EVALUATION OF PANELIST PERFORMANCE IN DESCRIPTIVE PROFILING OF RANCID SAUSAGES: A MULTIVARIATE STUDY

This paper illustrates an application of principal component analysis (PCA), partial least squares regression (PLS) and generalized procrustes analysis (GPA) to evaluate the ability of a trained group of assessors to perceive rancidity in foods. PCA and regression PLS were utilized to determine to which extent sensory attributes capture the information perceived by a trained sensory panel, and if this can be developed into a predictive model for rancidity in sausages. The data were submitted to a GPA to obtain a map of the products for each subject as compared with a consensus products map. Assessors plots for the sensory attributes were also obtained to reveal the dissimilarities between panelists and to explore clustering.     

A COMPARISON OF TWO MULTIVARIATE METHODS FOR THE ANALYSIS OF SENSORY PROFILE DATA

Tables of means, over assessors, are often used to summarize the results of sensory profile experiments. These tables are sometimes further summarized by Principal Components Analysis (PCA) to give plots of the samples in the principal sensory dimensions. An alternative procedure is to use Generalized Procrutes Analysis (GPA) on the assessor data to allow for differences in usage of the vocabulary and in the proportion of the scale used. It is shown that these methods give different configurations in the principal sensory dimensions when applied to the data from a study of cheeses (Muir et al. 1995). Using a Jackknife method to calculate the variability of the samples in the principal sensory dimensions, the results from the GPA method are shown to have a higher dimensionality than from the PCA method. Jackknife estimates of variability are used to calculate confidence ellipses to attach to the sensory space maps.     

A NEW SIGNIFICANCE TEST FOR CONSENSUS IN GENERALIZED PROCRUSTES ANALYSIS

Generalized Procrustes Analysis is frequently used to find a consensus from sensory panel data. Recently King and Arents (1991) have proposed a goodness of fit of the consensus configuration based on Monte-Carlo simulations. A modified test is developed that retains the original assessor configurations by permutation of the data rows. This leads to a more powerful statistical test as is shown by application to data collected in the free-choice profiling of eight milk products.     

MULTIDIMENSIONAL SORTING, SIMILARITY SCALING AND FREE-CHOICE PROFILING OF GRAPE JELLIES

The sensory data from three different methods, multidimensional sorting, similarity scaling, and free-choice profiling were compared using ten commercial grape jellies as a model system. Without any prior training, the overall similarities/dissimilarities between stimuli were judged using both multidimensional sorting and pair-wise scaling and the sensory attributes were rated using free-choice profiling by different panels. A two-dimensional stimulus configuration best represented the data from each of the three methods. The underlying dimensions of stimulus space were identified from free-choice profiling data and also explained some background variables. The texture, sweetness, sourness, grape flavor and color contributed to both dimensions. All three methods were very similar in describing the most important differences among stimuli as suggested by the highly significant correlation between their first dimensions. The Procrustes analysis coupled with permutation tests, as well as RV coefficient, indicated that similarity scaling and free-choice profiling reached maximum consensus, whereas multidimensional sorting shared slightly lesser consensus with the other two methods.     

Sensory evaluation of acids by free-choice profiling

The technique of free-choice profiling was applied in orderto characterize the sensory properties of some common organicand inorganic acids. Analysis of panelists' scores by generalizedProcrustes Analysis (GPA) provided information on the relationshipsamong samples and assessors for both the consensus and individualconfigurations. Results indicated that on a weight basis (w/vor v/v), acids differed in their flavor and taste dynamics.Acids were described differently by individual panelists. The GPA resulted in three important principal axes (PA). Thefirst PA had astringency/mouthfeel as the most important factor,while bitterness and sourness were the most important for thesecond and the third PAs, respectively. At 0.08% (w/v or v/v),the inorganic acids, hydrochloric and phosphoric, were moreastringent than sour. The bitterness of succinic (S) was intenseas was the sourness of fumaric, malic and the combinations offumaric:malic (FM), citric:malic (CM) and citric:fumaric (CF).The sensory characteristics of adipic and quinic were perceivedto be very weak at this concentration. The relationship betweenastringency and pH was more evident than was the relationshipbetween pH and sourness.     

