Using results from a large number of studies including a wide range of sample characteristics, the minimum number of consumers can be determined as the minimum number that provides stable sample configurations. For each study the average RV coefficient across simulations is determined for different number of assessors and the number required for obtaining an average RV coefficient of 0.95 is determined (Figure 2). This approach has been used for making recommendations on the minimum number of consumers needed for sorting tasks [22••], CATA questions [23] and projective mapping [25]. Despite the potentialities of this approach

for evaluating reliability it is still necessary to evaluate other parameters to evaluate the similarity between sample configurations. In particular, it is important to stress that the RV coefficient depends PD-0332991 manufacturer on the number of samples considered in the study [26] and

therefore it might not be the best parameter for evaluating the similarity of sample configurations. An alternative would be to use the RV2 coefficient, as stressed by Tomic et al. [17]. Another important issue that deserves further research is the threshold considered for determining that sample configuration MAPK inhibitor is stable. As an example, Vidal et al. [25] reported that changing the RV coefficient from 0.95 to 0.90 strongly changed conclusions on the stability of sample configurations but did not decrease sample discrimination. In closing this section it is interesting to highlight that additional MycoClean Mycoplasma Removal Kit statistical tools can be used to evaluate the stability of sample configurations. The adjusted Rand index has been recently proposed to evaluate the agreement of partitions of a set of samples in a sorting task [27]. This statistical tool can be extended to evaluate the stability of sample grouping obtained using cluster analysis on sample coordinates on the configurations gathered with different rapid methodologies. Perhaps one of the most important challenges regarding new methodologies for sensory characterization

is identifying their limitations. It is clear that these methodologies are not a replacement of classic DA with trained assessors. However, it has not been clearly established yet in which situations new methodologies provide equivalent information to DA and when they their application is not recommended if high quality detailed information is sought. Several studies comparing sensory characterizations obtained using DA with trained assessors and new methodologies with non-trained assessors have been performed using samples that show large or medium differences among them 28 and 29. In this sense, studies focusing on the effect of sample complexity and the degree of difference among samples on the discriminative ability of new methodologies are still lacking.