The sorting technique has been suggested as a rapid replacement method for the mapping of sensory data (Cartier et al., 2006). Sorting is currently being used in the food industry as a simple and efficient way of gathering important sensory data, mainly to classify samples into different groupings. When using this technique, the panel creates groups of products that are deemed to have similar attributes (Courcoux et
al., 2012; Abdi et al., 2007; Lelièvre et al., 2008). An advantage of this is that, sorting appeals to the
cognitive responses that we use in everyday life, and therefore no previous training is required (Valentin, 2012). So far the sorting technique has been used on a large variety of foods such as cheeses (Lawless et
al., 1995), jellies (Tang & Heymann, 1999) and red wine (Gawel et al., 2001).
Sorting can be simply the grouping of samples based on similarities (Chollet et al., 2011), at which point the researcher can stop the testing entirely as they will have the results they need. However, in many cases this initial grouping step is followed by a step in which the panellists are asked to describe each of the groups they have created, by indicating the relevant sensory terms (descriptors) (Chollet et al., 2011). When sorting samples, the panel are either free to sort as they wish, or are directed to sort the samples based on specific criteria, i.e. sorting according to the sensory quality of the samples or in the answer to a specific question (Valentin, 2012). The first objective when using a directed sorting procedure is to decide whether a trained or untrained panel will be used (Valentin, 2012). When choosing a panel for use with the
Figure 6 Rooibos sensory wheel including 27 terms, developed after the sensory analysis of 69
sorting technique, the researcher must choose the members according to the question that the researcher wishes to be answered. This is well illustrated by research done on wine. The question posed to a panel comprising wine connoisseurs, wine experts (such as wine makers), novices and trained sensory panellists was “whether the wine in front of the panellist is a ready-to-drink wine or whether it needs to be placed in a cellar and allowed to mature over time?” The results showed that the use of an untrained panel, in this particular case, was in fact not the best decision. The novice panellists did not have an extensive wine knowledge as the wine makers did, who unsurprisingly, produced the best results as they were able to answer the question posed to them correctly (Valentin, 2012).
In recent years a substantial body of research has been done on the use of sorting techniques. These have included research on the stability of the sorting maps produced (Blancher et al., 2012), the choice of panel members and panel size (Cartier et al., 2006; Blancher et al., 2012), the sample set size and type of samples suitable for use with the sorting technique (Cartier et al., 2006). The sorting method, as an example of rapid sensory techniques, has also been compared with other rapid techniques (Valentin et al., 2012; Varela & Ares, 2012; Nestrud & Lawless, 2009).
Research has also shown that the sorting technique, when carried out by a trained panel, can result in the production of a product map that is similar to the one that is produced when using DSA (Cartier et
al., 2006). It has been suggested that the sorting procedure should be performed by a trained panel so that
a rough description of the product can be established before continuing with DSA training and testing, as this will save time and therefore money (Cartier et al., 2006). When untrained panellists carry out sorting, the results obtained are reasonably similar to those obtained by the trained panel. This means that when there are time constraints and a trained panel are unable to be sourced, the researchers can use an untrained panel in their place with the achievement of similar results, although they will not be able to continue DSA after the initial sorting, as a trained panel will therefore be needed. In research untrained panels were found to produce consistent product maps over time, which shows that there may not be the need for repetition when using the sorting technique.
Researchers have, however, begun to question whether or not the sorting maps produced from using the sorting procedure are stable enough (Blancher et al., 2012). Although there is the ability to carry out this sensory technique using an untrained panel, Blancher et al. (2012) feels that there is a need to assess the reliability of the sorting. The results of the sorting task are deemed reliable and the map stable if the researchers produce the same, or a similar map when conducting the experiment again. This type of testing would have to be done using the same panel, same stimuli and the same directions given to the panellists (Blancher et al., 2012). Bootstrapping is a technique developed to test the stability of the sorting map and eliminates the need for the panel to reassess the samples, as the variability of the maps can be simulated from within the data (Blancher et al., 2012). This technique draws confidence ellipses around the products on the map, to aid in the interpretation of the results (Dehlholm, et al., 2012), if the ellipses are far apart and do not overlap, the products in question can be regarded as different (Blancher et al., 2012).
