Abstract
Beer is the most consumed alcoholic beverage globally, and in recent years, craft beer has gained significant popularity. Hops play an influential role in beer's aroma and flavour characteristics. New Zealand (NZ) is renowned for producing high-quality hop cultivars with distinct aroma characteristics that are well-suited to craft beer. Hops are primarily grown in the Tasman region, but due to the recent higher demand for craft beer, hop cultivation has expanded into other areas of NZ. Although NZ-grown hops account for only 1-2% of the global hop supply, they are highly export-driven, with approximately 85% exported overseas.
Given the rising prevalence of food fraud, particularly geographical origin fraud, the NZ hop industry may be at risk for fraud due to its high value, distinctiveness and strong export focus. Geographical origin fraud occurs when a product is mislabeled and falsely marketed as originating from a specific origin. The consequences of such fraud could include reputational damage and revenue loss. Consumers are increasingly demanding transparency regarding product origins and are drawn to single-origin products, which are valued for certain qualities associated with a specific location. As a result, there is a growing need to develop tools for authenticating geographical origin to safeguard and future-proof the industry. Additionally, with the recent expansion of hop cultivation into different regions within NZ, it is essential to understand how growing location influences hop aroma and composition.
This thesis explores the feasibility of various analytical methods that could be applied in the industry to help protect hops from geographical origin fraud through chemical analysis. Furthermore, it investigates hops' volatile and sensory characteristics from different origins, contributing valuable knowledge about how hop characteristics differ based on origin.
The traceability methods investigated included isotope ratio mass spectrometry (IRMS), spectroscopy methods (near-infrared (NIR) spectroscopy and hyperspectral imaging (HSI-NIR)), and volatile analysis using headspace solid-phase microextraction gas-chromatography mass-spectrometry (HS-SPME GC-MS). A sensory sorting method with descriptors and GC-MS analysis was applied to the same samples to evaluate their volatile and sensory characteristics in relation to origin.
The traceability methods were examined using two sample sets from the 2023 harvest. The farm level sample set included three cultivars from eight farms within the Tasman region. The regional sample set included six cultivars from two regions in NZ, namely Tasman (Motueka) and Central Otago (Clyde). Farm level samples from the 2024 harvest were also included to assess seasonal variation.
The first study examined the environmental conditions across all sample origins, including weather, agronomic factors and processing methods. Subsequently, the stable isotopes of hydrogen and oxygen were analysed to assess their potential for distinguishing between different origins. Hydrogen and oxygen could not discriminate at the farm level within the same geographical region. However, the oxygen isotope values could discriminate between the two regions (Tasman and Central Otago).
The second and third studies focused on rapid spectroscopic methods, NIR and HSI-NIR. These methods demonstrated the potential for distinguishing between farms when cultivars were analysed independently. The most accurate farm level model was from HSI-NIR, recording 95.83% of cross-validation samples correctly classified in both the Nectaron and Nelson Sauvin cultivars. Additionally, the regional classification of all cultivars illustrated potential when latent variables (LV) 1 and 3 were visualised. In the fourth study, GC-MS analysis also showed potential for farm and regional classification based on analysis of the volatile fraction. Although cultivar primarily influenced the groupings based on genetics, similar results were observed, as seen in the rapid methods for regional classification, with regional groupings observed when LV1 and LV3 were plotted.
Key similarities and differences in hop volatile profiles were explored to investigate origin specific markers. Variable identification analysis (VID) showed correlations between compounds and origins. However, no single compound defined the sample origin, but each sample showed a distinctive volatile fingerprint.
Since it was unclear whether these chemical differences, identified by GC-MS, were perceivable by humans, a rapid sensory sorting method with descriptors and GC-MS was applied to analyse two cultivars from farms within the Tasman region. The sensory experiment identified descriptive differences in each cultivar based on origin. Integrating sensory and chemical data provided a more informative characterisation of hop samples. For example, two nearby farms in Moteuka (Mac Hops and Northwood Hop farms) were perceived similarly in the Necatron cultivar (associated with the compound 2-methyl-3-buten-2-ol, and the descriptors “sweet” and “floral”) but were described differently in the Superdelic cultivar.
Rapid spectroscopic techniques show the most promising potential for practical implementation in the NZ hop industry due to their sensitivity, cost-effectiveness, and speed. However, if volatile information is also desired, GC-MS could provide both fingerprinting for traceability and useful volatile profiling to indicate quality and potential aroma properties. The findings of this thesis provide a guide for the NZ hop industry for selecting appropriate geographical origin authentication methodologies. If traceability measures are to be implemented, the industry must invest in building a robust and representative sample database using the chosen method. This research also contributes to the limited understanding of how hops' volatile and sensory characteristics differ by growing location, relevant to breeders, growers, brewers and marketers.