Discrimination testing

Discrimination testing is a technique employed in sensory analysis to determine whether there is a detectable difference among two or more products. The test uses a group of assessors (panellists) with a degree of training appropriate to the complexity of the test (see also: trained panel) to discriminate from one product to another through one of a variety of experimental designs. Though useful, these tests typically do not quantify or describe any differences, requiring a more specifically trained panel under different study design to describe differences and assess significance of the difference. Discrimination testing is a technique employed in sensory analysis to determine whether there is a detectable difference among two or more products. The test uses a group of assessors (panellists) with a degree of training appropriate to the complexity of the test (see also: trained panel) to discriminate from one product to another through one of a variety of experimental designs. Though useful, these tests typically do not quantify or describe any differences, requiring a more specifically trained panel under different study design to describe differences and assess significance of the difference. The statistical principle behind any discrimination test should be to reject a null hypothesis (H0) that states there is no detectable difference between two (or more) products. If there is sufficient evidence to reject H0 in favour of the alternative hypothesis, HA:There is a detectable difference, then a difference can be recorded. However, failure to reject H0 should not be assumed to be sufficient evidence to accept it. H0 is formulated on the premise that all of the assessors guessed when they made their response. The statistical test chosen should give a probability value that the result was arrived at through pure guesswork. If this probability is sufficiently low (usually below 0.05 or 5%) then H0 can be rejected in favour of HA. Tests used to decide whether or not to reject H0 include binomial, χ2 (Chi-squared), t-test etc.