Balanced incomplete block design: an alternative for data collection in the optimized descriptive profile

Abstract

The optimized descriptive profile (ODP) is a new descriptive methodology which proposes an optimized sample evaluation by no trained assessors. To permit that assessors with a low degree of training quantitatively evaluate samples consistently, this method proposes the presentation of reference materials ("weak" and "strong") together with all set of samples at the time of evaluation. The attribute-by-attribute protocol is recommended on the ODP technique to allow for comparison among the samples and avoid fatigue, since only one attribute is evaluated at a time. Simultaneous presentation of the samples, together with the reference materials, is the "key point" of the ODP, and therefore the number of samples may be a limiting factor in this technique. The purpose of the present study is to determine if the balanced incomplete block design (BIBD), used in descriptive techniques traditionally, may be an alternative for data collection on this specific methodology, the optimized descriptive profile (ODP). The BIB design does not allow for comparison among all samples together, but only among a fraction of them. Therefore an assessment of the data quality obtained through this design is necessary to increase the applicability of the ODP. In this experiment a specific set of grape juice formulations was used, which presented variations in pulp and sugar contents. Sensory evaluation was conducted with two independent panels, where each panel evaluated the samples using the ODP protocol in a specific design. Thus, one panel evaluated the samples using the balanced complete blocks design (BCBD) and the other panel the balanced incomplete blocks design (BIBD). The data obtained by the two designs were compared in relation to analysis of variation, F-test power and regression models. Data obtained by the ODP conducted in the BIBD was very similar to that of the ODP in the BCBD. In the ANOVA, the formulations differed (p < 0.05) in relation to all attributes, showing a test power (1 - β) greater than 0.95. The regression models obtained in the different designs indicated overlapping of the confidence intervals (CI 95%). Therefore, the BIBD presents itself as an alternative for data collection in the ODP without loss in quality of the results.