Stability of sample configurations from projective mapping: How many consumers are necessary?

Projective mapping for sensory characterisation with consumers has been used for a relatively short period of time, which suggests that the development of guidelines regarding best practices is strongly needed. The present work aims to provide an insight on the minimum number of consumers needed to reach stable sample configurations. Data sets from 21 different consumer studies, differing in product category, number of samples and degree of difference among them, were used to evaluate the influence of the number of consumers on the stability of sample configurations by means of a resampling approach. For each study, 1000 random subsets of different number of consumers were generated from the original data set. For each virtual panel, sample configurations were obtained using Multiple Factor Analysis. The agreement between them and the reference configurations (obtained with all the consumers) was evaluated through the RV coefficient, using the first two and the first four dimensions of the MFA. Results showed that the stability of sample configuration clearly depended on the degree of difference and type of differences among samples and the number of samples in the dataset. Across the 21 data sets analysed, results suggested that when working with widely different samples, 50 consumers seems as a safe recommendation of minimum consumer panel size to obtain reliable results with projective mapping. However, after any characterisation by projective mapping is completed, it is highly recommended to check, a posteriori, the reliability of the sample space configuration using a bootstrapping procedure.

[1]  Hildegarde Heymann,et al.  A summary of projective mapping observations – The effect of replicates and shape, and individual performance measurements , 2013 .

[2]  Harry T. Lawless,et al.  Perceptual mapping of apples and cheeses using projective mapping and sorting. , 2010 .

[3]  Dominique Valentin,et al.  Analyzing assessors and products in sorting tasks: DISTATIS, theory and applications , 2007 .

[4]  Damien Brémaud,et al.  An alternative to external preference mapping based on consumer perceptive mapping , 2006 .

[5]  Herbert Stone,et al.  Sensory Evaluation Practices , 1985 .

[6]  J. Alldredge,et al.  Impact of Serving Temperature on Sensory Properties of Red Wine as Evaluated Using Projective Mapping by a Trained Panel , 2012 .

[7]  P. Robert,et al.  A Unifying Tool for Linear Multivariate Statistical Methods: The RV‐Coefficient , 1976 .

[8]  Conor M. Delahunty,et al.  Descriptive sensory analysis: past, present and future , 2001 .

[9]  Pascal Schlich,et al.  Defining and Validating Assessor Compromises About Product Distances and Attribute Correlations , 1996 .

[10]  Hildegarde Heymann,et al.  PROJECTIVE MAPPING AND DESCRIPTIVE ANALYSIS OF MILK AND DARK CHOCOLATES , 2009 .

[11]  G. Hough,et al.  Number of consumers necessary for survival analysis estimations based on each consumer evaluating a single sample , 2011 .

[12]  Bonnie M. King,et al.  Cost/efficiency evaluation of descriptive analysis panels. I: Panel size , 1995 .

[13]  Jérôme Pagès,et al.  Construction of a product space from the ultra-flash profiling method: application to 10 red wines from the Loire Valley. , 2009 .

[14]  Einar Risvik,et al.  Projective mapping: A tool for sensory analysis and consumer research , 1994 .

[15]  V. Orlien,et al.  Reduction of salt in pork sausages by the addition of carrot fibre or potato starch and high pressure treatment. , 2012, Meat science.

[16]  Jason Parcon,et al.  A method to investigate the stability of a sorting map , 2012 .

[17]  V. Orlien,et al.  Synergistic cooperation of high pressure and carrot dietary fibre on texture and colour of pork sausages. , 2011, Meat science.

[18]  H. Moskowitz Base size in product testing: A psychophysical viewpoint and analysis , 1997 .

[19]  Harry T. Lawless,et al.  CONSISTENCY OF MULTIDIMENSIONAL SCALING MODELS DERIVED FROM ODOR SORTING , 1990 .

[20]  Per B. Brockhoff,et al.  Confidence ellipses: A variation based on parametric bootstrapping applicable on Multiple Factor Analysis results for rapid graphical evaluation , 2012 .

