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Applying sorting algorithms to sensory ranking tests - A proof of concept study.
Markus Ekman1,2, Asa Amanda Olsson1,2,3, Kent Andersson1,2
1Food Science Summer Scholars Program, Cornell University, Ithaca, NY, 14853, USA.
Current Research in Food Science
|September 11, 2020
Summary
Sensory panelist ranking tasks can be improved by using a Merge Sort procedure, similar to computer science algorithms. Most panelists naturally use a less effective Bubble Sort method without specific training.
Area of Science:
- Sensory Science
- Cognitive Psychology
- Computer Science Algorithms
Background:
- Sensory panelist ranking is a common task, often assumed to be simple.
- The cognitive processes underlying sensory ranking are not well understood.
- Traditional ranking methods can become complex with numerous samples.
Purpose of the Study:
- To compare the efficacy of Bubble Sort and Merge Sort algorithms in sensory ranking tasks.
- To understand the natural cognitive strategies panelists employ during ranking.
- To identify methods for improving the accuracy and efficiency of sensory ranking.
Main Methods:
- A sensory ranking task involving six ascending concentrations of sucrose was conducted.
- Panelists (n=73) were assigned to either a Bubble Sort or Merge Sort procedure.
- Cognitive strategies were investigated through interviews and an additional ranking test (n=78).
Main Results:
- The Merge Sort procedure yielded superior results compared to the Bubble Sort procedure.
- Panelists predominantly utilized a Bubble Sort-like strategy when uninstructed.
- The perceived difficulty of Merge Sort suggests a need for enhanced panelist training.
Conclusions:
- Applying computer science sorting algorithms, specifically Merge Sort, can enhance sensory ranking accuracy.
- Panelist training in structured ranking procedures like Merge Sort is recommended.
- Understanding and guiding panelists' cognitive strategies can optimize sensory data collection.
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