Data analyses of a multiple‐samples sensory ranking test and its duplicated test: A review

• Published in: Journal of sensory studies Vol. 33; no. 4
• Main Authors: Carabante, Kennet M., Prinyawiwatkul, Witoon
• Format: Journal Article
• Language: English
• Published: Cincinnati Wiley Subscription Services, Inc 01.08.2018
• Subjects: Children; Data processing; Geriatrics; Older people; Product development; Sensory evaluation; Statistical analysis
• ISSN: 0887-8250; 1745-459X
• DOI: 10.1111/joss.12435

A multiple‐samples ranking test is a quick, simple, and useful tool to assess differences in preference or sensory attribute intensity among multiple products. In this test, each panelist evaluates and ranks a complete set of samples once, generating one vector of multiple dependent data. To fit this ordinal dependency, the nonparametric Friedman's test is required. Duplicated ranking tests require a statistical analysis method that accounts for an additional level of dependency between duplicates. The study of duplicated ranking testing has not received much attention until recently. This review discusses historical development of methods and statistical analysis of sensory ranking tests, leading to current practices, alternative procedures including duplicated ranking testing, some factors that induce errors, and statistical considerations for the duplicated multiple‐samples ranking test. Practical applications A multiple‐samples sensory ranking test has been used to compare multiple samples on the basis of ranks of preference or attribute intensity. Most of the applications of sensory ranking tests include early product sorting or screening in a product development process, studies in which children and elderly are target subjects, and descriptive panel screening. In some cases, to reduce the number of subjects, time and cost, duplicated ranking tests are performed and data are analyzed using the nonparametric Friedman's test, not taking into consideration additional dependency between duplicates. Duplicated ranking testing can be beneficial if data analysis is properly handled, which is discussed in this review article.