Sensory panel consistency during development of a vocabulary for warmed-over flavour
Byrne, D.V. ; O’Sullivan, M.G. ; Dijksterhuis, G.B. ; Bredie, W.L.P. ; Martens, M. - \ 2001
Food Quality and Preference 12 (2001)3. - ISSN 0950-3293 - p. 171 - 187.
Cooking temperature - Generalised Procrustes Analysis - Pork meat - RN gene - Sensory vocabulary development - Warmed-over flavour
A sensory vocabulary of 20 terms each with a corresponding reference material was developed over 7 sessions using pork patties derived from the meat of carriers and non-carriers of the RN- gene. Patties were oven-cooked at 150 and 170°C and chill-stored for up to 5 days to facilitate warmed-over flavour development. Generalised Procrustes Analysis (GPA) was used to investigate sensory terms and their individual use by panellists over the sessions. GPA explained variance indicated that the final vocabulary displayed a similar amount of information to that of the initial vocabulary of 42 terms. Individual panellists scale use was found to converge over the sessions. Panel agreement on many odour and flavour terms appeared to be enhanced as term synonyms were removed in vocabulary development. Sample discriminability decreased from sessions 1-4, where term concepts were verbally communicated to the panel. Term reference introduction in session 5 caused a levelling in sample discriminability and a reduction in agreement, most likely related to perceptual confusion. Subsequently, references enhanced both discriminability and agreement. Thus, it may be more useful to introduce reference materials earlier, if not in the first session, of the vocabulary development process.
Power of experimental designs, estimated by Monte Carlo simulation
Martens, Harald ; Dijksterhuis, Garmt B. ; Byrne, Derek V. - \ 2000
Journal of Chemometrics 14 (2000)5-6. - ISSN 0886-9383 - p. 441 - 462.
Analysis of effects - ANOVA - Experimental design - Monte Carlo simulation - PLS - Power estimation - Regression - Sensory science - Significance - Warmed-over flavour
What is the optimal size of an experiment? How should the practical experimenter determine this optimal experimental size? The paper presents a conceptually simple method for estimating the statistical power of experimental designs, based on Monte Carlo simulation. In the planning stage of a project, several alternative experimental designs may thereby be compared with respect to their ability to balance the risk of committing Type I and Type II errors against the cost. The Monte Carlo power estimation of a design is based on the following main steps: generate artificial data in a number of (say 5000) hypothetical experiments based on the design and on certain assumptions; analyse each artificial data set in the same way that the future, real data set is intended to be analysed; from the distributions of the obtained parameter estimates, study the risks associated with the given experimental design. The method is illustrated for a factorial design in sensory analysis concerning warmed-over flavour development in meat. Here the Monte Carlo simulations indicated that four replicates were needed, given certain assumptions. The real experiment was performed independently twice, each time in four replicates. The resulting analysis of effects gave satisfactory results, indicating that four replicates had given the necessary and sufficient power in each of the two experiments. (C) 2000 John Wiley and Sons, Ltd.