RV coefficients, introduced by Escoufier in 1973, are commonly applied to the data obtained from DISTATIS,
in order to evaluate the similarity between two configurations i.e. replications (Abdi, 2007; Louw, 2014). RV
coefficient values are represented between 0 and 1. It has been found in literature that an RV coefficient
from as low as 0.4 up to 0.7 indicates sufficient similarity between bi-plots (Louw, 2014). When faced with a low RV, however, it is important to understand the complexity of the product being tested. Looking at the bi-plots can give further indication of the reason for the low RV values, as well as the reasons for the
similarities between repetitions (T. Næs, Nofima, Norway, May 2014, personal communication).
It has been suggested that the sorting task be used to select products or samples before undertaking another test, such as DSA or consumer testing (Chollet et al., 2011). This way the initial number of samples to be tested can be decreased to include only the samples deemed to be the most appropriate for the study. The sorting technique seems to be a real contender to take over from DSA for the evaluation of certain products. An advantage of using the sorting method it that it does not require a quantitative scoring system and there is no forced agreement amongst the panellists. Another of the main traits contributing to the popularity of this procedure is the rapidity with which the analyses can be performed, which is much faster than the DSA method (Cartier et al., 2006; Abdi et al., 2007). This rapid analysis does not allow for panel fatigue that can often have a major effect on the results (Cartier et al., 2006). The ability to evaluate a larger samples set than DSA, is another aspect that appeals to both researchers and the food industry, as a simple result can be achieved whilst saving both time and money (Cartier et al., 2006; Abdi et al., 2007).
There are, however, challenges in the sorting method as with most other methods available. The number of samples that can be evaluated accurately at one time has still not been definitively decided upon. Cartier et al. (2006) has suggested that the number of samples to be analysed in one batch be limited to between 6 and 15 samples. The fact that all the samples have to be presented at the same time limits the number that can be analysed at once (Chollet et al., 2011). The types of samples and nature of the samples needs to be assessed in detail before using the sorting technique. It has been suggested that if the samples have a delicate and unstable chemical or physical profile then analysis by the sorting technique is not the best choice, due to all the samples having to be presented at the same time. For example a product that needs to remain cool, i.e. ice cream, can become compromised during the analysis. In this case it is up to the researcher to ensure the samples are packaged specially or the conditions surrounding the samples are controlled (Cartier et al., 2006). If this is not possible DSA or another form of analysis will need to be considered. The sorting technique is not recommended when very detailed and precise information is required. The use of the sorting technique gives the researchers qualitative information rather than quantitative information and is not recommended when researchers wish to quantify the differences between products (Cartier et al., 2006; Chollet et al., 2011).
The number of panellists required to carry out the sorting technique and to obtain a stable set of results, is still unknown (Blancher et al., 2012). Chollet et al. (2011) demonstrated that sorting of beer
required more assessors (approximately 20 assessors) than DSA (10-15 assessors). Currently there is still debate as to the recommended number of panellists required to achieve a stable result, and may be dependant of the type of product being sorted.
The use of the sorting technique to determine the shelf-life stability of a product is not recommended. The nature of the test set-up, where samples are positioned relative to one another, is not correct, for shelf life testing, as the goal is not to compare samples to one another, but rather to determine if a product has maintained the required attributes for product freshness (Cartier et al., 2006). When deciding upon an analysis method, researchers need to take into account the products being analysed and the results they wish to obtain before deciding on the method of analysis. Both DSA and sorting have advantages and disadvantages, which allow them to be suited and unsuited to certain tasks.
The sorting technique is a much faster and time saving alternative to the DSA method when the researcher is looking for information that is not necessarily extremely detailed. The way the sorting task can be used to single out samples deemed most appropriate for further testing in detail can be of interest to the rooibos industry. The rapid sorting of rooibos infusions into groups based on the quality of the samples could help graders to rapidly sort production batches. Further analysis of the samples, when more detail is required, can then be performed on the samples that have been grouped into the different groupings according to their similarity in quality or sensory profiles. Table 4 indicates comparisons between the sorting method and the DSA method.
Table 4 Comparison between DSA and the sorting method.
DSAa,b,c,d Sortinge,f,g
Trained panel (10-15) Trained/Untrained panel (20)
Training required No training required
Time consuming Rapid method
High cost Low cost (rapid)
Quantitative data No quantitative data
ANOVA and PCA plots MDS/DISTATIS and CA plot
Complex Easy to understand
Descriptors provided Descriptors not provided (own criteria)
a
Carlucci & Monteleone, 2001; bLawless & Heymann, 2010; cMurray et al., 2001; dPiggott et al., 1998; eAbdi et al.,
2007; fChollet et al., 2011; gCartier et al., 2006; hLelièvre et al., 2008.