Evaluation of Polarized Projective Mapping as a possible tool for attributing South African Chenin blanc dry wine styles
Abstract
Multiple Factor Analysis (MFA) According to the Chenin blanc Association of South Africa, there are three recognized dry wine styles, Fresh and Fruity (FF), Rich and Ripe Unwooded (RRU), and Rich and Ripe Wooded (RRW), classically attributed with the help of sensory evaluation. One of the “rapid methods” has drawn our attention for the purpose of simplifying and making style attribution for large sample sets, evaluated during different sessions, more robust. Polarized Projective Mapping (PPM) is a hybrid of Projective Mapping (PM) and Polarised Sensory Positioning (PSP). It is a reference-based method in which poles (references) are used for the evaluation of similarities and dissimilarities between samples. Panelists are presented with “free-moving” products to arrange around the poles, according to similarities and dissimilarities, to create a 2D product map. Additionally, the judges give a description of the samples, generating a short list of attributes. Our approach to testing this method was to first establish the poles using PM, then test the model using PPM with samples that were either known (used in the PM session and that contributed to the choice of poles) or unknown. The sample set consisted of 18 commercial Chenin blanc wines, vintages 2013 and 2014, from the three representative styles, chosen according to the tasting notes description. Four PPM experiments were performed. The poles were kept constant among the PPM experiments, while different combinations of “free-moving” wines were evaluated to test the consistency of product groupings. In all tasks sensory descriptors were generated. For each session 15 judges were recruited. Each judge repeated the exercise after a 15 minute break. For PM the sample set consisted of 12 samples (9 wines, 3 of them duplicates). For PPM, the sample sets also had 12 samples, with one of the poles and one other sample duplicated. The PPM sessions were organized as follows: PPM1 same samples as PM, PPM2 and PPM3 half known and half unknown samples, and PPM4 only unknown samples. The data generated was evaluated statistically by means of multiple factor analysis (MFA). Multiple factor analysis (MFA) on the individual tasks showed in the PM and all four PPM tasks, the RRW group separated most clearly from other wines and blind duplicates of this style grouped well together. The FF and RRU styles grouped less consistently from one task to another and blind duplicates were not grouped as closely to one another. MFA results comparing all four PPM experiments showed good repeatability in grouping of wines among the separate sessions, especially for wooded wines. New rapid methods provide significant cost benefits for the wine industry and researchers. PPM may be a useful tool for researchers to apply in the analysis of large sample sets of wines.
Issue: Macrowine 2016
Type: Poster
Authors
*Stellenbosch University