The use of analysis of variance and three-way factor analysis methods for studying the quality of a sensory panel

In sensory analysis a panel of assessors evaluate a collection of samples/products with respect to a number of sensory characteristics. Assessments are collected in a threeway data matrix crossing products, attributes and assessors. The main objective of the experiment is to evaluate products. However, the performance of each assessor and of the panel as a whole is of crucial importance for a successful analysis. At this aim univariate analysis for each sensory attribute as well as multi-way analysis considering all directions of information are usually performed. The present work studies the quality of a panel using both methods. The basic idea is to compare results and investigate relations between the two different analytical approaches

Investigating paired comparisons after principal component analysis

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Linking gene regulation and the exo-metabolome: A comparative transcriptomics approach to identify genes that impact on the production of volatile aroma compounds in yeast

BACKGROUND: 'Omics' tools provide novel opportunities for system-wide analysis of complex cellular functions. Secondary metabolism is an example of a complex network of biochemical pathways, which, although well mapped from a biochemical point of view, is not well understood with regards to its physiological roles and genetic and biochemical regulation. Many of the metabolites produced by this network such as higher alcohols and esters are significant aroma impact compounds in fermentation products, and different yeast strains are known to produce highly divergent aroma profiles. Here, we investigated whether we can predict the impact of specific genes of known or unknown function on this metabolic network by combining whole transcriptome and partial exo-metabolome analysis. RESULTS: For this purpose, the gene expression levels of five different industrial wine yeast strains that produce divergent aroma profiles were established at three different time points of alcoholic fermentation in synthetic wine must. A matrix of gene expression data was generated and integrated with the concentrations of volatile aroma compounds measured at the same time points. This relatively unbiased approach to the study of volatile aroma compounds enabled us to identify candidate genes for aroma profile modification. Five of these genes, namely YMR210W, BAT1, AAD10, AAD14 and ACS1 were selected for overexpression in commercial wine yeast, VIN13. Analysis of the data show a statistically significant correlation between the changes in the exo-metabome of the overexpressing strains and the changes that were predicted based on the unbiased alignment of transcriptomic and exo-metabolomic data. CONCLUSION: The data suggest that a comparative transcriptomics and metabolomics approach can be used to identify the metabolic impacts of the expression of individual genes in complex systems, and the amenability of transcriptomic data to direct applications of biotechnological relevance

Evaluation of complementary numerical and visual approaches for investigating pairwise comparisons after principal component analysis

We propose and evaluate numerical and visual methods for investigating paired comparisons after principal component analysis (PCA). PCA results can be visualized to facilitate an understanding of the relationships between the products and the sensory attributes. But identifying and visualizing significant product differences in multiple PCs simultaneously is not straightforward. A benefit of the proposed methods is that they provide a screening tool for evaluating PCA results rapidly. We begin with a real data set which is analyzed and submitted to the truncated total bootstrap (TTB) procedure. This TTB procedure simulates and analyzes results from virtual panels. The TTB-derived results form clouds of uncertainty around each product and paired comparison. Although these clouds can be visualized directly or by plotting the smallest contours that enclose 95% of their kernel-estimated densities, we propose that plotting TTB-derived 95% confidence ellipsoids provide a less cumbersome approach. We show that it is also possible to calculate P values that evaluate whether pairs of products are discriminated in the PCA subspace. The interpretation of these P values coincides with the visual interpretation of the confidence ellipsoids. The volumes of these confidence ellipsoids, which quantify uncertainty, are calculated easily. The confidence ellipsoids, the P values, and the volumes provide a simple and consistent approach for investigating paired comparisons after PCA. We illustrate the methods with two real data sets, one a sensory quantitative-descriptive data set from a trained panel, the other a check-all-that-apply (CATA) data set from a consumer panel. We also conduct a simulation study based on each of these data sets. The results from these simulation studies show that under repetition, the 95% confidence ellipsoids often have coverage of approximately 95%, but in some cases, coverage can be substantially lower. This indicates that the proposed ellipsoids have an approximately frequentist interpretation, but coverage varies. The complementary numerical and visual approaches can be applied to a wide range of data sets from sensory evaluation and to data from other domains.submittedVersio

Discriminability and uncertainty in principal component analysis (PCA) of temporal check-all-that-apply (TCATA) data

Temporal check-all-that-apply (TCATA) data can be summarized and explored using principal component analysis (PCA). Here we analyze TCATA data on Syrah wines obtained from a trained sensory panel. We evaluate new and existing methods to explore the uncertainty in the PCA scores. To do so, we use the bootstrap procedure to obtain many virtual panels from the real panel’s data. Virtual-panel PCA scores are obtained using two methods. The first method, called the partial bootstrap (PB), obtains virtual-panel scores from regression. The second method, called the truncated total bootstrap (TTB), applies PCA to the virtual-panel results to obtain scores, which are truncated and superimposed on the real-panel scores by Procrustes rotation. We use the virtual scores from each method to investigate uncertainty in the real-panel PCA scores visually and numerically. To understand the uncertainty of the scores, we obtain confidence ellipses (CEs) and their areas, as well as confidence intervals (CIs) and their widths. Next, to determine whether PCA scores for different samples are well separated, we propose a procedure for approximating the standard errors of sample differences and correcting for multiple comparisons. We propose a discriminability index, and show that it can enhance the interpretability of PCA results. We incorporate graphical features into our PCA biplots to visualize discriminability. We did not find a large difference between the PB and TTB methods for understanding the uncertainty and discriminability in PCA scores. Although the TCATA data that we analyzed have a special structure, the methodological approaches presented here can be readily adapted to other applications of PCA.submittedVersio

Portion size selection as related to product and consumer characteristics studied by PLS path modelling.

