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Area of Science:

  • Food sensory science
  • Information theory
  • Complexity science

Background:

  • Complexity is studied across disciplines like ecology and sensory science.
  • Quantifying system complexity is a key challenge.
  • Existing methods for complexity measurement are diverse.

Purpose of the Study:

  • To review approaches for quantifying complexity in food sensory perception.
  • To highlight the benefits of an information-theoretical framework for sensory science.
  • To inspire interdisciplinary exploration of complex phenomena using information theory.

Main Methods:

  • Review of existing complexity quantification methods.
  • Discussion of information-theoretical measures, including Shannon entropy.
  • Analysis of applicability to food sensory perception.

Main Results:

  • Information theory provides a potential unified measure for complexity.
  • Shannon entropy quantifies uncertainty and can serve as a complexity measure.
  • An information-theoretical footing can improve comparability and reproducibility in sensory science.

Conclusions:

  • Adopting an information-theoretical approach can standardize complexity measurement in food sensory perception.
  • This framework offers potential for cross-disciplinary insights between sensory science and physics.
  • Further research can leverage information theory to understand complex sensory systems.