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使用混合物设计和基于分数的配方,更好地理解植物蛋白基解决方案的认知。

Using a mixture design and fraction-based formulation to better understand perceptions of plant-protein-based solutions.

机构信息

Univ Paris Saclay, UMR SayFood, AgroParisTech, INRAE, F-78850 Thiverval Grignon, France; Roquette Frères, 10 rue haute loge, F-62136 Lestrem, France.

Univ Paris Saclay, UMR SayFood, AgroParisTech, INRAE, F-91300 Massy, France.

出版信息

Food Res Int. 2021 Mar;141:110151. doi: 10.1016/j.foodres.2021.110151. Epub 2021 Jan 18.

Abstract

The food industry is focused on developing plant-based foods that incorporate pea protein isolates. However, these ingredients are often described as having persistent beany, bitter, and astringent notes, which can decrease the desirability of the resulting foods. These perceptions are rooted in the complex composition of volatile and non-volatile compounds in foods. The aim of our study was to better understand how the volatile and non-volatile fractions of pea protein isolates influence the perception of pea-protein-based foods. To this end, a mixture design was used. First, we obtained three fractions (the pellet, permeate, and retentate) from two pea protein isolates, resulting in a total of six fractions. Second, we used various combinations of the six fractions to create a set of 46 pea-protein-based solutions via various processes (solubilization, centrifugation, filtration, and mixing). Each fraction was specifically representative of the following constituent groups: insoluble proteins (the pellet); soluble compounds, such as volatiles, peptides, and phenolics (the permeate); and soluble proteins interacting with volatiles (the retentate). Factor levels were chosen with two aims: to explore the widest possible range of combinations and to realistically represent protein concentrations so as to build optimal mixture models. Third, 17 trained panelists were asked to score the attributes of the solutions using sensory profiling. Model performance was assessed using analysis of variance; results were significant for 18/18 attributes, and there was no significant lack-of-fit for 17/18 attributes. It was also assessed using the results of trials conducted with six supplementary solutions. These results clarified the origin of the perceived beany, bitter, and astringent notes. Beaniness was mainly influenced by the retentate and permeate fractions and was strongly affected by hexanal levels. Bitterness was mainly influenced by the retentate fraction, whereas astringency was influenced by the retentate and pellet fractions. Additionally, perception of these latter two attributes was affected by caffeic acid levels. This study has increased understanding of the relationship between pea protein fractions and the undesirable sensory attributes of pea protein isolates. It has also revealed how fraction-based formulation could be used to reduce the beaniness, bitterness, and astringency of pea-protein-based foods.

摘要

食品行业专注于开发含有豌豆蛋白分离物的植物性食品。然而,这些成分通常被描述为具有持久的豆腥味、苦味和涩味,这会降低食品的吸引力。这些看法源于食品中挥发性和非挥发性化合物的复杂组成。我们研究的目的是更好地了解豌豆蛋白分离物的挥发性和非挥发性部分如何影响豌豆蛋白基食品的感知。为此,我们使用了混合物设计。首先,我们从两种豌豆蛋白分离物中获得了三个部分(颗粒、渗透物和保留物),总共得到了六个部分。其次,我们使用六种部分的各种组合通过各种过程(溶解、离心、过滤和混合)来创建一组 46 种豌豆蛋白基溶液。每个部分都特别代表以下组成部分:不溶性蛋白质(颗粒);可溶性化合物,如挥发性物质、肽和酚类(渗透物);以及与挥发性物质相互作用的可溶性蛋白质(保留物)。因子水平的选择有两个目的:探索尽可能广泛的组合范围,并真实地代表蛋白质浓度,以建立最佳的混合物模型。第三,17 名经过培训的品尝员被要求使用感官分析对溶液的属性进行评分。使用方差分析评估模型性能;结果对 18/18 个属性显著,17/18 个属性不存在显著的不拟合。还使用了对六个补充溶液进行的试验结果进行了评估。这些结果澄清了感知到的豆腥味、苦味和涩味的来源。豆腥味主要受保留物和渗透物部分的影响,并且强烈受己醛水平的影响。苦味主要受保留物部分的影响,而涩味受保留物和颗粒部分的影响。此外,后两个属性的感知受咖啡酸水平的影响。本研究增加了对豌豆蛋白分离物中豌豆蛋白分数与豌豆蛋白分离物不良感官属性之间关系的理解。它还揭示了基于分数的配方如何用于减少豌豆蛋白基食品的豆腥味、苦味和涩味。

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