Enhanced reliability in subjective meat color data: A comparative study of Bayesian ordinal vs. frequentist metric...

Ordinal scales are commonly used in subjective meat color evaluations. Although these data appear numeric, they represent ordered categories and analysis with metric models may lead to type I and II errors. This study used a set of trained panel beef subjective color data to demonstrate the application of ordinal models under the Bayesian framework and to compare them with the metric frequentist methodology. Rather than using null hypothesis significance testing and P values from ANOVA, the Bayesian framework allows direct evaluations of pairwise hypothesis through posterior probabilities. Results indicate that averaging observations across panelists resulted in information loss regardless of the statistical approach, thus suggesting evaluators should be modeled as a random effect. The metric ANOVA showed a retail day by location interaction for color uniformity (P = 0.008) and post hoc tests detected no location differences until day 3. Pairwise hypothesis testing using the Bayesian ordinal models was subsequently conducted to directly evaluate the location effect on day 3, and the results supported a location difference consistent with the metric post hoc comparison. Despite similar findings between the methodologies for color uniformity, ordinal models remain more appropriate because they respect the ordered categorical structure of ordinal data. The Bayesian framework provided probabilistic inferences from the posterior distribution, offering results that are more intuitive and informative for interpretation and decision-making.