A Transfer of Technology from Engineering: Use of ROC Curves from Signal Detection Theory to Investigate Information Processing in the Brain during Sensory Difference Testing

Authors

  • Sukanya Wichchukit,

    1. Author Wichchukit is with Dept. of Food Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart Univ., Kamphaeng Saen Campus, 1 Malaiman, Kamphaeng Saen, Nakorn-pathom 73140, Thailand. Author O’Mahony is with Dept. of Food Science and Technology, Univ. of California, Davis 1 Shields Avenue, Davis, CA 95616. Direct inquiries to author Wichchukit (E-mail: fengskw@ku.ac.th).
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  • Michael O’Mahony

    1. Author Wichchukit is with Dept. of Food Engineering, Faculty of Engineering at Kamphaeng Saen, Kasetsart Univ., Kamphaeng Saen Campus, 1 Malaiman, Kamphaeng Saen, Nakorn-pathom 73140, Thailand. Author O’Mahony is with Dept. of Food Science and Technology, Univ. of California, Davis 1 Shields Avenue, Davis, CA 95616. Direct inquiries to author Wichchukit (E-mail: fengskw@ku.ac.th).
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Abstract

Abstract:  This article reviews a beneficial effect of technology transfer from Electrical Engineering to Food Sensory Science. Specifically, it reviews the recent adoption in Food Sensory Science of the receiver operating characteristic (ROC) curve, a tool that is incorporated in the theory of signal detection. Its use allows the information processing that takes place in the brain during sensory difference testing to be studied and understood. The review deals with how Signal Detection Theory, also called Thurstonian modeling, led to the adoption of a more sophisticated way of analyzing the data from sensory difference tests, by introducing the signal-to-noise ratio, d′, as a fundamental measure of perceived small sensory differences. Generally, the method of computation of d′ is a simple matter for some of the better known difference tests like the triangle, duo–trio and 2-AFC. However, there are occasions when these tests are not appropriate and other tests like the same–different and the A Not–A test are more suitable. Yet, for these, it is necessary to understand how the brain processes information during the test before d′ can be computed. It is for this task that the ROC curve has a particular use.

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