Performance of flash profile and napping with and without training for describing small sensory differences in a model wine

Research output: Contribution to journalJournal articleResearchpeer-review

  • Jing Liu
  • Marlene Schou Grønbeck
  • Rosella Di Monaco
  • Davide Giacalone
  • Bredie, Wender
Rapid sensory methods are a convenient alternative to conventional descriptive analysis suitable for quickly assessing sensory product differences. As these methods gain in popularity, assessments of their discriminability and reproducibility in food applications are increasingly needed. Moreover, it is of interest to explore whether small adjustments to the existing protocols could improve the results. In this study different variations of two rapid sensory methods, one based on holistic assessment – Napping, and one based on attribute evaluation – Flash Profile, were tested for the evaluation of the flavour in wine. Model wines were developed with control over the sensory differences in terms of sensory characters and sensory intensities (weak to moderate). Some modifications to the classical Napping and Flash Profile protocols were employed in order to improve discriminability, repeatability and accuracy. The results showed that conducting Napping with a panel training on either the method (training on how to arrange samples on the sheet) or the product (familiarisation with the sensory properties of the wines) improved the outcome compared to the classical Napping protocol. The classical Flash Profile protocol and its modified version including a Napping with subsequent attributes generation as the word generation step and limiting the number of attributes for ranking gave a similar sample space. The Napping method could best highlight qualitative sample differences, whereas the Flash Profile provided a more precise product mapping on quantitative differences between model wines.
Original languageEnglish
JournalFood Quality and Preference
Issue numberA
Pages (from-to)41-49
Number of pages9
Publication statusPublished - 2016

ID: 143850713