TY - JOUR
T1 - Quantification of sensory and food quality
T2 - The R-index analysis
AU - Lee, Hye Seong
AU - Van Hout, Danielle
PY - 2009/8
Y1 - 2009/8
N2 - The accurate quantification of sensory difference/similarity between foods, as well as consumer acceptance/preference and concepts, is greatly needed to optimize and maintain food quality. The R-Index is one class ofmeasures of the degree of difference/similarity, and was originally developed for sensory difference tests for food quality control, product development, and so on. The index is based on signal detection theory and is free of the response bias that can invalidate difference testing protocols, including categorization and same-different and A-Not A tests. It is also a nonparametric analysis, making no assumptions about sensory distributions, and is simple to compute and understand. The R-Index is also flexible in its application. Methods based on R-Index analysis have been used as detection and sensory difference tests, as simple alternatives to hedonic scaling, and for themeasurement of consumer concepts. This review indicates the various computational strategies for the R-Index and its practical applications to consumer and sensorymeasurements in food science.
AB - The accurate quantification of sensory difference/similarity between foods, as well as consumer acceptance/preference and concepts, is greatly needed to optimize and maintain food quality. The R-Index is one class ofmeasures of the degree of difference/similarity, and was originally developed for sensory difference tests for food quality control, product development, and so on. The index is based on signal detection theory and is free of the response bias that can invalidate difference testing protocols, including categorization and same-different and A-Not A tests. It is also a nonparametric analysis, making no assumptions about sensory distributions, and is simple to compute and understand. The R-Index is also flexible in its application. Methods based on R-Index analysis have been used as detection and sensory difference tests, as simple alternatives to hedonic scaling, and for themeasurement of consumer concepts. This review indicates the various computational strategies for the R-Index and its practical applications to consumer and sensorymeasurements in food science.
KW - Concept measurement
KW - Consumer preference
KW - Detection threshold
KW - R-Index
KW - Sensory discrimination
UR - http://www.scopus.com/inward/record.url?scp=70350138464&partnerID=8YFLogxK
U2 - 10.1111/j.1750-3841.2009.01204.x
DO - 10.1111/j.1750-3841.2009.01204.x
M3 - Review article
C2 - 19723222
AN - SCOPUS:70350138464
SN - 0022-1147
VL - 74
SP - R57-R64
JO - Journal of Food Science
JF - Journal of Food Science
IS - 6
ER -