SENSORY EVALUATION OF VANILLA-DAIRY DESSERTS BY REPERTORY GRID METHOD AND FREE CHOICE PROFILE

“Natillas” are semisolid dairy desserts widely consumed in Spain, with notable differences existing between commercial brands in their composition and sensory and physical properties. The repertory grid method, in conjunction with the free choice profile (FCP), was used to provide data on how consumers perceived eight commercial “natillas” samples. The most frequent terms used were: vanilla flavor, sweetness, consistency and liquid texture as described by 77, 67, 57 and 53% of the consumers, respectively. A three‐dimensional solution was chosen. Dimension 1 accounted for 27.23% of variance and separated the samples largely by yellow‐color intensity and consistency. Dimension 2 (15.88%) was mainly related to the visual attributes of texture, creaminess and different notes of flavor. The third dimension (11.34%) was related to structural‐texture attributes, orange‐yellow color and acid and artificial flavors. The assessors plot showed two clusters. Analysis of each group data revealed differences in their perception of sample variation, mainly regarding texture and color.     

EVALUATION AND APPLICATIONS OF ODOR PROFILING

An odor profiling procedure was developed based on the ASTM odor profiling method. This modified procedure involved using approximately twenty panelists. Panel sessions and data collection were controlled by computer. The results obtained by this panel compared favorably to results obtained by the ASTM panel for which 150 panelists evaluated each compound, indicating that a small panel can be used to produce replicable results. Statistical methods of finding similarities and dissimilarities among compounds using profile data are discussed and compared to results from a multidimensional scaling (MDS) study in which degrees of differences among compounds were judged directly. These results indicate that profile data can be used to define and map the degree of similarity/dissimilarity among compounds, as well as to define the sensory dimensions on which these compounds differ. The use of factor analysis to study the underlying sensory dimensions of the odor space is also discussed. It is hoped that this type of research will lead to a better understanding of the underlying dimensions used to describe odorants.     

A STATISTICAL TEST OF CONSENSUS OBTAINED FROM GENERALIZED PROCRUSTES ANALYSIS OF SENSORY DATA

Matrix matching techniques such as Generalized Procrustes Analysis (GPA) always produce a matrix of maximal agreement which can then be used to graphically represent samples in “consensus plots”. The degree to which the consensus plots produced by GPA on sensory data (such as that obtained from free choice profiling) actually give a picture of true consensus among panelists, as opposed to being merely artifacts of the analysis, has not been examined. Using a Monte Carlo approach, a statistical test is defined for qualifying this consensus. Examples of the application of the test to sensory profiling data of fruit flavors are given.     

EXPECTATIONS AND STABILITY OF PREFERENCE CHOICE

When identical milk samples are presented, only 30% of participants respond with a “no preference” rating. Stability of the “no preference” rating was studied under a variety of conditions, having consumer panelists rate both identical and different pairs of milk samples with varying fat content. The proportion of participants choosing the “no preference” option, when the samples in the pair were identical, was largely consistent despite manipulation of pretest conditions and changes in test questions and answer formats. However, when the milk preference test was preceded by a same/different test, and those responding “same” were assumed to have no preference, the percent of “no preference” was two to three times larger for identical test samples (60–69%). Thus by branching the question, the “false preference” choice for identical milks was lowered. Among those responding “different” to identical milks, the false‐alarm rate increased to 91%, suggesting that perception of (spurious or momentary) differences is driving at least part of the preference choice.     

COMPARISON OF PROJECTIVE MAPPING AND SORTING DATA COLLECTION AND MULTIVARIATE METHODOLOGIES FOR IDENTIFICATION OF SIMILARITY-OF-USE OF SNACK BARS1

Three data collection procedures, sorting and two forms of projective mapping (PM), were compared for ease-of-use and the ability to produce meaningful spatial maps when analyzed using Multidimensional Scaling (MDS), Generalized Procrustes Analysis (GPA) or Coordinate Averaging (CA). Eighteen commercially available snack bars were evaluated for similarity-of-use by two panels of 24. MDS of the sorting data and Procrustes analyses of PM data collected on unlabeled axis grouped the bars according to function and provided a meaningful spatial relationship in one dimension. However, MDS analysis of these PM data grouped the bars by similarity-of-use and provided a meaningful spatial interpretation in two dimensions. The CA analysis was not effective in separating the bars by similarity-of-use but did provide an indication of liking. A comparison of spatial configurations using RV coefficients showed moderate correlations between the methods. A panelist survey showed no significant differences in the ease-of-use, task interest or level-of-satisfaction with the final arrangement between the sorting and the PM data collection methods, but panelists did find it easier to change their minds using the PM procedure.     

CONFIDENCE ELLIPSES APPLIED TO THE COMPARISON OF SENSORY PROFILES

Our experiment involved seven panels and six chocolates – five dark chocolates and one milk chocolate. The aim of the study was to compare the sensory profiles of the chocolates. A natural question to ask is “Did the panelists detect any differences among the five dark chocolates or did they systematically contrast them with the milk chocolate?” The scatter plot of the chocolates obtained by principal component analysis was useless to answer that question, because of the proximity of the points. To overcome that, we used confidence ellipses calculated using bootstrap. The originality of the study lies in the fact that we applied those ellipses to hierarchical multiple factor analysis (HMFA): among the seven panels, six were composed of trained professionals and the last one was composed of untrained students, and through that method, we managed to compare the two types of panels and balance the role of each trained panel. HMFA provides in a single scatter plot a representation of the six chocolates for each panel, the trained panels and all the panels. Confidence ellipses around each chocolate show that the combined panels – the six trained panels and also the untrained panel – differentiate the five dark chocolates. They also show how much larger the untrained panel's variability is than that of the trained panels, and how comparable are the trained panels' variability to each other.     

THE JUST-ABOUT-RIGHT INTENSITY SCALE: FUNCTIONAL ANALYSES AND RELATION TO HEDONICS

This study assessed the meaning of just‐about‐right (JAR) categories when used with a 5‐point intensity scale. The panelists comprised both consumer and in‐house respondents. The JAR categories have various meanings as perceived by the respondents: “prefer product,”“very good,”“I like the product,”“like it very much,”“highly favorable,”“high acceptability,”“desirable like the product,”“best for the situation” and “correct.” Thus, the JAR meanings invoke preference and acceptability. This article presents two additional analyses to help the product tested. One method of analysis divides below‐JAR deviation from the above‐JAR deviation, with standard statistical procedures applied in each data set. The second method introduces the signal‐to‐noise ratio statistic for analyzing the relation of JAR to overall liking. Both analytic methods provide new ways to look at the JAR data, especially with respect to hedonics and product improvement.