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Cited by 567

The DOI matches its Crossref record; 7 of the 7 references it cites were found in a registry or an index.

THE PERCEPTION OF FOOD TEXTURE ‐ THE PHILOSOPHY OF THE BREAKDOWN PATH

HUTCHINGS, J. B., LILLFORD, P. J.·1988·Journal of Texture Studies

Abstract

Many authors have drawn attention to the limitations of present texture testing methods. To a great extent this may be due to the assumption that the perception of the structural properties of foods is comprised of a series of single attributes. This philosophy may account for the failure of the single instrumental measurement as a reliable texture descriptor. The present paper emphasises that texture perception is a dynamic sensory monitor of changes made to a food by processes occurring in the mouth. A general three dimensional model applicable to foods is postulated with “Degree of Structure”, “Degree of Lubrication” and “Time” as its axes. As each food is changed in the mouth, it describes its own “Breakdown Path” throughout the three dimensions. This approach is seen as the start of a general hypothesis for the physics and psychophysics of mastication.

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Crossref · 10.1111/j.1745-4603.1988.tb00928.x

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Metadata

Authors
HUTCHINGS, J. B., & LILLFORD, P. J.
Venue
Journal of Texture Studies
Publisher
Wiley
Published
1988-08
Volume
19
Issue
2
Pages
103-115
ISSN
  • 0022-4901 (ISSN-L)
  • 1745-4603
OpenAlex
W2094306691
Cited by
567
References
7
FWCI
3.63
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: THE PERCEPTION OF FOOD TEXTURE ‐ THE PHILOSOPHY OF THE BREAKDOWN PATH. https://app.cochat.ai/papers/10.1111/j.1745-4603.1988.tb00928.x
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HUTCHINGS, J. B., & LILLFORD, P. J. (1988). THE PERCEPTION OF FOOD TEXTURE ‐ THE PHILOSOPHY OF THE BREAKDOWN PATH. Journal of Texture Studies. https://doi.org/10.1111/j.1745-4603.1988.tb00928.x

Data sources

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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.