DESCRIPTION OF THE SENSORY CHARACTERISTICS OF SPANISH UNIFLORAL HONEYS BY FREE CHOICE PROFILING

Different Spanish unifloral honeys (eucalyptus, sunflower, rosemary, thyme, lavender, citrus, anise, quercus, and lemon blossom) and one multifloral honey were studied by Free-Choice Profiling (FCP) analysis. Generalized Procrustes Analysis (GPA) applied to the FCP data allowed discrimination between samples and provided information on the attributes responsible for the differences observed. The honeys had significantly different sensory characteristics. Textural attributes were the predominant factor in discriminating between samples, and appearance (color included) was also correlated with GPA dimensions to a lesser extent.     

THE RELATION OF REPERTORY GRID/GENERALIZED PROCRUSTES ANALYSIS SOLUTIONS TO THE DIMENSIONS OF PERCEPTION: APPLICATION TO MUNSELL COLOR STIMULI

The repertory grid method (RGM) was initially devised to determine how individuals construe their worlds. There have been many attempts to extend this principle to look at relationships amongst objects, as seen by groups of individuals. However, to derive the implied consensus representation of the objects, poses conceptual and mathematical problems because of the idiosyncratic nature of constructs elicited using RGM. Generalized Procrustes analysis (GPA) offers a solution since there is no assumption of common interpretation of constructs. It is an implicit but apparently untested assumption of GPA, that mathematical and perceptual structure are analogous. To test this, combined RGM/GPA was applied to color. Using the method of triads, 24 subjects independently described constructs to characterize 26 Munsell color chips differing in Hue, Value and Chroma. Subjects then rated the colors on their own construct scales. The first six dimensions of the consequent color space were interpreted as brightness, greenness, lightness, purple/blueness, redness and yellowness. This differs from expectation only in that the colors were recovered on uni-polar dimensions rather than red-green and blue-yellow continua. However, since the main perceptual attributes of color were recovered the analogy assumption of GPA is supported, as is application to repertory grids.     

ASSOCIATION OF STRAWBERRY YOGURT SENSORY PROPERTIES WITH PRODUCT COMPOSITION BY PROCRUSTES ANALYSIS

The flavor of eight samples of commercial strawberry yogurt was studied by Free-Choice Profile analysis (FCP). Generalized Procrustes Analysis (GPA) applied to FCP allowed differentiation between samples and highlighted flavor attributes responsible for the observed differences. The relation between sensory and physicochemical datasets was studied by means of GPA. Those samples with higher carbohydrate content were perceived as sweeter, having stronger strawberry flavor, and with more dairy and yogurt flavors. Samples with higher titratable acidity, ash and protein content were perceived as more acidic and higher in intensity of "faulty" or "defective" flavors. Higher moisture content was associated with lower intensity of "dairy" flavors (creamy, dairy, and yogurt) and greater intensity of rancid flavor. It is concluded that, though not often used to this end, GPA is a suitable method to study the relationship of sensory and instrumental measurements.     

THE APPLICATION OF TWO FREE-CHOICE PROFILE METHODS TO INVESTIGATE THE SENSORY CHARACTERISTICS OF CHOCOLATE

The concept of free-choice profiling allows untrained assessors to participate in sensory profiling. However, it is sometimes the case that such individuals are unable to describe what they perceive when the samples are presented in the isolation of the sensory testing booths. This paper reports an experiment to compare the usual free-choice profile procedure, with a more structured approach based on Kelly's repertory grid method. Generalized Procrustes analysis was used to analyse the data. The sample configurations obtained from both approaches were very similar, as were the interpretations of the main perceptual dimensions.     

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.     

GRAPES: A METHOD AND A SAS® PROGRAM FOR GRAPHICAL REPRESENTATIONS OF ASSESSOR PERFORMANCES

GRAPES computes individual and global analyses of variance for sensory profiling data, consisting of several sessions in which all the panelists gave scores to all the products for a number of attributes. The fitted model takes into account the session effect. GRAPES summarizes the results by means of graphical assessor scatterplots which allow to check and to compare panelist performances, such as the way of using scale, the reliability, the discrimination power and the agreement with the panel. In addition, GRAPES detects the outliers for each of these criterion. The usefulness of GRAPES for the panel leader will be demonstrated using texture and flavor profiling of 4 restructured steaks by 12 assessors for 15 attributes. The SAS® program GRAPES, available by e-mail from the author, requires the SAS/BASE®, SAS/STAT®, SAS/GRAPH® and SAS/IML® softwares.     

