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从非牛顿流变学预测液体食品产品的厚度感知。

Predicting thickness perception of liquid food products from their non-Newtonian rheology.

机构信息

Unilever Innovation Centre Wageningen, Wageningen, The Netherlands.

Van der Waals-Zeeman Institute, IoP, University of Amsterdam, Amsterdam, Netherlands.

出版信息

Nat Commun. 2021 Nov 3;12(1):6328. doi: 10.1038/s41467-021-26687-w.

DOI:10.1038/s41467-021-26687-w
PMID:34732723
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8566491/
Abstract

The "mouthfeel" of food products is a key factor in our perception of food quality and in our appreciation of food products. Extensive research has been performed on what determines mouthfeel, and how it can be linked to laboratory measurements and eventually predicted. This was mainly done on the basis of simple models that do not accurately take the rheology of the food products into account. Here, we show that the subjectively perceived "thickness" of liquid foods, or the force needed to make the sample flow or deform in the mouth, can be directly related to their non-Newtonian rheology. Measuring the shear-thinning rheology and modeling the squeeze flow between the tongue and the palate in the oral cavity allows to predict how a panel perceives soup "thickness". This is done for various liquid bouillons with viscosities ranging from that of water to low-viscous soups and for high-viscous xanthan gum solutions. Our findings show that our tongues, just like our eyes and ears, are logarithmic measuring instruments in agreement with the Weber-Fechner law that predicts a logarithmic relation between stimulus amplitude and perceived strength. Our results pave the way for more accurate prediction of mouthfeel characteristics of liquid food products.

摘要

食品的“口感”是我们感知食品质量和欣赏食品的关键因素。人们对决定口感的因素以及如何将口感与实验室测量联系起来并最终进行预测进行了广泛的研究。这主要是基于简单的模型,这些模型不能准确地考虑到食品的流变学性质。在这里,我们表明,人们主观感知到的液体食品的“稠度”,或者在口中使样品流动或变形所需的力,可以直接与其非牛顿流变学相关。测量剪切稀化流变学并对口腔中舌头和上颚之间的挤压流动进行建模,可以预测小组对汤“稠度”的感知。我们对各种粘度从水到低粘度汤再到高粘度黄原胶溶液的液体肉汤进行了这项研究。我们的研究结果表明,我们的舌头就像我们的眼睛和耳朵一样,是对数测量仪器,与韦伯-费希纳定律一致,该定律预测刺激幅度与感知强度之间存在对数关系。我们的研究结果为更准确地预测液体食品口感特征铺平了道路。

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