Search

The recurring need to assess product reformulations has kept difference testing at the forefront of sensory science. Within the realm of difference testing, the Tetrad test has risen in popularity recently as its superiority over the Triangle test has been demonstrated both in theory and in practice. But it remains to compare the Tetrad test in detail with other commonly used testing methods such as the Degree of Difference (DOD) test. In this paper, we provide such a comparison by considering, from a theoretical perspective, the differences between both power and precision for the Tetrad and DOD tests. In particular we show that, theoretically and for the range of sensory effect sizes likely to be of interest in consumer research, the Tetrad test is more powerful and more precise than the DOD test. Even so, if there is substantially more perceptual noise in the Tetrad test from the two additional stimuli, it is possible that performance of the DOD could surpass the performance of the Tetrad test in practice. To investigate this last statement, we quantify the additional noise required to negate the theoretical advantage of the Tetrad test. [ABSTRACT FROM AUTHOR]

Highlights: [•] The variance of d’ is compared for the Tetrad, Triangle, and 2-AFC tests. [•] The expected widths of the likelihood-based confidence intervals for these tests are compared. [•] The advantage of the Tetrad over the Triangle and 2-AFC for equivalence testing is examined. [ABSTRACT FROM AUTHOR]

Highlights: [•] Tests for comparing 2-AC data with placebo data are discussed. [•] A recently suggested test is shown to have a too high type I error rate. [•] Two alternative tests are proposed with correct type I error and high power. [•] One test is shown to be particularly insightful. [ABSTRACT FROM AUTHOR]

Abstract: Ideal point modeling is a type of multivariate mapping in which consumers are assumed to use internal ideals in their hedonic evaluation of products. In their calculation processes, these techniques typically assume that consumers have a unique ideal for the product set tested. But this assumption is difficult to verify from the liking data alone, and may be violated if different subcategories of products, such as light and dark chocolate, are included in the same experiment. In this paper we propose the use of the Ideal Profile Method (IPM) to test this assumption. In IPM, consumers are asked to rate explicitly their ideals for each product tested. The variability between products of the averaged ideal ratings can then be used to check for the assumption of a single ideal. The procedure we describe involves ANOVA and confidence ellipses associated to the Hotelling T 2 test. We then consider three cases to illustrate the use of this methodology in practice. [Copyright &y& Elsevier]

Abstract: This study tested the principle of supercombinatorality, i.e. that food combinations (of more than two items) that are fully compatible on a pairwise basis are more compatible than combinations that are not fully compatible pairwise. Previous work has shown this to hold for salad ingredient combinations predicted for individuals, but this has not yet been tested for groups. This study extended the previous findings to group data, and in a different product system, namely pizza toppings. Purchase intent responses to pairs of 25 different pizza toppings were collected and used to predict pizzas (with 1–6 toppings) that would appeal to the entire group. Results showed purchase interest to be higher for the predicted pizzas than for non-predicted pizzas supporting the supercombinatorality principle. The study demonstrates that food product developers can use consumer-driven data and a graph theoretic approach to screen large numbers of potential food combinations in order to predict potentially successful combinations and to do so in a highly cost-efficient manner. [Copyright &y& Elsevier]

Abstract: Although Total Unduplicated Reach and Frequency (TURF) analysis has repeatedly demonstrated value in a variety of market research applications, TURF analyses on large problem sizes have historically either been unacceptably slow or approximate in their solutions. To resolve this dilemma, we begin by identifying and explaining the principle of non-synergy that is present in all situations for which TURF analyses apply. We use this principle to provide a competitive algorithm, called eTURF, for determining exact solutions to large TURF problems in reasonable time and we provide an illustrative example to demonstrate how eTURF can obtain considerable speed improvements. We then discuss how eTURF is useful not only in terms of providing optimal solutions but also in providing a metric by which to gauge the quality of heuristic TURF solutions. [Copyright &y& Elsevier]

Abstract: The analysis of choice data in which no difference/preference responses, or ties, occur is considered in this paper. A key issue addressed in the paper is the need for “identicality norms” for difference and preference tests. These norms reflect the researcher’s expectation for the number of ties that would have occurred in the experiment had the products tested been putatively identical. Without these norms, the issue of how to account for ties can never be fully resolved. After this idea is developed, some methods from the statistics literature to account for ties are reviewed and the Thurstonian 2-AC (2-Alternative Choice) model is discussed. Common practices of equal or proportional redistribution of ties are noted to be either conservative or liberal, respectively, when the binomial distribution is used to evaluate results. In particular, the exact probability function for the equal allocation method is given as a particular type of mixing distribution, known as a convolution, of binomial probability functions. Regardless of which statistical method is used to test tied data, however, none of the current methods of analysis can account for segmentation or the effect of heterogeneity in individual assessors. To study the possible effect of heterogeneity, the data could first be tested against an identicality norm. Thus, this research clarifies the assumptions that are made when conducting tests on paired comparison data with ties and provides guidance on the choice of analytic method. [Copyright &y& Elsevier]

