and ESI- modes) to find candidate biomarkers for Manuka honey when compared to the other mono-floral honey types (Buckwheat, Heather and Rape). Each sample type was run in triplicate, plus pooled QCs.  After importing the data into Progenesis QI and processing it through the unique Progenesis alignment and co-detection workflow, PCA analysis showed Manuka was clearly separated.

Principal component analysis Principal component analysis (PCA) scores plot from EZinfo (ESI negative ion HDMSE data).

By automatically exporting data to EZinfo for discriminate analysis, it was then possible to extract the best candidate biomarkers from an S-plot of Manuka honey versus all other honey types. Tentative identifications were also generated for several of the extracted potential biomarkers. MRM was used to validate that the peak identified as Leptosperin really did differ in abundance between Manuaka and non-Manuka honey.

Manuka markers Review of standardized abundance profiles and assignment of identity for three markers of Manuka honey as displayed in Progenesis QI software.

In summary

Food authenticity, adulteration and safety is a major concern across the globe.  A non-targeted high resolution MS OMICS approach combined with multivariate data analysis can identify ‘normal’ profiles of foodstuff allowing detection of fraud during the investigative stages.  It is possible to get biologically meaningful information by comparing multiple samples using an all-in-one high-throughput guided workflow in Progenesis QI.  Confident structural assignment in Progenesis QI means markers can be annotated and identified by databases of user choice.  Combining ion mobility with MS gives ‘cleaner’ fragmentation data allowing easier identification of markers.  Validation of markers is important using complimentary technology for confirmation.  New innovative rapid evaporative techniques will open new doors into authentication techniques at “point of entry”.

Are you doing untargeted LC-MS analysis? Would you like to see how the Progenesis QI software can help you get the results you are looking for?

You can download the software or contact us. A member of our friendly team can discuss the software further with you.

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