[21]  Gastón Ares,et al.  Comparison of two sensory profiling techniques based on consumer perception , 2010 .

[22]  Dominique Valentin,et al.  What is the validity of the sorting task for describing beers? A study using trained and untrained assessors , 2008 .

[23]  Guillermo Hough,et al.  Number of consumers necessary for sensory acceptability tests , 2006 .

[24]  Sébastien Lê,et al.  SENSOMINER : A PACKAGE FOR SENSORY DATA ANALYSIS , 2008 .

[25]  Maximo C. Gacula,et al.  Descriptive Sensory Analysis in Practice , 1997 .

[26]  J. Shao,et al.  The jackknife and bootstrap , 1996 .

[27]  N. Mantel The detection of disease clustering and a generalized regression approach. , 1967, Cancer research.

[28]  Einar Risvik,et al.  Evaluation of sensory profiling and projective mapping data , 1997 .

[29]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[30]  Harry T. Lawless,et al.  Sensory Evaluation of Food: Principles and Practices , 1998 .

[31]  Y. Escoufier LE TRAITEMENT DES VARIABLES VECTORIELLES , 1973 .

[32]  Sébastien Lê,et al.  FactoMineR: An R Package for Multivariate Analysis , 2008 .

[33]  Morten Meilgaard,et al.  Sensory Evaluation Techniques , 2020 .

[34]  Per B. Brockhoff,et al.  Rapid descriptive sensory methods – Comparison of Free Multiple Sorting, Partial Napping, Napping, Flash Profiling and conventional profiling , 2012 .

[35]  Maximo Gacula,et al.  SAMPLE SIZE IN CONSUMER TEST AND DESCRIPTIVE ANALYSIS , 2006 .

[36]  Gastón Ares,et al.  Investigation of the number of consumers necessary to obtain stable sample and descriptor configurations from check-all-that-apply (CATA) questions , 2014 .

[37]  Marine Cadoret,et al.  Construction and evaluation of confidence ellipses applied at sensory data , 2013 .

[38]  Davide Giacalone,et al.  Comparison of three sensory profiling methods based on consumer perception: CATA, CATA with intensity and Napping® , 2014 .

[39]  Jérôme Pagès,et al.  Collection and analysis of perceived product inter-distances using multiple factor analysis: Application to the study of 10 white wines from the Loire Valley , 2005 .

[40]  Tormod Næs,et al.  Validation of two Napping techniques as rapid sensory screening tools for high alcohol products. , 2013 .

[41]  Pauline Faye,et al.  Perceptive free sorting and verbalization tasks with naive subjects: an alternative to descriptive mappings , 2004 .

[42]  Paula Varela,et al.  Packaging information as a modulator of consumers’ perception of enriched and reduced-calorie biscuits in tasting and non-tasting tests , 2012 .

[43]  Pascal Schlich,et al.  Adequate number of consumers in a liking test. Insights from resampling in seven studies , 2014 .

[44]  Paula Varela,et al.  Exploring consumer product profiling techniques and their linkage to a quantitative descriptive analysis. , 2010 .

[45]  Carles Serrat,et al.  Number of consumers necessary for shelf life estimations based on survival analysis statistics , 2007 .

[46]  Tormod Næs,et al.  Projective Mapping for interpreting wine aroma differences as perceived by naïve and experienced assessors , 2013 .

[47]  Gastón Ares,et al.  Are consumer profiling techniques equivalent for some product categories? The case of orange-flavoured powdered drinks , 2011 .

[48]  Sylvie Chollet,et al.  Invited review Quick and dirty but still pretty good: a review of new descriptive methods in food science , 2012 .

[49]  Gastón Ares,et al.  Sensory profiling, the blurred line between sensory and consumer science. A review of novel methods for product characterization , 2012 .

[50]  Hildegarde Heymann,et al.  HOW MANY JUDGES SHOULD ONE USE FOR SENSORY DESCRIPTIVE ANALYSIS , 2012 .