Expectations of satiation and satiety have been increasingly investigated because of the interest in how they, along with liking, can modulate portion-size selection. Consumer characteristics can also be important when consumers select their portion size. However, the contribution and interaction of consumers and product aspects to portion size selection has not been unveiled. This study aims to better understanding these complex relations by simultaneously assessing the relative influence of consumer characteristics and product related properties on portion size selection utilizing PLS-Path Modelling (PLS-PM) approach. In this study, consumers (n = 101) answered questions regarding attitudes to health and hedonic characteristics of foods, and completed hunger and fullness questions. In an evaluation step, they tasted eight samples of yogurt with different textures and rated liking, expected satiation, expected satiety and portion size. The consumers were also classified on their mouth behaviour by using the JBMB™ tool. Results showed that liking, satiation, satiety and portion size depended firstly on the thickness, and then on the particle size of samples. PLS-PM was used to generate a model, indicating that liking was a direct predictor of portion size, with a stronger effect than satiation or satiety. The relationship between liking and satiety was observed both in direct direction (liking-satiety) and also indirect direction throughout satiation (liking-satiation-satiety). The former was negative effect and the latter was positive effect depending on the criteria which consumers used. These findings implied that liking is a main factor in the prediction of portion size however the relations are complex.submittedVersio

Portion size selection as related to product and consumer characteristics studied by PLS path modelling

Expectations of satiation and satiety have been increasingly investigated because of the interest in how they, along with liking, can modulate portion-size selection. Consumer characteristics can also be important when consumers select their portion size. However, the contribution and interaction of consumers and product aspects to portion size selection has not been unveiled. This study aims to better understanding these complex relations by simultaneously assessing the relative influence of consumer characteristics and product related properties on portion size selection utilizing PLS-Path Modelling (PLS-PM) approach. In this study, consumers (n = 101) answered questions regarding attitudes to health and hedonic characteristics of foods, and completed hunger and fullness questions. In an evaluation step, they tasted eight samples of yogurt with different textures and rated liking, expected satiation, expected satiety and portion size. The consumers were also classified on their mouth behaviour by using the JBMB™ tool. Results showed that liking, satiation, satiety and portion size depended firstly on the thickness, and then on the particle size of samples. PLS-PM was used to generate a model, indicating that liking was a direct predictor of portion size, with a stronger effect than satiation or satiety. The relationship between liking and satiety was observed both in direct direction (liking-satiety) and also indirect direction throughout satiation (liking-satiation-satiety). The former was negative effect and the latter was positive effect depending on the criteria which consumers used. These findings implied that liking is a main factor in the prediction of portion size however the relations are complex.acceptedVersio

Why use component-based methods in sensory science?

This paper discusses the advantages of using so-called component-based methods in sensory science. For instance, principal component analysis (PCA) and partial least squares (PLS) regression are used widely in the field; we will here discuss these and other methods for handling one block of data, as well as several blocks of data. Component-based methods all share a common feature: they define linear combinations of the variables to achieve data compression, interpretation, and prediction. The common properties of the component-based methods are listed and their advantages illustrated by examples. The paper equips practitioners with a list of solid and concrete arguments for using this methodology.publishedVersio

Critical evaluation of assessor difference correction approaches in sensory analysis

In sensory data analysis, assessor-dependent scaling effects may hinder the analysis of product differences. Romano et al. (2008) compared several approaches to reduce scaling differences between assessors by their ability to maximise the product effect F-values in a mixed ANOVA analysis. Their study on a sensory dataset of 14 cheese samples assessed by twelve assessors on a continuous scale showed that some of these approaches apparently improved the F-value of the product effect. However, this direct comparison is only legitimate if these F-values originate from the same null distribution. To obtain the null distributions of the different correction methods, we employed a permutation approach on the same cheese dataset also used by Romano et al. (2008) and a random noise simulation approach. Based on the empirically obtained null distributions, we calculated the corrected product effect significance to directly compare the performance of the preprocessing methods. Our results show that the null distributions of some preprocessing methods do not correspond to the expected F-distribution. In particular for the ten Berge method, the null distribution is shifted towards higher F-values. Therefore, an observed increase of the product effect F-value, as compared to the F-value on raw data, does not necessarily lead to increased product effect significance. If p-values are calculated based on such inflated F-values, significance may thus be overestimated. In contrast, calculation of p-values directly from the empirical null distributions obtained by permutation provides a common ground to properly compare method performance. Moreover, we show that differences in reproducibility between assessors, as they exist in real-world sensory datasets, may lead to overestimation of product effect significance by the mixed assessor model (MAM).publishedVersio

Projective mapping based on choice or preference: an affective approach to projective mapping

This work explores a new affective approach to projective mapping, based on consumers' choices or preferences. Two sessions, one week apart, were performed with the same consumers, using whole bread as a case study. Overall liking ratings (OL) were gathered in blind conditions and samples were also profiled by a trained panel using generic descriptive analysis. Three projective mapping tests were performed in different scenarios. Consumers' categorization and product descriptions were explored when consumers based their positioning on the products' similarities and differences (analytical approach, “classic napping”) both in blind and informed conditions, and when consumers were focusing on their preference or choice (affective approach). The affective approach to projective mapping successfully revealed consumers' drivers of liking and choice from a holistic perspective, where consumers summarized their main drivers for categorizing products as they would do when choosing in real life situations, based on their preferences