Suchergebnisse

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.

A Bayesian approach was developed by Hald et al.(1) to estimate the contribution of different food sources to the burden of human salmonellosis in Denmark. This article describes the development of several modifications that can be used to adapt the model to different countries and pathogens. Our modified Hald model has several advantages over the original approach, which include the introduction of uncertainty in the estimates of source prevalence and an improved strategy for identifiability. We have applied our modified model to the two major food‐borne zoonoses in New Zealand, namely, campylobacteriosis and salmonellosis. Major challenges were the data quality for salmonellosis and the inclusion of environmental sources of campylobacteriosis. We conclude that by modifying the Hald model we have improved its identifiability, made it more applicable to countries with less intensive surveillance, and feasible for other pathogens, in particular with respect to the inclusion of nonfood sources. The wider application and better understanding of this approach is of particular importance due to the value of the model for decision making and risk management.