Abstract: In statistical applications, such as a comparison of two items, it is useful to know whether one item is equivalent to another. Similarly it is often desirable to know whether one item can act as a substitute for another. Applications of the concept of equivalence include blend and flavor modifications of products, substitution of generic drugs for brand-name drugs, modifications of products in response to government regulations, or component substitutions with more healthful or lower cost components. In addition, some companies develop products that are direct substitutes for those of their competitors and make advertising claims concerning their equivalence. In a recent paper, Ennis and Ennis [Ennis, D. M., & Ennis, J. M. (2009). Hypothesis testing for equivalence based on symmetric open intervals. Communications in Statistics – Theory and Methods, 38(11), 1792–1803] used an open interval to define equivalence and provided exact and approximate methods for testing a null hypothesis of nonequivalence. In this paper, a discussion of this newly developed theory of equivalence testing is presented along with a comparison to existing methods such as the “two one-sided tests” (TOST) method. We provide numerical examples to illustrate this new theory and we demonstrate that although the TOST is a convenient approximation it is fundamentally inconsistent with the specification of the null hypothesis. [Copyright &y& Elsevier]

• Transforms survey responses into unique artworks that represent individual taste profiles. • Utilizes NFTs to present personalized consumer preferences and product recommendations. • Preliminary assessment of consumers' cross-modal (taste vs. visual) preference for NFT artwork. The emergence of Web3 technologies in the recent decade offers innovative tools and protocols that can revolutionize market research, which faces the ongoing challenges of comprehending consumer needs. One possibility is to securely store and represent personalized consumer preferences. This manuscript illustrates the application of Non-Fungible Tokens (NFTs) as a means to present consumer preference data and deliver personalized product recommendations directly to consumers. To do this, we initiated a study involving a small group of 31 respondents who participated in the "What's Your Whisky" survey. Their responses were transformed into unique pieces of artwork generated by an algorithm. Furthermore, whisky recommendations were derived from these survey responses and integrated into the corresponding artwork. These personalized artworks, which represent each respondent's whisky taste preference and top whisky selection, can be minted onto a blockchain as NFTs. This process enables respondents to securely own and retain them in their digital wallets, thus creating an immutable record of their whisky taste preferences and personalized recommendations. Finally, a follow-up survey was conducted to assess the value of personalized artwork NFTs. Respondents were asked for preferences between artwork derived from their taste preferences to those with the same whisky recommendations but with randomly sourced background art. Binomial test results revealed a clear preference for artwork based on their personal affirmed that respondents favored artwork based on their personal survey responses. [ABSTRACT FROM AUTHOR]

Highlights: [•] The limitations of the Proportion of Discriminators model are highlighted. [•] The need for a meaningful approach to sensory effect size estimation is underscored. [•] Tables are provided to guide the transition to Thurstonian sensory effect size estimation. [ABSTRACT FROM AUTHOR]

• Capsicum extract and seasoning ingredients interact in potato chips. • Interactions caused either an enhancement or suppression of sensory perceptions. • Information is helpful for salty snack formulations. The hot, spicy perception elicited by capsaicin and capsaicinoids found in chilis is often sought by many consumers. It is estimated that approximately one in four individuals globally consumes hot chilis. Therefore, the hot perception from capsaicin is being increasingly incorporated into several of today's dishes and food items. However, there is limited research on heat perception in finished food systems. In this study, fourteen (14) potato chip samples were evaluated by a trained descriptive panel to determine heat perception as well as potential flavor interactions between heat and other flavor enhancers commonly used in snack seasonings. Potato chips varied in their capsicum extract, sugar, salt, acid, umami, flow agent and fat content. Twenty-two (22) significant interaction effects (p-value ≤ 0.01) between capsicum and the remaining potato chip ingredients were found. Brown Sugar, Cardboard, Earthy, Heat, Jalapeño, Potato, Sweet and Umami perceptions were enhanced by interactions between capsicum and the potato chip ingredients while Fatty-Buttery, Salty and Vinegar perceptions were suppressed. Overall, the sensory perceptions were enhanced when in the presence of high capsaicin. These results indicate that the perceptions of certain sensory attributes are affected by the interaction between capsicum and other commonly used ingredients in salty snack formulations. This information can prove valuable for formulation efforts regarding seasonings with higher levels of heat as well as for potential brand decisions for salty snack products. Additionally, this information could be promising within the "better-for-you" category especially since capsaicin may impart some benefit around food-related health issues, including sugar and sodium reduction. [ABSTRACT FROM AUTHOR]