Using chemical and DNA marker analysis to authenticate a high-value food, manuka honey

Abstract

Ensuring the authenticity of food is a rapidly emerging issue, especially in regard to high-value products that are marketed through increasingly complex global food chains. With the ever-increasing potential for mislabeling, fraud and adulteration, governments are increasingly having to invest in, and assure, the authenticity of foods in international trade. This is particularly the case for manuka honey, an iconic New Zealand food product. We show how the authenticity of a specific type of honey can be determined using a combination of chemicals derived from nectar and DNA derived from pollen. We employ an inter-disciplinary approach to evaluate a selection of authenticity markers, followed by classification modelling to produce criteria that consistently identify manuka honey from New Zealand. The outcome of our work provides robust identification criteria that can be applied in a regulatory setting to authenticate a high-value natural food. Our approach can transfer to other foods where assurance of authenticity must take into account a high level of natural variability.

Food authentication: combining markers to help verify a specialty honey

High value speciality food products are threatened by mislabeling, fraud, and adulteration, which calls for practical authentication measures. Research led by the Ministry for Primary Industries in New Zealand was used to develop an analytical strategy that combines four chemicals and a DNA marker to authenticate mānuka honey, a speciality export from New Zealand. They found that a combination of chemicals from the mānuka plants nectar and species-specific DNA from mānuka pollen could be used as authenticity markers, when tested against a variety of plant and honey samples. Based on quantitative measurements of the markers, they used a classification modelling method to establish the identification criteria for both monofloral and multifloral mānuka honey. Combining multiple marker data and statistical classification analysis may also help develop other food authentication measures.