1. 𝑢= 𝑢 𝐸 2 + 𝑠 𝐿 2 + 𝑠 2
Statistical Analysis of Metabolite Data for the Determination of Manuka Honey Authenticity
Adrian J. Charlton, Roy MacArthur, James A. Donarski, Michael Dickinson, Terry Braggins, Jacob Jaine & Tony Wright
This Is Manuka Honey, Auckland War Memorial Museum, Auckland, NZ, 9th August 2016

2. Fera Science Ltd.
A leading supplier of scientific solutions, evidence and advice across the Agri-Food supply chain in Europe – “from farm to fork”.
Joint venture between UK government and Capita

3. Profiling of Manuka honey (2008)
1H NMR and high resolution mass spectrometry.
Upper trace: Non active manuka (kanuka?)
Centre trace: Non-manuka
Lower trace: Active (5+) Manuka
Origin biomarker?
Not present in database of EU samples
Active ingredient
(methylglyoxal)
Active ingredient precursor
(Dihydroxyacetone)
Simultaneous screening of origin, authenticity & activity

4. Honey authenticity

5. Non-peroxide activity testing
Traditional bioassay using Staphylococcus aureus ATCC 9144
25% w/v honey
in water (TA)
in catalase (NPA)
Phenol controls 1% - 7%
Incubation at 37°C 20h
Diameter of zones of inhibition measured
Results expressed as phenol equivalent.
Using Staphylococcus aureus ATCC 9144 commonly used for antibioitic assays and sensitivity testing of compounds.
Plate poured with agar inoculated with a known level of active bacteria, set and fridge. Punch wells aseptically and 0.1ml to each well of test material. Use phenol controls 1% to 7%, blank controls and add honey to water for total activity and to catalase for NPA. Catalase enzyme breaks down h202 into hydrogen and water therefore any activity comes from other components, hence the umf.
Incubate the plate at 37°C overnight then measure the diameter for the zones of inhibition which are shown as clear rings around the wells. This is caused by the bacteria in the agar growing except where they are inhibited by the material in the well that diffuses through the agar.

6. Methylglyoxal (MGO)
The major active ingredient and a chemical marker of Manuka honey
Effective antimicrobial agent
The higher the MGO level, the greater the antimicrobial activity
MGO is formed from Dihydroxyacetone (DHA)

7. Methylglyoxal C3H4O2 M.Wt 72.062 Dihydroxyacetone C3H6O3 M.Wt 90.078
What’s the problem with MGO and DHA as markers of Manuka honey authenticity?
Methylglyoxal
C3H4O2 M.Wt
Dihydroxyacetone
C3H6O3 M.Wt
Both compounds are very simple and easy/cheap to make from sugar. DHA is mass produced as an ingredient for fake tan

8. How much is the DHA* in a jar of Manuka honey worth?
DHA sells for about NZ$ 58 per Kg
A typical Manuka honey may contain 300 mg/Kg of DHA
At this concentration, a 1lb jar of Manuka honey contains 136 mg of DHA
So the DHA in the jar is worth:
NZ$ 58 x 136/1,000,000
NZ$ = Very cheap to adulterate!!!
Conclusion: We need some better markers of Manuka honey authenticity
*Similar calculations can be made for MGO

9. Metabolomics
Non-targeted profiling / Fingerprinting Statistics & Chemometrics Database searching & structure elucidation Mapping & interpretation

10. LC-HR-MS Metabolite Data from Manuka Honey
High resolution LC-MS data were provided
Accurate masses and retention times
Positive and negative ion intensities

11. Spectral fingerprinting
Computationally intensive
Data handling and
bioinformatics tools required
Multivariate Statistics
Spectral fingerprint
Univariate Statistics
Artificial intelligence

12. Aim
Each method was applied to address the same question. Does the dataset contain variables relating to potential biomakers that can differentiate clean Manuka from clean Kanuka honey?

13. Genetic programming
An artificial intelligence algorithm that was developed at Fera which finds combinations of variables that can successful classify data into predefined groups
Genetic programming found many variable combinations that could classify the honey with100% success.

