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Richmond, VA, United States

Bi J.,Sensometrics Research and Service | Lee H.-S.,Ewha Womans University | O'Mahony M.,University of California at Davis
Journal of Sensory Studies

The four-alternative forced choice (4-AFC) is one of a family of m-AFC tests used in Psychology. Based on a detection-theoretic model, for a given d' it is slightly more powerful than the 3-AFC and 2-AFC. Although its potential use in sensory food science is limited, there are occasions when it will be used. Accordingly, tables of d' values for given proportions of correct responses (pc) along with corresponding values of B, for computing variances, are given in this paper. Power comparisons are also made between 2-AFC, 3-AFC and 4-AFC tests. © 2010 Wiley Periodicals, Inc. Source

As the desire to promote health increases, reductions of certain ingredients, for example, sodium, sugar, and fat in food products, are widely requested. However, the reduction is not risk free in sensory and marketing aspects. Overreduction may change the taste and influence the flavor of a product and lead to a decrease in consumer's overall liking or purchase intent for the product. This article uses the benchmark dose (BMD) methodology to determine an appropriate reduction. Calculations of BMD and one-sided lower confidence limit of BMD are illustrated. The article also discusses how to calculate BMD and BMDL for overdispersed binary data in replicated testing based on a corrected beta-binomial model. USEPA Benchmark Dose Software (BMDS) were used and S-Plus programs were developed. Practical Application: The method discussed in the article is originally used to determine an appropriate reduction of certain ingredients, for example, sodium, sugar, and fat in food products, considering both health reason and sensory or marketing risk. © 2009 Institute of Food Technologists®. Source

This article attempts to deliver the following message to the researchers and practitioners in the sensory field. (1) Theoretically, drivers of consumer liking is based on relative importance of explanatory variables in a linear model. The problem is complicated when the variables involve linear dependence, which is the common situation in sensory and consumer data. (2) The commonly used methodologies, e.g., conjoint analysis, preference mapping and Kano's model, have serious limitations for determination of relative importance of correlated attributes and identification of drivers of consumer liking. (3) The conventional statistics, e.g., correlation coefficient, standard regression coefficient and P values of tests for regression parameters, etc., are inadequate and invalid measures of relative importance of correlated attributes. (4) There are three state-of-the-art methods for determination of relative importance of correlated attributes. They are the Lindeman, Merenda and Gold's method, Breiman's Random Forest and Johnson's relative weight. This article also provides statistical background and almost exhaustive main references on the topic of relative importance of variables scattered in various academic journals in different fields. The information will help the sensometricians and researchers with more statistical knowledge to embrace the mainstream of the research on the topic and to pursue advanced methods for drivers of consumer liking. © 2012 Wiley Periodicals, Inc. Source

Sensory measurement underlies sensory science. Sensory analysis and decision-making heavily depend on the quality of sensory data, which is determined by the performance of trained sensory panels and panelists. Various methods have been proposed for monitoring and assessing the performance. A weakness of the currently used methods is lack of a unified framework for various criteria and a variety of experiments with different types of data. This paper proposes to use accuracy, validity and reliability as general terminologies to describe sensory measurement and to apply the intraclass correlation coefficient (ICC) as a framework for monitoring and assessing performance. ICC can measure both similarity among panelists and sensitivity of panels and panelists. Hence, ICC can handle the problems of both reliability and validity. ICC can be obtained from different types of data for diverse experiments. This paper provides the equations and R and S-Plus functions for estimations of ICCs from continuous data (ratings), multivariate continuous data, ordinal data, ranking data, binary-choice data, multiple-choice data and forced-choice data. Confidence intervals, variances of the estimators, comparison with a fixed value and difference and similarity tests for multiple ICCs are also provided. The relationship between Cronbach's coefficient alpha and ICC is discussed. © 2012 Wiley Periodicals, Inc. Source

Bi J.,Sensometrics Research and Service
Journal of Sensory Studies

This paper discusses similarity tests using forced-choice methods in terms of Thurstonian discriminal distance, d', a measure of pure sensory difference or similarity. Critical values are derived for the similarity tests using the 2-alternative forced choice (AFC), the duo-trio, the 3-AFC and the triangle methods in terms of d'. Power analysis is conducted for the similarity tests using the four forced-choice methods. Three approaches are used to estimate sample sizes for the tests. The approaches are based, respectively, on exact binomial distribution, normal approximation with a continuity correction and Monte Carlo simulation. S-Plus and R codes were developed and provided for calculation and estimation of the critical values, testing powers and sample sizes for the tests. © 2011 Wiley Periodicals, Inc. Source

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