# Comparison of Three Approaches to Assess the Flavour Characteristics of Scotch Whisky Spirit
## 4 Discussion  
The aim of this study was to compare two sensory approaches (QDA and Napping) and an analytical technique (GC-MS) for the evaluation of the flavour differences among a set of new make Scotch whisky spirits produced in the laboratory using different yeasts. The method of production and used yeast resulted in variable ABVs. HMFA was used to explore the relationships between the data produced using the three methods. The results of this analysis showed that the three techniques grouped the spirits in a similar way with RV scores between 0.895 to 0.927. QDA discriminated the low ABV spirits better while GC-MS discriminated the higher ABV spirits more effectively. All three techniques discriminated the spirits in the same way. However, Napping had a lower resolution for distinguishing spirits from each other. The three methods are not directly interchangeable, as the data from each provides a unique insight into how the spirits differ. There are also advantages and disadvantages associated with each method, which we have compiled based on existing knowledge and from experience gained from this study. Sensory methods give a measure of human perceptions and hence are relevant to the consumer. However, sensory evaluation can be time consuming and even with trained panellists the data are noisy. QDA is an established sensory method, where spirits are evaluated in terms of perceived intensity of pre-selected flavour attributes. The advantage of this approach is that flavour profiles can be produced for each spirit. These data can be presented visually in a form that is easy to understand and allows spirits to be easily compared. Differences can be confirmed through statistical evaluation of the data. The main downsides of QDA are the requirement of trained and experienced panellists [15], and the time required to evaluate each individual flavour descriptor. Using a pre-selected vocabulary may result in important flavours being be overlooked. This is a particular concern when using QDA to evaluate novel products, such as the spirits produced from new yeast strains used in this study. The descriptor _nutty_ was used frequently during Napping but had not been included in the QDA vocabulary. The solution to this would be to carry out a preliminary session to fine-tune the vocabulary. However, this would add to the time and volume required to carry out the QDA which are the main limiting factors when conducting sensory work. This study showed that Napping was significantly less time consuming than QDA, taking around a third of the time to carry out although having a lower resolution in distinguishing the spirits. Similarly, in a study by [PERSON] et al. [43], the time was decreased to nearly one third. Although QDA requires trained panellists, this is not mandatory for Napping. This significantly reduces resource requirements, if no pre-trained panel is available, but the data are likely to be noisier. Although not studied here, familiarity with the products and their experience in describing flavours could also reduce the number of panellists required to analyse a sample set due to an increase in the quality of their evaluations [19; 43; 45; 46; 47; 48; 49]. Although considerably more rapid, Napping resulted in a similar product map to QDA. Other studies comparing Napping to descriptive techniques showed similar results in most cases [18; 27; 28; 29; 30; 31; 32; 33; 34; 35; 50; 51; 52]. With Napping the evaluation is based on the main characteristics of the sample, rather than full evaluation of single flavour descriptors, so subtle differences may be overlooked. This may depend on the nature of the samples. Some studies show that Napping is the best approach when the samples are very different, with QDA being more efficient for picking up more subtle differences between samples [53]. However, there have also been studies that have found a poor correlation between the techniques [54]. Unlike most of the previous research, our study used the same panel in