Comparison of Rate-All-That-Apply (RATA) and Descriptive sensory Analysis (DA) of model double emulsions with subtle perceptual differences

Abstract The Rate-All-That-Apply (RATA) method, an intensity-based Check-All-That-Apply (CATA) variant, has recently been developed for sensory characterization involving untrained panellists. The aim of this study was to investigate the sensory profiles of ten model (double) emulsions with subtle perceptual differences obtained from the Rate-All-That-Apply (RATA) method with untrained panellists (n = 80). For this purpose two different analysis approaches were followed (treating the data as frequencies and as intensities) and then compared to results obtained from Descriptive Analysis (DA) with trained panellists (n = 11). The RATA method was adapted by including a short familiarization session to acquaint participants with the RATA methodology, the use of the scale, the sensory terms, and product differences. The comparison involved discriminative ability and configuration similarity by means of Multiple Factor Analysis (MFA) and R V coefficients. The results in our study show that the RATA intensity approach resulted in higher discriminative ability compared to the RATA frequency approach. Both RATA frequency and RATA intensity resulted in similar overall configurations compared to DA. However, important differences between the use of RATA and DA scales suggest that these overall similarities should be interpreted with caution and warrant a deeper investigation on how RATA scales are understood and used by consumers.

[1]  J. Hayes,et al.  Interpreting consumer preferences: physicohedonic and psychohedonic models yield different information in a coffee-flavored dairy beverage. , 2014, Food quality and preference.

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

[3]  D. Mcclements,et al.  Reduced Fat Food Emulsions: Physicochemical, Sensory, and Biological Aspects , 2016, Critical reviews in food science and nutrition.

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

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

[6]  Gastón Ares,et al.  Check-all-that-apply (CATA) responses elicited by consumers: Within-assessor reproducibility and stability of sensory product characterizations , 2013 .

[7]  John E. Hayes,et al.  Explaining variability in sodium intake through oral sensory phenotype, salt sensation and liking , 2010, Physiology & Behavior.

[8]  M. Vingerhoeds,et al.  Partial coalescence as a tool to control sensory perception of emulsions , 2009 .

[9]  J. Kennedy,et al.  EVALUATION OF REPLICATED PROJECTIVE MAPPING OF GRANOLA BARS , 2010 .

[10]  Gastón Ares,et al.  IS A CONSUMER PANEL ABLE TO RELIABLY EVALUATE THE TEXTURE OF DAIRY DESSERTS USING UNSTRUCTURED INTENSITY SCALES? EVALUATION OF GLOBAL AND INDIVIDUAL PERFORMANCE , 2011 .

[11]  S. Jaeger,et al.  Examination of sensory product characterization bias when check-all-that-apply (CATA) questions are used concurrently with hedonic assessments , 2015 .

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

[13]  Jérôme Pagès,et al.  Which value can be granted to sensory profiles given by consumers? Methodology and results , 2001 .

[14]  M. Vingerhoeds,et al.  Aspects of sensory perception of food emulsions thickened by polysaccharides , 2006 .

[15]  Michelle K. Beresford,et al.  Comparison of sensory product profiles generated by trained assessors and consumers using CATA questions: Four case studies with complex and/or similar samples , 2015 .

[16]  C. G. D. Kruif,et al.  Sensory perception and lubrication properties of milk: Influence of fat content , 2012 .

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

[18]  M. Stieger,et al.  Effect of gelation of inner dispersed phase on stability of (w1/o/w2) multiple emulsions , 2015 .

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

[20]  B. Piqueras-Fiszman,et al.  Descriptive sensory profiling of double emulsions with gelled and non-gelled inner water phase. , 2016, Food research international.

[21]  P. V. Meeren,et al.  Influence of internal water phase gelation on the shear- and osmotic sensitivity of W/O/W-type double emulsions , 2016 .

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

[23]  Lyle V. Jones,et al.  DEVELOPMENT OF A SCALE FOR MEASURING SOLDIERS’FOOD PREFERENCES , 1955 .

[24]  Eric Dickinson,et al.  Factors affecting the perception of creaminess of oil-in-water emulsions , 2005 .

[25]  S. Jaeger,et al.  On the analysis of Rate-All-That-Apply (RATA) data , 2016 .

[26]  René A. de Wijk,et al.  Textural perception of liquid emulsions: Role of oil content, oil viscosity and emulsion viscosity , 2011 .

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

[28]  Edward B. Manoukian,et al.  Mathematical nonparametric statistics , 1986 .

[29]  Jérôme Pagès,et al.  Multiple factor analysis (AFMULT package) , 1994 .

[30]  Jérôme Pagès,et al.  Testing the significance of the RV coefficient , 2008, Comput. Stat. Data Anal..

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

[32]  Armand V. Cardello,et al.  Direct and indirect hedonic scaling methods: A comparison of the labeled affective magnitude (LAM) scale and best–worst scaling , 2009 .

[33]  Gastón Ares,et al.  Novel Techniques in Sensory Characterization and Consumer Profiling , 2014 .

[34]  Sébastien Lê,et al.  How reliable are the consumers? Comparison of sensory profiles from consumers and experts , 2010 .

[35]  Gastón Ares,et al.  Evaluation of a rating-based variant of check-all-that-apply questions: Rate-all-that-apply (RATA) , 2014 .

[36]  R. Singleton,et al.  Sensory Evaluation by Quantitative Descriptive Analysis , 2008 .

[37]  Rate-all-that-apply (RATA) with semi-trained assessors: An investigation of the method reproducibility at assessor-, attribute- and panel-level. , 2016 .