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In recent sensory literature, Bi and Kuesten (2015a. Food Quality and Preference, 43, 47-52; 2015b. Journal of Sensory Studies, 30, 461-471) have proposed the 'M + N' classification method with larger M and N (>3) for discrimination testing based on Fisher's exact test. Industry studies using the method are shared in this article to illustrate application of the method. The tests and measures are taken for non-fatiguing visual and manual inspections conducted across a variety of product categories. Further, within the examples provided, a validation between labs is documented and results of the method are compared to results from other methods (2-Alternative Choice and 2-Alternative Forced Choice) based on common conventional sensory measurement Thurstonian d′. Estimations are given of Intraclass Correlation Coefficient and Cronbach's coefficient alpha for evaluation of panel performance in the test using the 'M + N' classification method with larger M and N (only for M = N > 3). Practical applications Sorting methods are popular and often used for rapid investigation of product quality, product differences, or consumer perception and are used to help guide product maintenance and/or development efforts. The 'M + N' classification method with larger M and N, a type of sorting technique, can be applied for sensory discrimination and perceptual tests with generation of various indices, including Thurstonian discriminal distance delta (or d′) thereby offering the ability to compare results with other sensory discrimination methods. [ABSTRACT FROM AUTHOR]

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. Practical Applications Intraclass correlation coefficient ( ICC) may play a framework role in monitoring and assessing performance of trained sensory panels and panelists. It can be used as an index of the quality of sensory data. The larger the ICC or Cronbach's coefficient alpha value, the better the performance of panels and panelists. If and only if an ICC is significantly larger than a specified value, can the quality of the data for that attribute be regarded as acceptable. A statistical test that fails to show that an ICC or Cronbach's coefficient alpha is significantly larger than a specified lowest limit, e.g., 0.1, suggests that the products are undiscriminating, or the sensory data, at least for that attribute, might not be valid and reliable. We should use that data with caution. [ABSTRACT FROM AUTHOR]

• Performance reliability of sensory descriptive panels is critical to sensory analysis. • Intraclass correlation coefficient (ICC) and Cronbach's coefficient alpha can be used to measure performance reliability (PR). • Thurstonian d-prime and R-index can be used to measure Effect Size (ES). • Performance Reliability versus Effect Size (PR-ES) can be used as a framework for analysis of sensory descriptive panel data. • An historical protein powder case study is shared using the PR-ES framework. Performance reliability of sensory descriptive panels is critical to sensory analysis. Treatment effect size (ES) of products is a main objective of sensory analysis. The intraclass correlation coefficient (ICC) and Cronbach's coefficient alpha can be used to measure performance reliability, while Thurstonian d-prime and R-index can be used to measure ES. In this paper, an industry case study with a full historical database spanning 10 years of protein studies is shared. This case study uses the analysis framework of Performance Reliability versus Effect Size (PR-ES). R codes for the analysis are developed, used, and provided in the paper. [ABSTRACT FROM AUTHOR]