El Sistema Tributario Peruano

Instructed sorting was conducted using samples from the two production regions, i.e. Western Cape and Northern Cape. Both sample sets had to be sorted by a panel of expert judges in consecutive sessions according to aroma, as well as palate attributes associating with high quality, medium quality and low quality rooibos. All analyses were replicated to test for consistency of results.

According to Abdi et al. (2007), DISTATIS plots are able to show the similarities and differences between samples based on how they are grouped during the sorting task. DISTATIS is a combination of the statistical methods MDS and STATIS, although DISTATIS, in comparison to MDS, allows for individual panellist data to be taken into account (Abdi et al., 2007). The results obtained allows the researcher an opportunity to view the manner in which the panellists view the similarities or dissimilarities between products, and the latter usually provide further data for more targeted data analyses. Therefore once the samples are grouped and a DISTATIS plot is drawn up, the resulting clusters can be determined. The distances between the samples are a representation of the similarities between the samples. The closer

the samples, the more similar the samples are thought to be and the further apart the samples, the more different the samples tend to be.

The respective DISTATIS plots (Fig. 1 and Fig. 2) produced from the sorting of the rooibos samples from the Western Cape and Northern Cape based on aroma quality yielded similar results in terms of the overall outcome. In both cases it was clear that there was a separation along the 1st dimension, separating the high quality samples from the low quality samples. With the assistance of Ward’s cluster analysis, using at least four dimensions, it was possible to verify the sample groupings and therefore substantiate the split between the samples based on overall aroma quality. By including more dimensions in Ward’s cluster analysis, the correct groupings of the samples can be determined, as certain correlations/relationships between samples are lost when only looking at the samples on a two-dimensional DISTATIS plot. Ward’s cluster analysis combines similar objects together, ensuring that the overall within-cluster variation is kept to a minimum (Mooi & Sarstedt, 2011). The clusters obtained can be determined from a hierarchal dendrogram or tree diagram. When determining the number of clusters, it is important to remember that knowledge of the product in question is important, as this can help determine whether the number of clusters obtained make sense (Mooi & Sarstedt, 2011). In this study Ward’s cluster analysis helped to verify the groupings of the rooibos samples, and therefore indicate the similarities/dissimilarities between them. From the groupings it was clear that the high quality and low quality samples were not similar as they were situated far apart on the DISTATIS plots. The medium quality samples, on the other hand, were found to associate with the low quality samples, but in more instances with the high quality samples. It was expected that the medium quality rooibos samples would not be easily discernible from the other two categories of rooibos quality due to the mixed nature of its aroma profile, i.e. a mix of positive and negative aroma attributes.

With the inclusion of the descriptive task, it was possible to determine the reason behind the grouping of specific rooibos samples sourced from both production areas using CA plots. Although the CA plots appear to be similar to the DISTATIS plots, the DISTATIS plots do not take the inclusion of the descriptors into consideration. The high quality samples from both the Western Cape and Northern Cape were seen to associate with the positive aroma attributes “fynbos-floral”, “rooibos-woody”, “honey” and “caramel”. For the samples from the Northern Cape it was also found that, in addition to the attributes already mentioned, the attributes “fruity-sweet” and “apricot” aroma also associated with the high quality rooibos samples. In contrast, the low quality samples were seen to associate with the negatively associated aroma attributes. These include the “green grass/plant-like”, “rotting plant water”, and “musty/mouldy” and “hay/dried grass” aroma attributes. Several of the low quality Northern Cape samples also tended to associate with a “medicinal” aroma, a negative aroma attribute.

In view of the above, instructed sorting seems to be viable when rooibos samples need to be categorised as low quality or high quality based on aroma quality, especially when the sorting task is accompanied with a descriptive step. Our results have shown that the sorting task is, however, less

effective when medium quality samples need to be clustered based only on overall aroma quality. In order to correctly categorise these samples based on quality, and the inclusion of the sorting task relating to the taste and mouthfeel quality of the samples, may give greater insight into the sample quality, as these attributes can be indicators of quality.

In contrast with the above, it was not as easy to determine the overall palate quality of rooibos using the sorting task. The present study (Chapter 3) and Koch et al. (2012) showed that, because of the low intensities of the flavour attributes and very little variation in the taste and mouthfeel intensities within rooibos, it is often not easy to recognise these attributes or to distinguish between samples. It is thought that this may have influenced the results from the sorting task (Fig. 3 and Fig. 4). Although there is a separation between the high quality and low quality samples based on the palate attributes, the split is not as clearly defined as for the aroma quality, indicating that the panel had difficulty in grouping the samples. With the low intensities of palate attributes, it is often not possible to pick out a defining attribute, making it hard to profile the sample.

Overall it can be said that it is possible to use the sorting method to rapidly separate samples of a high quality and samples of a low quality, based to a greater extent on the aroma attributes and a lesser extent on the palate attributes. It is therefore possible to consider the sorting task as a tool to aid grading, based mainly on the aroma quality of the rooibos. The focus of quality grading needs to be concentrated on the aroma quality and less on the palate quality, due to the low intensities of the flavour attributes making interpretation difficult. The taste and mouthfeel attributes, although similar in their intensities, can be an indicator of quality and therefore if used in combination with the sorting based on aroma quality, could provide better grouping of medium quality samples. By being able to split the samples rapidly into high and low quality groupings, the grader will be able to screen the samples prior to further analysis, thereby speeding up the overall grading process. In order to ensure consistency, the grader can sort the samples according to a selected list of aroma attributes from the sensory lexicon or sensory wheel (Chapter

3). These attributes should be characteristic/defining of a high quality or low quality rooibos aroma. The

list of aroma attributes could include intensity scores i.e. “very”, “a little”, “medium” and “not”, which would allow the graders to quantify the attributes and therefore be better equipped to place the samples into different quality groupings (Lelièvre et al., 2008; Chollet et al., 2011). This will be especially helpful for the placement of the medium quality samples, which are hard to categorise because of the similarity these samples have with both the high and low quality samples. Once the screening has been done, the grader will now have an insight into the aroma quality and possibly the overall quality grading, before beginning further analyses.