MODELING THE EVOLUTION OF THE PERFORMANCE OF A SENSORY PANEL: A MIXED-MODEL AND CONTROL CHART APPROACH

A sensory panel is often used to profile the same type of product with the same set of attributes for many years. We are interested in characterizing the evolution of the performance of such a panel (and its panelists) over time. This article presents a methodology based on a mixed‐model approach that takes into account the evolution of both panel and panelist in the same model. At the panel level, linear and quadratic evolutions of the performance are tested. At the panelist level, the method allows detection of whether some panelists perform better than others, and whether this difference remains the same or evolves over time. This mixed‐model approach is followed by a graphical representation using a control chart method to identify occasional outliers. Data used to illustrate this methodology are eight sensory profiling data sets collected on ready‐made frozen meals between 1997 and 2001 (every 6 months). The performance index chosen as an example in this study is the individual repeatability measured by standard deviation over replicates.     

VISUAL CHARACTERIZATION OF THE ESTHETIC QUALITY OF THE ROSEBUSH

Since its introduction into the analysis of foodstuffs, sensory analysis has been applied in several contexts. This work seeks to widen the field of sensory analysis to include ornamental plants and to characterize their esthetic quality. Using the rosebush as a plant model, an attribute generation protocol is proposed in order to develop a conventional profile of such products. Further to statistical treatments aiming to verify the unambiguity, discrimination and independence of these attributes, a reduced list of 18 attributes has been set up. These attributes make up the very core of the conventional profiling studies currently undertaken .

PRACTICAL APPLICATIONS

The generation of a list of attributes that is not too long, in order to describe plants as exhaustively as possible, is one of the first steps of extending sensory analysis methods to ornamental horticulture. This list will be used to train a panel of assessors to characterize the rosebush.
Two applications are in progress. The first application consists of evaluating the impact of nitrogen nutrition on the visual quality of the rosebush. The second has the objective of determining which characteristics influence consumer preferences.     

A SIMPLE ALTERNATIVE TO GENERALIZED PROCRUSTES ANALYSIS: APPLICATION TO SENSORY PROFILING DATA

A statistical method for analyzing sensory profiling data obtained by means of fixed vocabulary or free choice profiling is discussed. The most interesting feature of this method is that it involves only simple statistical treatment and can therefore be performed using standard software packages. The outcomes of this method are compared to those of Generalized Procrustes Analysis on the basis of two data sets obtained, respectively, by means of fixed vocabulary and free choice profiling. A significance test is also discussed in order to assess whether the overall configuration of the products is meaningful. This significance test is based upon a simulation study involving the permutation procedure.     

MULTIVARIATE ANALYSIS OF CONVENTIONAL PROFILING DATA: A COMPARISON OF A BRITISH AND A NORWEGIAN TRAINED PANEL

Many studies have shown that conventional profiling provides reproducible and meaningful results. However, comparison of the technique as used in different countries appears to be nonexistent. In addition, data analysis is often approached differently, and this aspect is also addressed. This paper describes a study to compare the results obtained from profiling milk chocolate samples, using trained panels in Britain and Norway. Data were analyzed using principal component analysis, generalized Procrustes analysis and partial least squares regression. Results indicate that the underlying perceptual structure of the sample spaces obtained from both panels were similar, however, the emphasis on the underlying sensory dimensions differed. Moreover, it was possible to calibrate the two profiles, which has implications for marketing products for export, as well as providing a potential tool for panel monitoring and calibration across cultures.     

Normalization strategies for microRNA profiling experiments: a ‘normal’ way to a hidden layer of complexity?

MicroRNA (miRNA) profiling is a first important step in elucidating miRNA functions. Real time quantitative PCR (RT-qPCR) and microarray hybridization approaches as well as ultra high throughput sequencing of miRNAs (small RNA-seq) are popular and widely used profiling methods. All of these profiling approaches face significant introduction of bias. Normalization, often an underestimated aspect of data processing, can minimize systematic technical or experimental variation and thus has significant impact on the detection of differentially expressed miRNAs. At present, there is no consensus normalization method for any of the three miRNA profiling approach. Several normalization techniques are currently in use, of which some are similar to mRNA profiling normalization methods, while others are specifically modified or developed for miRNA data. The characteristic nature of miRNA molecules, their composition and the resulting data distribution of profiling experiments challenges the selection of adequate normalization techniques. Based on miRNA profiling studies and comparative studies on normalization methods and their performances, this review provides a critical overview of commonly used and newly developed normalization methods for miRNA RT-qPCR, miRNA hybridization microarray, and small RNA-seq datasets. Emphasis is laid on the complexity, the importance and the potential for further optimization of normalization techniques for miRNA profiling datasets.