14. Classification trees (100% success)

17. Chemometrics
Analysis was split using the positive ion and negative ion data separately.
Data were filtered to remove degenerate data points (within 5ppm and RT 0.3%)
Normalised each variable by median response across all samples and controls
15,033 (neg ion), 17,354 (pos data) variables
Classification rates 98-99% using decision trees and support vector machines.

18. Top contributing variables
Negative data

19. Top contributing variables
Positive data

20. Data plots (1 = kanuka 2 = manuka)

21. Data plots (1 = kanuka 2 = manuka)

22. Summary of analysis
14 data points were identified that had the potential to be powerful indicators of the authenticity of Manuka honey
These results were entirely consistent with work undertaken independently at Analytica
From the markers highlighted, 4 were chosen for evaluation as markers of Manuka honey authenticity

23. Statistical analysis - Remit
Provide assurance that the sample contains honey that has been produced under conditions in which it can be expected to be derived wholly or mainly from the nectar of the Manuka bush.
Identify samples which appear to be inconsistent with having been produced under conditions in which they can be expected to be derived wholly or mainly from the nectar of the Manuka bush.

24. Samples
604 Samples

25. Data cleansing
464 samples classed as:
Manuka or non-Manuka
Clean or Commercial

26. Classification rates Lepteredine 4 markers combined Leptosperin
Best balance is probably: ca 15% false positive &
ca 5% false negative
Ideal space
Low false
-ve
Low false +ve
False +ve: non-Manuka classified as Manuka
False -ve: Manuka classified as non-Manuka
POBA
We are in a good, if not an ideal space due to low false –ve rate
2-methoxyacetophenone

27. Leptosperin Threshold?
Percentage of Samples
Leptosperin Threshold (mg kg-1)
Manuka (n=220)
Non-Manuka (n=135)
>100 (Wholly or Mainly Manuka)
94.1%
2.2%
>50 and <100 (Manuka Blend)
5.9%
11.9%
< 50 (Non-Manuka)
0.0%
85.9%

28. Statistical models
Generalised from real data using statistical distribution models and measurement uncertainties (errors)
Ranges and therefore errors include variability introduced into the data by factors such as: collection location, vintage, analytical error
Only leptosperin presented as variability in other markers was higher so ranges are wider
Errors and ranges are best reduced and threshold refined by analysing more samples of known provenance

29. Calculated threshold 99.5 mg/kg - Leptosperin
For a threshold at 99.5 mg/kg Leptosperin somewhere between 84 and 94% of New Zealand Commercial “Other Honeys” will be identified as non-Manuka and between 85 and 95% of Manuka honeys will produce a result above this threshold.

30. Calculated threshold 63 mg/kg - Leptosperin
For a threshold of 63 mg/kg Leptosperin we estimate that between 72 and 85% of New Zealand Commercial “Other Honeys” will be identified as non-Manuka and between 92 and 99% of Manuka Honeys will produce a result above this threshold.

31. Combinations of markers
The use of the four markers combined into a single score, provides very similar estimates of performance.
Using all of the LC-MS data for classification does give better results but is much more difficult to implement routinely and emphasises yearly differences in the data sets

32. Vintage PCA plots shows the difference between years
Leptosperin is very stable between the 2 years > 5% difference

33. Conclusions Leptosperin is a good marker of Manuka honey
It’s complicated so not easily or cheaply made
Other identified markers are also robust but are maybe best used to improve confidence as they do not improve the classification rates over Leptosperin alone

34. Future work
Investigate use of multiple markers to improve certainty of classification
Ongoing mining of data set to identify potential new markers and marker combinations
Advise over the use of ongoing testing to understand natural variability in the concentration of markers
Participate/establish inter-laboratory studies to understand analytical variability

35. Fera Colleagues UMFHA Analytica Comvita University of York
Acknowledgements
Fera Colleagues UMFHA Analytica Comvita University of York

36. Thank you for listening!

37. Pollen Analysis vs NGS
NGS
Pollen analysis
Pollen analysis is subjective and in this case does not truly reflect the floral types in honey